DEA-C01 Data Operations and Support Practice Test 10 — 15 Questions

Question 1

Refer to the exhibit. A data engineer runs this CLI command to check an object's metadata. The engineer wants to verify if the object is eligible for lifecycle transition to S3 Glacier based on its age. What additional information is needed?

Accepted Answer

The current date. Option C is correct because the last modified date shows when the object was last modified, and lifecycle policies are based on object age. The engineer needs the current date to calculate the age. Option A is wrong because ContentLength is not relevant. Option B is wrong because ETag is for integrity. Option D is wrong because metadata is shown but not needed for age calculation.

Answer

The ETag value

Answer

The metadata archive flag

Answer

The ContentLength value

Question 2

A company uses Amazon Athena to query data in S3. Recently, queries have become slow. The data is stored as CSV files in a partitioned table. What is the most effective way to improve query performance?

Accepted Answer

Convert the data to Parquet format and optimize partitioning.. Option C is correct because converting to Parquet and partitioning improves compression, columnar scanning, and partition pruning. Option A is wrong because increasing file size alone may not help; large CSV files still require full scans. Option B is wrong because converting to JSON would likely worsen performance. Option D is wrong because more nodes only help with distributed processing, but Athena manages resources automatically.

Answer

Increase the number of nodes in the Athena query engine.

Answer

Convert the data to JSON format.

Answer

Increase the size of the CSV files to reduce the number of files.

Question 3

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket and writes to an Amazon Redshift table. The job fails with a permission error. Which IAM policy addition is MOST likely required for the Glue job's role?

Accepted Answer

Add redshift:DataAPI. Option B is correct because the Glue job needs to write to Redshift; the role requires redshift:DataAPI access to use the Redshift Data API, which is the recommended method for Glue to write to Redshift. Option A is for Redshift Spectrum, not needed here. Option C is for Redshift cluster management. Option D is for reading from Redshift.

Answer

Add redshift:ModifyCluster

Answer

Add redshift:DescribeStatement

Answer

Add redshift:GetClusterCredentials

Question 4

A data engineer is troubleshooting a DMS task that is replicating data from an on-premises Oracle database to an RDS for MySQL instance. The task is failing with 'ORA-1555: snapshot too old' error. What is the best course of action?

Accepted Answer

Increase the UNDO tablespace size and set UNDO_RETENTION to a higher value.. Option A is correct because the snapshot too old error occurs when UNDO segments are too small or retention is too short for the long-running transaction needed by CDC. Increasing undo tablespace size and retention time allows CDC to read consistent old data. Option B is wrong because increasing redo logs does not prevent snapshot too old errors. Option C is wrong because it would lose CDC capability. Option D is wrong because batch optimized mode may not work with CDC and does not address the root cause.

Answer

Disable full supplemental logging on the source tables.

Answer

Increase the size of the redo logs on the source database.

Answer

Enable batch optimized apply on the DMS task.

Question 5

A company runs an Amazon RDS for PostgreSQL database and wants to capture change data (inserts, updates, deletes) to stream into Amazon Kinesis Data Streams for real-time processing. Which AWS service should be used to capture the changes directly from the database?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS DMS with ongoing replication (change data capture) is the correct service because it can continuously capture insert, update, and delete operations from the PostgreSQL transaction logs (WAL) and stream them to a Kinesis Data Streams endpoint. This allows real-time processing without modifying the source database or requiring application-level triggers.

Answer

Amazon RDS automated snapshots

Answer

AWS Glue ETL job scheduled to run every minute

Answer

Amazon Kinesis Agent

Question 6

A data engineer is designing a data pipeline that ingests streaming data from an IoT fleet using Kinesis Data Streams and processes it with a Lambda function. The Lambda function often times out when the data volume spikes. What is the most scalable solution?

Accepted Answer

Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.. Option D is correct because increasing shard count increases throughput, and using a fan-out pattern with Kinesis Data Analytics involves windowed processing that can handle spikes without Lambda timeouts. Option A is wrong because increasing Lambda timeout may not be enough for large spikes. Option B is wrong because Lambda reserved concurrency limits scaling. Option C is wrong because reducing batch size decreases throughput.

Answer

Reduce the batch size in the event source mapping.

Answer

Increase the Lambda function timeout to 15 minutes.

Answer

Increase the Lambda function memory and set reserved concurrency.

Question 7

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an S3 bucket. The application is consistently running out of memory and failing. The operator has already increased the Parallelism and TaskManager memory. What is the next BEST step to troubleshoot?

Accepted Answer

Enable Apache Flink metrics in Amazon CloudWatch to monitor heap and checkpoint details. Option D is correct because enabling Apache Flink metrics on CloudWatch allows monitoring of heap usage, checkpoint sizes, and backpressure, which can diagnose the memory issue. Option A adds latency but doesn't diagnose memory. Option B changes processing semantics but doesn't address memory. Option C is unrelated.

Answer

Change the processing mode from exactly-once to at-least-once

Answer

Reduce the number of shards in the source stream

Answer

Increase the buffer timeout for the S3 sink

Question 8

A company stores sensitive customer data in an S3 bucket. The data engineer needs to ensure that all data is encrypted at rest. Which S3 feature should be enabled?

Accepted Answer

Default encryption. Option C is correct because default encryption ensures all new objects are encrypted with SSE-S3, SSE-KMS, or SSE-C. Option A is wrong because S3 Block Public Access is a security feature for access control, not encryption. Option B is wrong because bucket policies can enforce encryption, but default encryption is simpler. Option D is wrong because versioning does not encrypt data.

Answer

S3 Versioning

Answer

S3 Block Public Access

Answer

Bucket policy requiring aws:SecureTransport

Question 9

A data engineer needs to export data from an Amazon DynamoDB table to Amazon S3 for archival purposes. The export should be a one-time operation and must not impact the read capacity of the table. Which approach meets these requirements?

Accepted Answer

Use DynamoDB on-demand backup feature to export to S3. Option B is correct because DynamoDB's on-demand backup exports to S3 without consuming read capacity. Option A impacts read capacity. Option C is for Spark jobs. Option D is for real-time streaming.

Answer

Use a Scan operation in a script to read all items and write to S3

Answer

Use AWS Glue ETL with a DynamoDB connector

Answer

Set up a DynamoDB Stream to Lambda that writes to S3

Question 10

A data engineer is running a Glue ETL job that reads from a JDBC source and writes to S3 in Parquet format. The job is slow and the engineer notices that the number of DPUs used is low. What can be done to improve performance?

Accepted Answer

Increase the number of workers (MaxCapacity) in the job configuration.. Option B is correct because increasing the number of workers increases parallelism. Option A is wrong because Parquet is already efficient; converting to CSV would worsen performance. Option C is wrong because pushing down filters reduces data read, but if the bottleneck is parallelism, more workers help. Option D is wrong because disabling bookmarks may cause reprocessing but does not directly improve speed.

Answer

Disable job bookmarks to avoid reading metadata.

Answer

Use push-down predicates to filter data at the source.

Answer

Change the output format to CSV to reduce CPU overhead.

Question 11

A data engineer is setting up a Redshift cluster and needs to ensure high availability. Which TWO actions should be taken?

Accepted Answer

Enable automatic replication across Availability Zones.. Options B and D are correct. Multi-node clusters with automatic replication provide high availability. Option A is wrong because single-node clusters are not HA. Option C is wrong because concurrency scaling improves performance, not HA. Option E is wrong because cross-region snapshot is disaster recovery, not HA.

Answer

Enable concurrency scaling.

Answer

Configure cross-region snapshot copy.

Answer

Use a single-node cluster to reduce complexity.

Question 12

A company is using Amazon Athena to query data in an S3 bucket. Queries are failing with the error 'HIVE_PATH_ALREADY_EXISTS'. The data is partitioned by year, month, day. What is the MOST likely cause?

Accepted Answer

A partition was manually added to the Glue Data Catalog that already exists. Option C is correct because the error occurs when a partition is already registered in the Glue Data Catalog and a new ALTER TABLE ADD PARTITION tries to add it again. Option A would cause schema mismatch. Option B would cause permission error. Option D would cause file not found.

Answer

The data format in the partition is inconsistent with the table schema

Answer

The S3 location for the partition is empty

Answer

The IAM role used by Athena lacks s3:ListBucket permission on the bucket

Question 13

A company is using Amazon EMR to run Spark jobs. The jobs are failing due to memory issues. Which THREE configurations can help mitigate out-of-memory errors?

Accepted Answer

Tune Spark memory configurations like spark.executor.memory and spark.memory.fraction.. Options A, B, and D are correct. Increasing instance memory, tuning memory fractions, and enabling dynamic allocation help manage memory. Option C is wrong because more cores per node increases parallelism, potentially worsening memory pressure. Option E is wrong because instance store volumes are for temporary storage, not memory.

Answer

Configure instance store volumes for intermediate shuffle data.

Answer

Use instances with more vCPUs to process more tasks in parallel.

Question 14

A data engineer is designing a data pipeline that ingests millions of small JSON files (1-10 KB each) from an S3 bucket into Amazon Redshift. The current approach uses a Lambda function triggered by S3 events to call the Redshift COPY command for each file. This is causing high latency and throttling. Which alternative is MOST cost-effective and efficient?

Accepted Answer

Use Amazon Kinesis Data Firehose to buffer and write larger files to S3, then use a scheduled COPY command. Option D is correct because Kinesis Firehose can buffer the small files and write larger batches to S3, then use a scheduled COPY or Redshift Spectrum. Option A still processes each file individually. Option B is for streaming, not batch. Option C adds complexity and cost.

Answer

Use Amazon Kinesis Data Streams and a consumer to batch files before COPY

Answer

Increase the Lambda concurrency limit and memory

Answer

Use AWS Glue to merge files into larger Parquet files before loading

Question 15

A data engineer needs to monitor the performance of an RDS for PostgreSQL database. Which THREE CloudWatch metrics are most useful for this purpose?

Accepted Answer

CPUUtilization. CPUUtilization is a critical metric for monitoring RDS for PostgreSQL because high CPU usage can indicate inefficient queries, insufficient instance size, or contention. Sustained high CPU can lead to performance degradation and increased query latency, making it essential for capacity planning and troubleshooting.

Answer

FreeStorageSpace

Answer

NetworkThroughput