DEA-C01 Practice Test 5 — 10 Questions

Question 1

A data engineer is designing a data pipeline that ingests data from an on-premises database into Amazon S3 using AWS Database Migration Service (DMS). The data must be encrypted at rest in S3 using SSE-S3. The engineer also needs to track changes to the source database in real time. Which DMS configuration should the engineer use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use DMS with ongoing replication (change data capture) enabled.. DMS with ongoing replication (change data capture) enables real-time tracking of changes from the source database. Option A is incorrect because using a snapshot only captures data at a point in time, not real-time changes. Option C is incorrect because a full load task only loads existing data without capturing ongoing changes. Option D is incorrect because streaming to Amazon Kinesis is unnecessary; DMS CDC can directly replicate changes to S3. Encryption at rest in S3 with SSE-S3 is automatically supported by DMS when writing to S3.

Answer

Use DMS with a snapshot of the source database.

Answer

Use DMS with a full load task only.

Answer

Use DMS with a full load task and then stream to Amazon Kinesis.

Question 2

Refer to the exhibit. An AWS Glue ETL job is failing with an OutOfMemoryError. The job reads from Amazon S3 and performs a GROUP BY on a large dataset. Which change should the data engineer make to resolve this error?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. The OutOfMemoryError in an AWS Glue ETL job performing a GROUP BY on a large dataset indicates that the executors do not have enough memory to handle the shuffle operations required for aggregation. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory and compute resources available, allowing the job to process larger partitions without running out of memory.

Answer

Use coalesce to reduce the number of partitions.

Answer

Increase the number of partitions in the DataFrame.

Answer

Use repartition to increase the number of partitions.

Question 3

A data engineer needs to store semi-structured JSON logs from AWS CloudTrail. The logs are append-only and rarely accessed after 90 days. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is the most cost-effective storage solution for CloudTrail logs that are append-only and rarely accessed after 90 days. It offers the lowest storage cost among AWS options (approximately $0.00099 per GB/month) and is designed for data that is accessed at most once or twice per year, with retrieval times of 12–48 hours. Since the logs are rarely accessed after 90 days, the retrieval latency is acceptable, and the cost savings over S3 Standard (which costs ~$0.023 per GB/month) are substantial.

Answer

Amazon S3 Standard

Answer

Amazon EBS with cold HDD volumes

Answer

Amazon DynamoDB with on-demand capacity

Question 4

A company uses Amazon DynamoDB to store session data for a web application. The application experiences occasional spikes in traffic, causing throttling on the table. The data engineer needs to implement a solution that handles traffic spikes without manual intervention and minimizes cost. What should the data engineer do?

Accepted Answer

Enable DynamoDB Auto Scaling for the table.. DynamoDB Auto Scaling (option D) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, using CloudWatch alarms and the Application Auto Scaling service. This handles traffic spikes without manual intervention and minimizes cost by scaling down during low traffic periods, making it the ideal solution for variable workloads like session data.

Answer

Switch to provisioned capacity with a high fixed read/write capacity.

Answer

Implement DynamoDB Accelerator (DAX) to cache read requests.

Answer

Purchase DynamoDB reserved capacity.

Question 5

A data pipeline uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The delivery occasionally fails with 'Firehose is throttled'. What should be done to reduce throttling?

Accepted Answer

Increase the buffer size and buffer interval. Increasing the buffer size and buffer interval gives Kinesis Data Firehose more time and data volume to accumulate before delivering to S3, reducing the frequency of PutRecord.Batch calls to the underlying Kinesis stream. This directly mitigates throttling by lowering the request rate, as Firehose throttling typically occurs when the per-shard write throughput limit (1,000 records/second or 1 MB/second) is exceeded.

Answer

Enable compression on the Firehose delivery stream

Answer

Decrease the buffer size to flush more frequently

Answer

Increase the number of shards in the Kinesis stream

Question 6

Which TWO options are valid ways to reduce storage costs for an Amazon S3 data lake that stores historical data rarely accessed after 30 days? (Choose TWO.)

Accepted Answer

Create a lifecycle policy to transition objects to S3 Standard-IA after 30 days.. S3 Standard-IA (Infrequent Access) is designed for data accessed less frequently but requires rapid access when needed. Transitioning objects to Standard-IA after 30 days reduces storage costs compared to S3 Standard while maintaining low-latency retrieval, making it ideal for a data lake where historical data is rarely accessed after the first month.

Answer

Enable S3 Transfer Acceleration for all uploads.

Answer

Create a lifecycle policy to delete objects after 30 days.

Answer

Enable S3 Versioning to preserve all object versions.

Question 7

Arrange the steps to implement a data lake on Amazon S3 with AWS Lake Formation.

Accepted Answer

Create S3 bucket, then register it in Lake Formation, then set up administrators, then define schema in Glue Catalog, then grant access to users. Start by creating the S3 bucket. Then register it in Lake Formation, set up administrators, define the schema in Glue Catalog, and finally grant access to users.

Answer

Set up administrators, then create S3 bucket, then register it in Lake Formation, then define schema in Glue Catalog, then grant access to users

Answer

Create S3 bucket, then define schema in Glue Catalog, then register it in Lake Formation, then set up administrators, then grant access to users

Answer

Register S3 bucket in Lake Formation, then create S3 bucket, then set up administrators, then define schema in Glue Catalog, then grant access to users

Question 8

Refer to the exhibit. This log snippet is from a failed AWS Glue job. The job processes a large dataset in memory. What is the MOST likely cause of the OutOfMemoryError?

Accepted Answer

The Glue job is running with insufficient DPUs or worker type.. An OutOfMemoryError in AWS Glue typically occurs when the allocated DPUs or worker type are insufficient for the in-memory processing of a large dataset. Option B is incorrect because unsupported file formats cause parsing errors, not memory errors. Option C is incorrect because mismatched keys in a join cause data skew or incorrect results, but not directly an OutOfMemoryError. Option D is incorrect because too many partitions usually lead to small file overhead, not heap space exhaustion.

Answer

The input data is in an unsupported file format.

Answer

The job is attempting to join two tables with mismatched keys.

Answer

The job has too many partitions.

Question 9

A data engineering team is managing an Amazon Redshift cluster that is used for BI reporting. The cluster has a mix of large tables (some over 1 TB) and many smaller tables. The team notices that queries on a large fact table are slow. The fact table is distributed using KEY distribution on the customer_id column, which has high cardinality. The team wants to improve query performance. They have the option to change the distribution style and sort key. Which redesign should they implement?

Accepted Answer

Change the distribution style to EVEN and set the sort key to a date column used in WHERE clauses.. Using EVEN distribution ensures data is evenly distributed across all nodes, avoiding data skew that can occur with KEY distribution on a high-cardinality column like customer_id. Setting the sort key to a date column used in WHERE clauses enables range-restricted scans, significantly reducing the amount of data scanned for common BI queries that filter by date. This combination improves query performance by maximizing parallelism and minimizing I/O.

Answer

Keep the distribution style as AUTO and set the sort key to customer_id.

Answer

Change the distribution style to ALL and set the sort key to customer_id.

Answer

Change the distribution style to KEY on a different column with high cardinality.

Question 10

Which THREE of the following are benefits of using Amazon DynamoDB Accelerator (DAX)? (Choose three.)

Accepted Answer

Offloads read traffic from the DynamoDB table.. DAX acts as a read-through cache that offloads read traffic from the DynamoDB table, reducing the number of read requests that hit the underlying table and thus lowering the consumed read capacity units (RCUs). This allows the table to handle more concurrent reads without scaling up provisioned capacity.

Answer

Improves write throughput by batching writes.

Answer

Supports write-through caching to improve write performance.