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

Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.

Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.

Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.

Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132

Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10

AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3

Amazon Athena achieves the fastest query performance when data is stored in columnar formats such as Apache Parquet and when queries can take advantage of partition pruning and predicate pushdown.

Converting CSV files to Parquet significantly reduces the amount of data scanned because Parquet stores data in a column-oriented layout. Since Athena queries only a subset of attributes, it reads only the required columns instead of scanning entire rows, which dramatically improves performance. Predicate pushdown further reduces query time by filtering data at the storage layer.

Partitioning the data by date ensures that Athena scans only the relevant partitions for same-day queries, minimizing unnecessary data reads. Storing one Parquet file per day is efficient and avoids the overhead of managing excessive small files.

ORC is also a columnar format, but Parquet is more commonly optimized and recommended for Athena workloads in AWS exam guidance. JSON and Avro are row-based or semi-row-based formats and result in larger scan sizes and slower query execution.

Therefore, Option D provides the shortest query times and aligns with Athena performance best practices.

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.