ARA-R01 Exam Dumps : SnowPro Advanced: Architect Recertification Exam

 Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake’s near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.

Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.

Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.

Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.

References:

• Semi-structured Data
• Snowflake for Data Lake

According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:

• Clustering is optimal when either:
• Clustering is most effective when the clustering key is used in the following types of query predicates:
• Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
• For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key. Adding more than 3-4 columns tends to increase costs more than benefits.

Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:

• These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
• These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
• These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
• These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.

References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

• Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5.csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded.
• Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv.
• Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed.
• Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5.csv.

Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM @%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration