Data-Engineer-Associate VCE Exam Download

To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.

Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.

Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.

References:

Amazon Redshift COPY Command Documentation

Redshift data sharing is a feature that enables you to share live data across different Redshift clusters without the need to copy or move data. Data sharing provides secure and governed access to data, while preserving the performance and concurrency benefits of Redshift. By setting up the sales team BI cluster as a consumer of the ETL cluster, the company can share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution also minimizes the usage of the computing resources of the ETL cluster, as the data sharing does not consume any storage space or compute resources from the producer cluster. The other options are either not feasible or not efficient. Creating materialized views or database views would require the sales team to have direct access to the ETL cluster, which could interfere with the critical analysis tasks. Unloading a copy of the data from the ETL cluster to an Amazon S3 bucket every week would introduce additional latency and cost, as well as create data inconsistency issues. References:

Sharing data across Amazon Redshift clusters

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. References:

Working with concurrency scaling - Amazon Redshift

Amazon Redshift Concurrency Scaling - Amazon Web Services

Configuring concurrency scaling queues - Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)

AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department’s Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.

The other options are not optimal for the following reasons:

A. Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department’s Region.

C. Modify the IAM roles of the HR departments to add a data filter for each department’s Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.

E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.

References:

1: AWS Lake Formation

2: AWS Lake Formation Permissions

: AWS Identity and Access Management

: Amazon S3