Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Option B best meets a highly resilient DR requirement with an RTO under 1 hour because a Multi-AZ deployment is designed to provide rapid recovery through redundancy across Availability Zones, rather than requiring a restore-and-rebuild process after a failure. A snapshot-based strategy (Option C) can be valuable for backup and regional recovery, but restoring a new cluster from snapshots is a heavier operation and is not the most reliable path to consistently achieving sub-hour RTO during real incidents. The study material highlights snapshot/restore as an operational mechanism for creating a new environment from a Redshift cluster’s saved state, which inherently implies a restore process and associated time to bring resources online.

Option D (cluster relocation) is primarily an operational move mechanism and is not the same as maintaining continuously resilient capacity for unplanned AZ-impacting events. Option A is not the best choice because Multi-AZ resiliency is aligned with modern Redshift architectures, and RA3 is the recommended node family for contemporary Redshift deployments where performance and managed storage characteristics are key for enterprise analytics. The study material reinforces Amazon Redshift as the platform for high-performance enterprise analytics, so a resilience-first configuration is appropriate.

Option C is correct because the fastest design combines a columnar storage format with partitioning on the most common query predicate. AWS Athena guidance says that Parquet and ORC are optimized columnar formats and that columns frequently used as filters are good candidates for partitioning. AWS also states that when a query filters on a partition key, the engine reads only matching partitions, which significantly reduces data scanned. Since the company’s main analytical need is insights specific to sensor categories, partitioning by sensor category provides the strongest pruning. Sorting by date then helps organize time-based data within each category partition.

Options B and D use CSV, which is row-based and much slower for analytical scans than Parquet or ORC. Option A uses a good format, but partitioning by date is less optimal than partitioning by sensor category when category is the main filter in the company’s queries. The study guide also identifies Parquet as the preferred analytic storage format for efficient columnar queries.

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.

1: AWS Lake Formation

2: AWS Lake Formation Permissions

AWS Identity and Access Management

Amazon S3