Free and Premium Snowflake COF-C02 Dumps Questions Answers

In Snowflake, tag inheritance is a feature that allows tags, which are key-value pairs assigned to objects for the purpose of data governance and metadata management, to be inherited within a hierarchy. The hierarchy in which tag inheritance is possible is from Schema to Table to Column. This means that a tag applied to a schema can be inherited by the tables within that schema, and a tag applied to a table can be inherited by the columns within that table.References: Snowflake Documentation on Tagging and Object Hierarchy

A directory table in Snowflake is an implicit object layered on a stage, whether internal or external. It allows users to query the contents of a stage as if it were a table, providing metadata about the files stored in the stage, such as filenames, file sizes, and last modified timestamps.

References:

Snowflake Documentation: Directory Tables

The ORGADMIN role in Snowflake is designed to manage organization-level activities. This role can perform several tasks that span across multiple accounts within the organization.

View Usage Information: The ORGADMIN role can view usage statistics and billing information for all accounts within the organization.

-- Example: Viewing usage information

SELECT *

FROM organization_usage_history;

Enable Database Replication: The ORGADMIN role has the authority to enable and manage database replication for accounts within the organization.

-- Example: Enabling database replication

ALTER DATABASE my_database ENABLE REPLICATION TO ACCOUNT other_account;

References:

Snowflake Documentation: Organization Administration

Snowflake Documentation: Replication

 To use Partner Connect, the ACCOUNTADMIN role is required. Partner Connect allows account administrators to easily create trial accounts with selected Snowflake business partners and integrate these accounts with Snowflake

The bulk data load history available after a COPY statement is stored in the metadata of the pipe and is retained for 64 days1.

The LOGIN_HISTORY view in the ACCOUNT_USAGE schema provides information about login attempts, including both successful and failed logins. This view can be used to review the failed login attempts of a specific user for the past 30 days. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 For a temporary table, the maximum total Continuous Data Protection (CDP) charges incurred are for the duration of the session in which the table was created, which does not exceed 24 hours2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation2

Snowflake supports the use of external stages with Amazon Web Services (AWS), Microsoft Azure Cloud, and Google Cloud Platform (GCP). These platforms allow users to stage data externally and integrate with Snowflake for data loading operations

The UNDROP command in Snowflake can be used to directly restore Views and Tables. These objects, when dropped, are moved to a ‘Recycle Bin’ where they can be restored within a time limit before they are permanently deleted. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The SQL command Select * from table(validate(t1, job_id => '_last')); is used to return errors from the last executed COPY command into table t1 in the current session. It checks the results of the most recent data load operation and provides details on any errors that occurred during that process1.

The query results cache can be used as long as the data in the table has not changed since the last time the query was run. If the underlying data has changed, Snowflake will not use the cached results and will re-execute the query1.

The parse_json() function in Snowflake interprets an input string as a JSON document and produces a VARIANT value containing the JSON document. This function is specifically designed for parsing strings that contain valid JSON information3.

The LATERAL FLATTEN command in Snowflake produces a result set that includes several columns, among which PATH and INDEX are includedPATH indicates the path to the element within a data structure that needs to be flattened, and INDEX represents the index of the element if it is an array2.

 Temporary and transient tables in Snowflake do not have a Fail-safe period. Once the session ends or the tables are dropped, the data is purged and not recoverable1.

Snowflake’s architecture is designed to operate entirely on cloud infrastructure. It uses the core compute and storage services of each cloud provider, which allows it to leverage the scalability and reliability of cloud resources. Snowflake’s services are deployed across multiple availability zones within a cloud provider’s region to ensure high availability and fault tolerance. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 When the retention period for an object ends in Snowflake, Time Travel on the historical data is dropped ©. This means that the ability to access historical data via Time Travel is no longer available once the retention period has expired2.

Snowpipe via REST API can reference both named internal stages within Snowflake and external stages, such as Amazon S3, Google Cloud Storage, or Microsoft Azure1. This means that Snowpipe is not limited to only external stages as a source for data loading.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

The responsibilities of Snowflake’s Cloud Service layer include authentication (A), which ensures secure access to the platform; resource management (B), which involves allocating and managing compute resources; and query parsing and optimization (D), which improves the efficiency and performance of SQL query execution3.

 The GET command is used to download files from a Snowflake stage to a local folder on a client’s machine2.

To go back to the first version of a transient table created three days prior, one can use Time Travel if the DATA_RETENTION_TIME_IN_DAYS was set to at least 3 days. This allows the user to access historical data within the specified retention period. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The Snowflake Cloud Services layer manages various services, including authentication and metadata management. This layer ties together all the different components of Snowflake to process user requests, manage sessions, and control access3.

The graphical representation of warehouse utilization indicates periods of significant queuing, suggesting that the current single cluster cannot efficiently handle all incoming queries. Configuring the warehouse to a multi-cluster warehouse will distribute the load among multiple clusters, reducing queuing times and improving overall performance1.

References = Snowflake Documentation on Multi-cluster Warehouses1

In Snowflake, to extract a specific element from a JSON stored in a VARIANT column, the correct syntax is to use the dot notation. Therefore, the query select src.dealership from car_sales; will return the dealership information contained within each JSON object in the src column.

References: For a detailed explanation, please refer to the Snowflake documentation on querying semi-structured data.

Shares in Snowflake are named objects that encapsulate all the information required to share databases, schemas, tables, secure views, and secure UDFs. These objects can be added to a share by granting privileges on them to the share via a database role

 The COPY command is used in Snowflake to load data from files located in an external stage into a table. This command allows for efficient and parallelized data loading from various file formats1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

 Snowflake supports unloading data in JSON and delimited file formats such as CSV and TSV. These formats are commonly used for data interchange and are supported by Snowflake for unloading operations

 In Snowflake’s architecture, the Cloud Services layer is responsible for generating the query execution plan. This layer handles all the coordination, optimization, and management tasks, including query parsing, optimization, and compilation into an execution plan that can be processed by the Compute layer.

 In the Snowflake Data Marketplace, the types of data listings available include ‘Vendor’, which refers to the providers of data, and ‘Personalized’, which indicates customized data offerings tailored to specific consumer needs45.

The Snowflake cloud services layer is responsible for tasks such as authentication and access control, ensuring secure access to the platform, and metadata management, which involves organizing and maintaining information about the data stored in Snowflake56.

 In Snowflake, a schema is a logical grouping of database objects, which can include streams and external tables. A stream is an object that allows users to query data that has changed in specified tables or views, and an external table is a table that references data stored outside of Snowflake. Roles, warehouses, users, and shares are not contained within a schema. References: SHOW OBJECTS, Database, Schema, & Share DDL

Worksheets in Snowsight can be shared directly with other Snowflake users within the same account. This feature allows for collaboration and sharing of SQL queries or Python code, as well as other data manipulation tasks1.

 In Snowflake, both materialized views and transient tables will incur storage charges because they store data. They will also incur compute charges when queries are run against them, as compute resources are used to process the queries. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The billable compute time includes the time the warehouse is running queries plus the auto-suspend time after the last query if the warehouse is not manually suspended. In this scenario, the warehouse runs for 3 minutes, suspends after 10 minutes of inactivity, resumes for a 10-second query, and then is manually suspended. The total billable time is the sum of the initial 3 minutes, the 10 minutes of auto-suspend time, and the brief period for the 10-second query, which is rounded up to the next full minute due to Snowflake’s billing practices. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Network policies in Snowflake can be set at the user level and at the account level2.

The Snowflake Search Optimization Service is designed to support improved performance for selective point lookup queries. These are queries that retrieve specific records from a database, often based on a unique identifier or a small set of criteria3.

The Cloud Services layer in Snowflake is responsible for managing the result cache. When a query is executed, the results are stored in this cache, and subsequent identical queries can leverage these cached results without re-executing the entire query1.

 By default, the Snowflake role required to create a share is ACCOUNTADMIN (D). This role has the necessary privileges to perform administrative tasks, including creating shares for data sharing purposes

 To ensure that additional multi-clusters are resumed with no delay, a virtual warehouse should be configured to a size larger than generally required. This configuration allows for immediate availability of additional resources when needed, without waiting for new clusters to start up

 The credit consumption for maintaining a materialized view is impacted by how often the base table changes © and whether the materialized view has a cluster key defined (D). Changes to the base table can trigger a refresh of the materialized view, consuming credits. Additionally, having a cluster key defined can optimize the performance and credit usage during the materialized view’s maintenance. References: SnowPro Core Certification materialized view credit consumption

When dealing with semi-structured data in Snowflake, and the specific types of operations to be performed on the data are not yet determined, Snowflake recommends using the VARIANT data type. The VARIANT type is highly flexible and capable of storing data in multiple formats, including JSON, AVRO, BSON, and more, within a single column. This flexibility allows users to perform various operations on the data, including querying and manipulation of nested data structures without predefined schemas.

References:

Snowflake Documentation: Semi-structured Data Types

When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.

References:

Snowflake Documentation: Data Unloading Considerations

In Snowflake, when setting up a network policy that specifies both allowed and blocked IP address lists, if an IP address appears in both lists, access from that IP address will be denied. The reason is that Snowflake prioritizes security, and the presence of an IP address in the blocked list indicates it should not be allowed regardless of its presence in the allowed list. This ensures that access controls remain stringent and that any potentially unsafe IP addresses are not inadvertently permitted access.

References:

Snowflake Documentation: Network Policies

The Virtual Private Snowflake (VPS) edition offers the highest level of security for organizations with the strictest security requirements. This edition provides a dedicated and isolated instance of Snowflake, including enhanced security features and compliance certifications to meet the needs of highly regulated industries or any organization requiring the utmost in data protection and privacy.

References:

Snowflake Documentation: Snowflake Editions

Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:

Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.

Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.

Options B, D, and E do not directly relate to observing the effects of query pruning. "Partitions total" shows the total available, not the impact of pruning, while "Bytes read from result" and "Bytes written" relate to output rather than the efficiency of data scanning.References: Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.

Snowflake recommends enhancing the granularity and management of privileges by creating and utilizing custom roles. When additional privileges are needed beyond those provided by the system-defined roles for a virtual warehouse or any other resource, these privileges should be granted to a custom role. This approach allows for more precise control over access rights and the ability to tailor permissions to the specific needs of different user groups or applications within the organization, while also maintaining the integrity and security model of system-defined roles.

References:

Snowflake Documentation: Roles and Privileges

Snowflake bills virtual warehouse usage in one-minute increments, rounding up to the nearest minute for any partial minute of compute time used. If a virtual warehouse runs for 61 seconds and then, after being shut down, restarts and runs for an additional 30 seconds, the total time billed would be 120 seconds or 2 minutes. The first 61 seconds are rounded up to 2 minutes, and the subsequent 30 seconds are within a new minute, which is also rounded up to the nearest minute.

References:

Snowflake Documentation: Virtual Warehouses Billing

The default value for auto-suspending a Virtual Warehouse in the Snowflake Web Interface (UI) is 10 minutes. This setting helps manage compute costs by automatically suspending warehouses that are not in use, ensuring that compute resources are efficiently allocated and not wasted on idle warehouses.

References:

Snowflake Documentation: Virtual Warehouses

A Snowflake user would decide to use a materialized view instead of a regular view primarily when the base tables do not change frequently. Materialized views store the result of the view query and update it as the underlying data changes, making them ideal for situations where the data is relatively static and query performance is critical. By precomputing and storing the query results, materialized views can significantly reduce query execution times for complex aggregations, joins, and calculations.

References:

Snowflake Documentation: Materialized Views

The SYSTEM$GET_PRIVATELINK function in Snowflake provides proxy information necessary for configuring PrivateLink connections, which can protect Snowsight as well as other Snowflake services. PrivateLink enhances security by allowing Snowflake to be accessed via a private connection within a cloud provider’s network, reducing exposure to the public internet.

References:

Snowflake Documentation: PrivateLink Setup

To utilize the search optimization service in Snowflake, the correct syntax for granting privileges to a role involves specific commands that include adding search optimization capabilities:

Option C: GRANT ADD SEARCH OPTIMIZATION ON SCHEMA TO ROLE . This command grants the specified role the ability to implement search optimization at the schema level, which is essential for enhancing search capabilities within that schema.

Options A and B do not include the correct verb "ADD," which is necessary for this specific type of grant command in Snowflake. Option D incorrectly mentions the database level, as search optimization privileges are typically configured at the schema level, not the database level.References: Snowflake documentation on the use of GRANT statements for configuring search optimization.

A Pre-signed URL provides access to files stored in Snowflake without requiring authorization at the time of access. This feature allows users to generate a URL with a limited validity period that grants temporary access to a file in a secure manner. It's particularly useful for sharing data with external parties or applications without the need for them to authenticate directly with Snowflake.

References:

Snowflake Documentation: Using Pre-signed URLs

When creating a CSV file format in Snowflake and the requirement is to wrap columns by single quotes or double quotes, the FIELD_OPTIONALLY_ENCLOSED_BY parameter should be used in the file format specification. This parameter allows you to define a character (either a single quote or a double quote) that can optionally enclose each field in the CSV file, providing flexibility in handling fields that contain special characters or delimiters as part of their data.

References:

Snowflake Documentation: CSV File Format

Characteristics of Snowsight worksheets in Snowflake include:

A. Worksheets can be grouped under folders, and a folder of folders: This organizational feature allows users to efficiently manage and categorize their worksheets within Snowsight, Snowflake's web-based UI, enhancing the user experience by keeping related worksheets together.

E. Users can import worksheets and share them with other users: Snowsight supports the sharing of worksheets among users, fostering collaboration by allowing users to share queries, analyses, and findings. This feature is crucial for collaborative data exploration and analysis workflows.

References:

Snowflake Documentation: Snowsight (UI for Snowflake)

Snowflake's architecture is unique in that it combines elements of both traditional shared-disk and shared-nothing database architectures. This hybrid approach allows Snowflake to offer the scalability and performance benefits of a shared-nothing architecture (with compute and storage separated) while maintaining the simplicity and flexibility of a shared-disk architecture in managing data across all nodes in the system. This results in an architecture that provides on-demand scalability, both vertically and horizontally, without sacrificing performance or data cohesion.

References:

Snowflake Documentation: Snowflake Architecture

Characteristics of reader accounts in Snowflake include:

A. Reader account users cannot add new data to the account: Reader accounts are intended for data consumption only. Users of these accounts can query and analyze the data shared with them but cannot upload or add new data to the account.

E. Reader accounts enable data consumers to access and query data shared by the provider: One of the primary purposes of reader accounts is to allow data consumers to access and perform queries on the data shared by another Snowflake account, facilitating secure and controlled data sharing.

References:

Snowflake Documentation: Reader Accounts

UTF-8 is the only supported character set for loading and unloading data from all supported file formats in Snowflake. UTF-8 is a widely used encoding that supports a large range of characters from various languages, making it suitable for internationalization and ensuring data compatibility across different systems and platforms.

References:

Snowflake Documentation: Data Loading and Unloading

The Fail-safe retention period for transient and temporary tables in Snowflake is 0 days. Fail-safe is a feature designed to protect data against accidental loss or deletion by retaining historical data for a period after its Time Travel retention period expires. However, transient and temporary tables, which are designed for temporary or short-term storage and operations, do not have a Fail-safe period. Once the data is deleted or the table is dropped, it cannot be recovered.

References:

Snowflake Documentation: Understanding Fail-safe

By default, the ACCOUNTADMIN role in Snowflake has the ability to create a share from a shared database. This role has the highest level of access within a Snowflake account, including the management of all aspects of the account, such as users, roles, warehouses, and databases, as well as the creation and management of shares for secure data sharing with other Snowflake accounts.

References:

Snowflake Documentation: Roles

A cached MFA token is valid for up to four hours.

Scalar functions in Snowflake (and SQL in general) are designed to return a single value for each invocation. They operate on a single value and return a single result, making them suitable for a wide range of data transformations and calculations within queries.

References:

Snowflake Documentation: Functions

By default, the ACCOUNTADMIN role in Snowflake has access to the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function. This function is used to set global account parameters, impacting the entire Snowflake account's configuration and behavior. The ACCOUNTADMIN role is the highest-level administrative role in Snowflake, granting the necessary privileges to manage account settings and security features, including the use of global account parameters.

References:

Snowflake Documentation: SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER

To verify the privileges that have been granted to a specific role in Snowflake, the correct SQL command is SHOW GRANTS TO ROLE <Role Name>. This command lists all the privileges granted to the specified role, including access to schemas, tables, and other database objects. This is a useful command for administrators and users with sufficient privileges to audit and manage role permissions within the Snowflake environment.

References:

Snowflake Documentation: SHOW GRANTS

To extract the URL of a directory table on an external stage for further transformation in Snowflake, the GET_ABSOLUTE_PATH function can be used. This function returns the full path of a file or directory within a specified stage, enabling users to dynamically construct URLs for accessing or processing data stored in external stages.

References:

Snowflake Documentation: Working with Stages

A materialized view in Snowflake is a database object derived from a query specification, stored for later use, and can significantly speed up expensive aggregations on large data sets. Materialized views store the result of their underlying query, reducing the need to recompute the result each time the view is accessed. This makes them ideal for improving the performance of read-heavy, aggregate-intensive queries.

References:

Snowflake Documentation: Using Materialized Views

In the Query Profile of Snowflake, the TableScan operator represents the access to a single table. This operator indicates that the query execution involved reading data from a table stored in Snowflake. TableScan is a fundamental operation in query execution plans, showing how the database engine retrieves data directly from tables as part of processing a query.

References:

Snowflake Documentation: Understanding the Query Profile

Temporary tables in Snowflake do not consume storage costs. They are designed for transient data that is needed only for the duration of a session. Data stored in temporary tables is held in the virtual warehouse's cache and does not persist beyond the session's lifetime, thereby not incurring any storage charges.

References:

Snowflake Documentation: Temporary Tables

Query pruning in Snowflake is the mechanism used to limit the number of micro-partitions scanned by a query. By analyzing the filters and conditions applied in a query, Snowflake can skip over micro-partitions that do not contain relevant data, thereby reducing the amount of data processed and improving query performance. This technique is particularly effective for large datasets and is a key component of Snowflake's performance optimization features.

References:

Snowflake Documentation: Query Performance Optimization

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

Snowflake Documentation: Data Sharing

When data is loaded into Snowflake, it undergoes a reorganization process where the data is stored in a columnar format and compressed. The columnar storage format enables efficient querying and data retrieval, as it allows for reading only the necessary columns for a query, thereby reducing IO operations. Additionally, Snowflake uses advanced compression techniques to minimize storage costs and improve performance. This combination of columnar storage and compression is key to Snowflake's data warehousing capabilities.

References:

Snowflake Documentation: Data Storage and Organization

In Snowflake, stored procedures and User-Defined Functions (UDFs) have different invocation patterns within SQL:

Option B is correct: A single executable statement can call only one stored procedure due to the procedural and potentially transactional nature of stored procedures. In contrast, a single SQL statement can call multiple UDFs because UDFs are designed to operate more like functions in traditional programming, where they return a value and can be embedded within SQL queries.References: Snowflake documentation comparing the operational differences between stored procedures and UDFs.

The Query Profile in Snowflake can identify common query problems, including:

B. Inefficient pruning: This refers to the inability of a query to effectively limit the amount of data being scanned, potentially leading to suboptimal performance.

D. Queries too large to fit in memory: This indicates that a query requires more memory than is available in the virtual warehouse, which can lead to spilling to disk and degraded performance.

The Query Profile helps diagnose these issues by providing detailed execution statistics and visualizations, aiding in query optimization and troubleshooting.

References:

Snowflake Documentation: Query Profile

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When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

References:

Snowflake Documentation: Querying Semi-structured Data

To get the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view should be used. This Information Schema view provides granular storage metrics for tables within Snowflake, including data related to the size of the table, the amount of data stored, and storage usage over time. It's an essential tool for administrators and users looking to monitor and optimize storage consumption and costs.

References:

Snowflake Documentation: Information Schema - TABLE STORAGE METRICS View

The search optimization service in Snowflake supports use cases involving conjunctions (AND) of multiple equality predicates. This service enhances the performance of queries that include multiple equality conditions by utilizing search indexes to quickly filter data without scanning entire tables or partitions. It's particularly beneficial for improving the response times of complex queries that rely on specific data matching across multiple conditions.

References:

Snowflake Documentation: Search Optimization Service

A directory table in Snowflake is used to store file-level metadata about the data files in a stage. It is conceptually similar to an external table and provides information such as file size, last modified timestamp, and file URL. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Multi-Factor Authentication (MFA) provides increased login security for users connecting to Snowflake. Snowflake’s MFA is powered by Duo Security service, which adds an additional layer of security during the login process.

 When the DROP MANAGED ACCOUNT command is executed in Snowflake, it removes the managed account, including all objects created within the account, and access to the account is immediately restricted2.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query. This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In a regular schema within Snowflake, the object owner has the privilege to grant object privileges. The object owner is typically the role that created the object or to whom the ownership of the object has been transferred78.

References = [COF-C02] SnowPro Core Certification Exam Study Guide

When using the ALLOW_CLIENT_MFA_CACHING parameter, a cached Multi-Factor Authentication (MFA) token is valid for up to 4 hours. This allows for continuous, secure connectivity without users needing to respond to an MFA prompt at the start of each connection attempt to Snowflake within this timeframe2.

The BUILD_STAGE_FILE_URL function generates a Snowflake-hosted file URL to a staged file using the stage name and relative file path as inputs2.

Snowflake external tables are read-only, which means data manipulation language (DML) operations like INSERT, RENAME, or ALTER cannot be performed on them. However, external tables can be used for query and join operations3.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The FLATTEN function can be used with the recursive argument to return a list of distinct key names in all nested elements within an object. This function is particularly useful for working with semi-structured data in Snowflake

Dynamic Data Masking in Snowflake supports tables and views. These objects can have masking policies applied to their columns to dynamically mask data at query time3.

The Notify & Suspend action for a resource monitor in Snowflake sends a notification to all account administrators who have notifications enabled and suspends all assigned warehouses. However, the suspension only occurs after all currently running statements in the warehouses have been completed1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 A network policy in Snowflake is used to restrict access to the Snowflake account from unauthorized or unknown sources. It allows administrators to specify allowed IP address ranges, thus preventing access from any IP addresses not listed in the policy1.

The LATERAL modifier for the FLATTEN function allows joining information outside the object (such as other columns in the source table) with the flattened data, creating a lateral view that correlates with the preceding tables in the FROM clause2345. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The statistics that can be used to identify queries with inefficient pruning are ‘Partitions scanned’ and ‘Partitions total’. These statistics indicate how much of the data was actually needed and scanned versus the total available, which can highlight inefficiencies in data pruning34.

 A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Statistic output in the Query Profile Overview of Snowflake provides detailed insights into the performance of different parts of the query. Specifically, it highlights the "Most expensive nodes," which are the operations or steps within the query execution that consume the most resources, such as CPU and memory. Identifying these nodes helps in pinpointing performance bottlenecks and optimizing query execution by focusing efforts on the most resource-intensive parts of the query.

References:

Snowflake Documentation on Query Profile Overview: It details the components of the profile overview, emphasizing how to interpret the statistics section to improve query performance by understanding which nodes are most resource-intensive.

QUSTION NO: 582

How do secure views compare to non-secure views in Snowflake?

A. Secure views execute slowly compared to non-secure views.

B. Non-secure views are preferred over secure views when sharing data.

C. Secure views are similar to materialized views in that they are the most performant.

D. There are no performance differences between secure and non-secure views.

Answer: D

Secure views and non-secure views in Snowflake are differentiated primarily by their handling of data access and security rather than performance characteristics. A secure view enforces row-level security and ensures that the view definition is hidden from the users. However, in terms of performance, secure views do not inherently execute slower or faster than non-secure views. The performance of both types of views depends more on other factors such as underlying table design, query complexity, and system workload rather than the security features embedded in the views themselves.

References:

Snowflake Documentation on Views: This section provides an overview of both secure and non-secure views, clarifying that the main difference lies in security features rather than performance, thus supporting the assertion that there are no inherent performance differences.

QUSTION NO: 583

When using SnowSQL, which configuration options are required when unloading data from a SQL query run on a local machine? {Select TWO).

A. echo

B. quiet

C. output_file

D. output_format

E. force_put_overwrite

Answer: C, D

When unloading data from SnowSQL (Snowflake's command-line client), to a file on a local machine, you need to specify certain configuration options to determine how and where the data should be outputted. The correct configuration options required are:

C. output_file: This configuration option specifies the file path where the output from the query should be stored. It is essential for directing the results of your SQL query into a local file, rather than just displaying it on the screen.

D. output_format: This option determines the format of the output file (e.g., CSV, JSON, etc.). It is crucial for ensuring that the data is unloaded in a structured format that meets the requirements of downstream processes or systems.

These options are specified in the SnowSQL configuration file or directly in the SnowSQL command line. The configuration file allows users to set defaults and customize their usage of SnowSQL, including output preferences for unloading data.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

Snowflake Documentation: Configuring SnowSQL at Snowflake Documentation

QUSTION NO: 584

How can a Snowflake user post-process the result of SHOW FILE FORMATS?

A. Use the RESULT_SCAN function.

B. Create a CURSOR for the command.

C. Put it in the FROM clause in brackets.

D. Assign the command to RESULTSET.

Answer: A

first run SHOW FILE FORMATS

then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))

QUSTION NO: 585

Which file function gives a user or application access to download unstructured data from a Snowflake stage?

A. BUILD_SCOPED_FILE_URL

B. BUILD_STAGE_FILE_URL

C. GET_PRESIGNED_URL

D. GET STAGE LOCATION

Answer: C

The function that provides access to download unstructured data from a Snowflake stage is:

C. GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.

Example usage:

SELECT GET_PRESIGNED_URL('stage_name', 'file_path');

This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.

References:

Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation

QUSTION NO: 586

When should a multi-cluster virtual warehouse be used in Snowflake?

A. When queuing is delaying query execution on the warehouse

B. When there is significant disk spilling shown on the Query Profile

C. When dynamic vertical scaling is being used in the warehouse

D. When there are no concurrent queries running on the warehouse

Answer: A

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

A. When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

References:

Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

QUSTION NO: 587

A JSON object is loaded into a column named data using a Snowflake variant datatype. The root node of the object is BIKE. The child attribute for this root node is BIKEID.

Which statement will allow the user to access BIKEID?

A. select data:BIKEID

B. select data.BIKE.BIKEID

C. select data:BIKE.BIKEID

D. select data:BIKE:BIKEID

Answer: C

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure.References: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

QUSTION NO: 588

Which Snowflake tool is recommended for data batch processing?

A. SnowCD

B. SnowSQL

C. Snowsight

D. The Snowflake API

Answer: B

For data batch processing in Snowflake, the recommended tool is:

B. SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

QUSTION NO: 589

How does the Snowflake search optimization service improve query performance?

A. It improves the performance of range searches.

B. It defines different clustering keys on the same source table.

C. It improves the performance of all queries running against a given table.

D. It improves the performance of equality searches.

Answer: D

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

D. It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions.

This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

References:

Snowflake Documentation: Search Optimization Service at Snowflake Documentation

QUSTION NO: 590

What compute resource is used when loading data using Snowpipe?

A. Snowpipe uses virtual warehouses provided by the user.

B. Snowpipe uses an Apache Kafka server for its compute resources.

C. Snowpipe uses compute resources provided by Snowflake.

D. Snowpipe uses cloud platform compute resources provided by the user.

Answer: C

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

References:

Snowflake Documentation: Understanding Snowpipe

QUSTION NO: 591

What is one of the characteristics of data shares?

A. Data shares support full DML operations.

B. Data shares work by copying data to consumer accounts.

C. Data shares utilize secure views for sharing view objects.

D. Data shares are cloud agnostic and can cross regions by default.

Answer: C

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

References:

Snowflake Documentation: Understanding Secure Views in Data Sharing

QUSTION NO: 592

Which DDL/DML operation is allowed on an inbound data share?

A. ALTER TA3LE

B. INSERT INTO

C. MERGE

D. SELECT

Answer: D

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

References:

Snowflake Documentation: Using Data Shares

QUSTION NO: 593

In Snowflake, the use of federated authentication enables which Single Sign-On (SSO) workflow activities? (Select TWO).

A. Authorizing users

B. Initiating user sessions

C. Logging into Snowflake

D. Logging out of Snowflake

E. Performing role authentication

Answer: B C

Federated authentication in Snowflake allows users to use their organizational credentials to log in to Snowflake, leveraging Single Sign-On (SSO). The key activities enabled by this setup include:

B. Initiating user sessions: Federated authentication streamlines the process of starting a user session in Snowflake by using the existing authentication mechanisms of an organization.

C. Logging into Snowflake: It simplifies the login process, allowing users to authenticate with their organization's identity provider instead of managing separate credentials for Snowflake.

References:

Snowflake Documentation: Configuring Federated Authentication

QUSTION NO: 594

A user wants to upload a file to an internal Snowflake stage using a put command.

Which tools and or connectors could be used to execute this command? (Select TWO).

A. SnowCD

B. SnowSQL

C. SQL API

D. Python connector

E. Snowsight worksheets

Answer: B, E

To upload a file to an internal Snowflake stage using a PUT command, you can use:

B. SnowSQL: SnowSQL, the command-line client for Snowflake, supports the PUT command, allowing users to upload files directly to Snowflake stages from their local file systems.

E. Snowsight worksheets: Snowsight, the web interface for Snowflake, provides a user-friendly environment for executing SQL commands, including the PUT command, through its interactive worksheets.

References:

Snowflake Documentation: Loading Data into Snowflake using SnowSQL

Snowflake Documentation: Using Snowsight

Temporary tables do not consume any storage costs in Snowflake because they only exist for the duration of the session that created them and are automatically dropped when the session ends, thus incurring no long-term storage charges4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowflake supports the storage of semi-structured data using the ARRAY and VARIANT data types. The ARRAY data type can directly contain VARIANT, and thus indirectly contain any other data type, including itself. The VARIANT data type can store a value of any other type, including OBJECT and ARRAY, and is often used to represent semi-structured data formats like JSON, Avro, ORC, Parquet, or XML34.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake command to unload the result of a query to a single file is COPY INTO with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage

In Snowflake, permanent tables and external tables can be shared with other accounts using Secure Data Sharing. Temporary tables, transient tables, and UDTFs are not shareable objects

The SECURITYADMIN role in Snowflake can manage any object grant globally, including modifying and revoking grants. This role has the necessary privileges to oversee and control access to all securable objects within the Snowflake environment4.

 A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Running a virtual warehouse is required when loading data into a table and when unloading data from a table because these operations require compute resources that are provided by the virtual warehouse23.

The parameter DATA_RETENTION_TIME_IN_DAYS can be set at the account level to define the minimum number of days Snowflake retains historical data for Time Travel1.

When federated authentication is activated in Snowflake, users authenticate via an external identity provider (IdP) rather than using Snowflake-managed credentials. Therefore, a user with a password defined by Snowflake will be unable to enter a password and must use their IdP credentials to log in.

QUSTION NO: 579

What value provides information about disk usage for operations where intermediate results do not fit in memory in a Query Profile?

A. IO

B. Network

C. Pruning

D. Spilling

Answer: D

In Snowflake, when a query execution requires more memory than what is available, Snowflake handles these situations by spilling the intermediate results to disk. This process is known as "spilling." The Query Profile in Snowflake includes a metric that helps users identify when and how much data spilling occurs during the execution of a query. This information is crucial for optimizing queries as excessive spilling can significantly slow down query performance. The value that provides this information about disk usage due to intermediate results not fitting in memory is appropriately labeled as "Spilling" in the Query Profile.

References:

Snowflake Documentation on Query Profile and Performance: This section explains the various components of the query profile, including the spilling metric, which indicates disk usage for operations where intermediate results exceed available memory.

The purpose of a Query Profile is to provide a detailed analysis of a particular query’s execution plan, including the mechanics, behavior, and performance. It helps in identifying potential performance bottlenecks and areas for optimization

Snowflake’s Data Exchange feature provides a data hub for secure data collaboration. It allows providers to publish data that can be discovered and accessed by a selected group of invited members, facilitating secure and controlled data sharing within a collaborative environment3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The STRIP NULL_VALUES file format option, when set to TRUE, removes object or array elements that contain null values during the loading process of semi-structured data files into Snowflake. This ensures that the data loaded into Snowflake tables does not contain these null elements, which can be useful when the “null” values in files indicate missing values and have no other special meaning2.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The minimum Snowflake Edition that supports secure storage of Protected Health Information (PHI) data is the Business Critical Edition. This edition offers enhanced security features necessary for compliance with regulations such as HIPAA and HITRUST CSF4.

 For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key. This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 To traverse semi-structured data in Snowflake, a user can insert a colon (:) between the VARIANT column name and any first-level element. This path syntax is used to retrieve elements in a VARIANT column4.

The DATA_SHARING_USAGE schema includes views that display information about listings published in the Snowflake Marketplace or a data exchange, which includes DATA_TRANSFER_HISTORY and LISTING_TELEMETRY_DAILY2.

The purpose of the Snowflake SPLIT_TO_TABLE function is to split a string based on a specified delimiter and flatten the results into rows. This table function is useful for transforming a delimited string into a set of rows that can be further processed or queried5.

 To view and modify resource monitors, the least privileges needed are MONITOR and MODIFY. These privileges allow a user to monitor credit usage and make changes to resource monitors3.

The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

In Snowflake, to create a materialized view, the user must have the necessary privileges on the schema where the view will be created. These privileges are granted through roles, not directly to individual users. Therefore, the correct process is to grant the role to the user and then grant the privilege to create the materialized view to the role itself.

The statement GRANT ROLE MYROLE TO USER USER1; grants the specified role to the user, allowing them to assume that role and exercise its privileges. The subsequent statement CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE; grants the privilege to create a materialized view within the specified schema to the role MYROLE. Any user who has been granted MYROLE can then create materialized views in MYDB.MYSCHEMA.

References:

Snowflake Documentation on Roles

Snowflake Documentation on Materialized Views

The list command in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running the list @file__stage command will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

In Snowflake, a Pipe is the object designed to enable the continuous, near-real-time loading of data from files as soon as they are available in a cloud storage location. Pipes use Snowflake’s COPY command to load data and can be associated with a Stage object to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

References:

Snowflake Documentation on Pipes

SnowPro® Core Certification Study Guide

Reader accounts in Snowflake are designed to allow users to read data shared with them but do not have the capability to extract data for use outside of Snowflake. They are intended for consuming shared data within the Snowflake environment only.

 Virtual Warehouses in Snowflake can indeed be resized while they are suspended. Resizing a warehouse involves changing the number of compute resources (servers) allocated to it, which can be done to adjust performance and cost. When a warehouse is suspended, it is not currently running any queries, but its definition and metadata remain intact, allowing for modifications like resizing.

 Snowflake supports several methods for authenticating users, including federated authentication, key-pair authentication, and OAuth. Federated authentication allows users to authenticate using their organization’s identity provider. Key-pair authentication uses a public-private key pair for secure login, and OAuth is an open standard for access delegation commonly used for token-based authentication. References: Authentication policies | Snowflake Documentation, Authenticating to the server | Snowflake Documentation, External API authentication and secrets | Snowflake Documentation.

 Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Replication and Egress Charges1

 Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:

B. Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.

C. Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.

While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.

References:

Snowflake Documentation on Time Travel

SnowPro® Core Certification Study Guide

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

For data loading using Snowpipe, the recommended file size is a compressed file greater than 10 MB and up to 100 MB. This size range is optimal for Snowpipe’s continuous, micro-batch loading process, allowing for efficient and timely data ingestion without overwhelming the system with files that are too large or too small.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe1

Snowflake supports two geospatial data types: GEOGRAPHY and GEOMETRY. The GEOGRAPHY data type is used to store geospatial data that models the Earth as a perfect sphere, which is suitable for global geospatial data. This data type follows the WGS 84 standard and is used for storing points, lines, and polygons on the Earth’s surface. The GEOMETRY data type, on the other hand, represents features in a planar (Euclidean, Cartesian) coordinate system and is typically used for local spatial reference systems. Since the question specifically asks about geospatial data, which commonly refers to Earth-related spatial data, the correct answer is GEOGRAPHY3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Secure sharing in Snowflake allows users to share specific objects with other Snowflake accounts without physically copying the data, thus not consuming additional storage. Tables and Secure User Defined Functions (UDFs) are among the objects that can be shared using this feature. Materialized views, sequences, and procedures are not shareable objects in Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Secure Data Sharing1

The primary responsibility of Snowflake’s virtual warehouses is to execute queries. Virtual warehouses are one of the key components of Snowflake’s architecture, providing the compute power required to perform data processing tasks such as running SQL queries, performing joins, aggregations, and other data manipulations.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouses1

A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.

The indicators that it may be appropriate to use a clustering key for a table include:

D. Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.

E. The clustering depth for the table is large: A large clustering depth indicates that the table’s data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables

Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION

Stack Overflow discussion on cluster key selection in Snowflake

Transient tables in Snowflake have a default Time Travel retention period of 1 day, which allows users to access historical data within the last 24 hours. However, transient tables do not have a Fail-safe period. Fail-safe is an additional layer of data protection that retains data beyond the Time Travel period for recovery purposes in case of extreme data loss. Since transient tables are designed for temporary or intermediate workloads with no requirement for long-term durability, they do not include a Fail-safe period by default1.

References:

Snowflake Documentation on Storage Costs for Time Travel and Fail-safe

Assigning the custom role to the SYSADMIN role is considered a best practice because it allows the SYSADMIN role to manage objects created by the custom role. This is important for maintaining proper access control and ensuring that the SYSADMIN can perform necessary administrative tasks on objects created by users with the custom role.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Section 1.3 - SnowPro Core Certification Study Guide1

The most efficient way to get files into Snowflake, especially when new files are being written to a cloud storage location at frequent intervals, is to use Snowpipe with auto-ingest. Snowpipe is Snowflake’s continuous data ingestion service that loads data as soon as it becomes available in a cloud storage location. By setting up cloud provider notifications, Snowpipe can be triggered automatically whenever new files are written to the storage location, ensuring that the data is loaded into Snowflake with minimal latency and without the need for manual intervention or scheduling frequent tasks.

References:

Snowflake Documentation on Snowpipe

SnowPro® Core Certification Study Guide

 Snowflake’s architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

B. The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.

D. Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system’s ability to handle a high number of concurrent queries.

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

References:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

When a CLUSTER BY clause is added to a Snowflake table, it specifies one or more columns to organize the data within the table’s micro-partitions. This clustering aims to colocate data with similar values in the same or adjacent micro-partitions. By doing so, it enhances the efficiency of query pruning, where the Snowflake query optimizer can skip over irrelevant micro-partitions that do not contain the data relevant to the query, thereby improving performance.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables1.

Community discussions on how source data’s ordering affects a table with a cluster key

The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment. This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.

When loading data into Snowflake, it is recommended to:

C. Load files that are approximately 100-250 MB (or larger): This size is optimal for parallel processing and can help to maximize throughput. Smaller files can lead to overhead that outweighs the actual data processing time.

D. Avoid using embedded characters such as commas for numeric data types: Embedded characters can cause issues during data loading as they may be interpreted incorrectly. It’s best to clean the data of such characters to ensure accurate and efficient data loading.

These best practices are designed to optimize the data loading process, ensuring that data is loaded quickly and accurately into Snowflake.

References:

Snowflake Documentation on Data Loading Considerations

[COF-C02] SnowPro Core Certification Exam Study Guide

The Information Schema and Account Usage Share in Snowflake provide metadata and historical usage data for various objects within a Snowflake account. Specifically, they offer storage information for Tables, Databases, and Internal Stages. These schemas contain views and table functions that allow users to query object metadata and usage metrics, such as the amount of data stored and historical activity.

Tables: The storage information includes data on the daily average amount of data in database tables.

Databases: For databases, the storage usage is calculated based on all the data contained within the database, including tables and stages.

Internal Stages: Internal stages are locations within Snowflake for temporarily storing data, and their storage usage is also tracked.

References: The information is verified according to the SnowPro Core Certification Study Guide and Snowflake documentation

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Loading1

Column-level security is a feature that allows fine-grained control over access to specific columns within a table. This is particularly useful for managing sensitive data and ensuring that only authorized users can view or manipulate certain pieces of information. According to my last update, this feature was available in the Enterprise Edition or higher editions of Snowflake.

References: Based on my internal data as of 2021, column-level security is an advanced feature typically reserved for higher-tiered editions like the Enterprise Edition in data warehousing solutions such as Snowflake.

When a Snowflake virtual warehouse is resized, the data cached in the warehouse is not lost. This is because the cache is maintained independently of the warehouse size. Resizing a warehouse, whether scaling up or down, does not affect the cached data, ensuring that query performance is not impacted by such changes.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouse Performance1

 The COPY INTO command is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.

References:

Snowflake Documentation on Unloading Data

Snowflake SnowPro Core: Copy Into Command to Unload Rows to Files in Named Stage

 Even if a developer is granted ownership of a table with a masking policy, they will not be able to modify the table to read the masked data if their role does not have the necessary permissions. Ownership of a table does not automatically confer the ability to alter masking policies, which are designed to protect sensitive data. Masking policies are applied at the schema level and require specific privileges to modify12.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Masking Policies

 Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake’s continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe and Virtual Warehouses1

Data Robot is recognized as a machine learning and data science partner within the Snowflake Partner Ecosystem. It provides an enterprise AI platform that enables users to build and deploy accurate predictive models quickly. As a partner, Data Robot integrates with Snowflake to enhance data science capabilities2.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Machine Learning & Data Science Partners

 External functions in Snowflake are a special type of User-Defined Function (UDF) that call code executed outside of Snowflake, typically through a remote service. Unlike traditional UDFs, external functions do not contain SQL code within Snowflake; instead, they interact with external services to process data2.

.

 When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.

On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake’s storage.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Stages