DEA-C01 Data Operations and Support Practice Test 23 — 15 Questions

Question 1

A data engineer uses Amazon EMR to run a Spark job that reads from S3 and writes to HDFS on the cluster. The job fails with an 'OutOfMemoryError: Java heap space' error in the executors. Which parameter adjustment should be made to resolve this?

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

Accepted Answer

Increase spark.executor.memory. The OutOfMemoryError: Java heap space in Spark executors indicates that the executor JVM heap is insufficient to hold the data being processed. Increasing spark.executor.memory allocates more heap space to each executor, allowing it to handle larger partitions or aggregations without running out of memory. This directly addresses the root cause of the error.

Answer

Increase spark.default.parallelism

Answer

Increase spark.sql.shuffle.partitions

Answer

Increase spark.driver.memory

Question 2

A company runs a data lake on Amazon S3 with AWS Glue and Amazon Athena. The data engineer notices that queries are slow and scanning large amounts of data. Which THREE actions should the engineer take to optimize query performance and reduce costs?

Accepted Answer

Compress data files using gzip or snappy.. Option C is correct because compressing data files with gzip or snappy reduces the total bytes stored and scanned, and Athena charges and performs based on data scanned, so smaller files lower both query latency and cost. Option D is correct because partitioning the S3 data by frequently filtered columns such as date or region enables partition pruning, so Athena reads only the relevant prefixes instead of scanning the entire table. Option E is correct because columnar formats like Parquet or ORC let Athena read only the columns referenced in the query and provide better compression and predicate pushdown, dramatically reducing scanned data compared to row-based formats like CSV or JSON. Option A is not appropriate because increasing the Athena query timeout only allows long-running queries to finish; it does not reduce the amount of data scanned or improve performance. Option B is not appropriate because adding DPUs to the Glue job speeds up ETL processing, not Athena query performance or the volume of data scanned at query time.

Answer

Increase the query timeout in Athena.

Answer

Increase the number of DPUs in the Glue job.

Question 3

A company uses Amazon Redshift for data warehousing. They notice that query performance has degraded over time. Which maintenance operation should be performed to improve performance?

Accepted Answer

Run the VACUUM command. The VACUUM command in Amazon Redshift reclaims space from deleted rows and re-sorts rows to restore the sort order, which improves query performance by reducing I/O and enabling more efficient scans. Over time, as data is updated or deleted, the physical storage becomes fragmented and sort order degrades, leading to slower queries. Running VACUUM (or VACUUM SORT ONLY, VACUUM DELETE ONLY, etc.) is the recommended maintenance operation to address this degradation.

Answer

Drop and recreate the table

Answer

Run the REINDEX command

Answer

Run the ANALYZE command

Question 4

Refer to the exhibit. An AWS Glue job is failing with 'AccessDenied' when trying to write to the 'data-lake-bucket' which is encrypted with an AWS KMS key. The IAM role used by the Glue job has the attached policy shown. What is the MOST likely cause of the failure?

Accepted Answer

The policy does not include kms:GenerateDataKey or kms:Encrypt permission.. When an S3 bucket is encrypted with a customer-managed KMS key, any principal writing objects must have both S3 write permissions and KMS permissions to use the key. The Glue job's IAM policy lacks kms:GenerateDataKey and kms:Encrypt, so S3 rejects the PutObject with AccessDenied. Adding those KMS actions to the policy resolves the failure.

Answer

The policy does not include s3:ListBucket permission.

Answer

The policy does not include s3:GetObject permission.

Answer

The KMS key ARN in the policy is incorrect.

Question 5

Refer to the exhibit. A data engineer is troubleshooting an AWS Lambda function that processes data from Amazon S3. The function is triggered by S3 events, but no logs appear in CloudWatch Logs. The engineer runs the AWS CLI command shown. What is the MOST likely reason for the missing logs?

Accepted Answer

The Lambda execution role does not have permissions to create log groups and write logs.. The most likely reason for missing CloudWatch Logs is that the Lambda execution role lacks the necessary permissions to create log groups and write log streams. When a Lambda function is invoked, it attempts to create a log group named /aws/lambda/<function-name> and a log stream, then write logs. If the execution role does not include actions like logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents, the function cannot write any logs, resulting in no log entries. This is a common misconfiguration, especially when roles are custom-created without the default AWSLambdaBasicExecutionRole policy.

Answer

The Lambda function is configured to log to a different log group.

Answer

The Lambda function is not being invoked by S3 events.

Answer

The log retention policy is set to 7 days, causing logs to expire immediately.

Question 6

A data engineer is designing a data pipeline that ingests data from multiple sources into Amazon S3, then processes it with AWS Glue and loads it into Amazon Redshift. Which THREE practices should be implemented to ensure data quality?

Accepted Answer

Implement data validation checks at the ingestion stage. Option A is correct because validating data at ingestion (for example, checking types, nulls, ranges, and referential integrity before writing to Amazon S3) prevents corrupt or malformed records from propagating downstream into Glue ETL jobs and Redshift tables. Option B is correct because AWS Glue DataBrew provides visual data profiling, column statistics, and built-in transformations/rules that can enforce schema consistency and detect anomalies before loading into Redshift. Option E is correct because Amazon CloudWatch alarms on Glue job metrics, Redshift load events, and custom anomaly metrics enable proactive detection of pipeline failures and unexpected data patterns, which is essential for ongoing data quality assurance. Option C is not correct because compressing files reduces storage and I/O costs but does not validate or improve data accuracy, completeness, or consistency. Option D is not correct because manual sampling is ad hoc, non-scalable, and not a reliable or automated data quality control compared with systematic validation, profiling, and monitoring.

Answer

Compress data files to reduce storage costs

Answer

Use manual sampling to check data quality periodically

Question 7

A company is using AWS Glue to process data stored in Amazon S3. The Glue job runs successfully but takes longer than expected. Which TWO actions can reduce the job runtime?

Accepted Answer

Increase the number of DPUs allocated to the job. Option B is correct because increasing the number of DPUs (Data Processing Units) allocated to a Glue job adds more compute capacity and parallelism, allowing Spark executors to process partitions concurrently and thereby reducing overall runtime. Option E is correct because partitioning the input data in S3 (for example, by date or category) lets Glue's Spark engine prune irrelevant partitions and read only the data it needs, cutting I/O and shuffle overhead. Option A is wrong because disabling job bookmarks causes Glue to reprocess already-processed data, which typically increases runtime rather than reducing it. Option C is wrong because reducing the number of workers lowers parallelism and generally makes the job slower. Option D is wrong because Python shell jobs are single-node, non-distributed, and intended for lightweight scripts, so they cannot efficiently process large datasets that a distributed Spark job handles.

Answer

Disable job bookmarking

Answer

Reduce the number of workers

Answer

Change the job type from Spark to Python shell

Question 8

A data engineer needs to monitor the number of records processed by a Kinesis Data Firehose delivery stream and set an alarm if the count drops below a threshold. Which CloudWatch metric should be used?

Accepted Answer

IncomingRecords. IncomingRecords is the CloudWatch metric that specifically counts the number of records successfully ingested into a Kinesis Data Firehose delivery stream. It is emitted per delivery stream and directly reflects the volume of data being processed, making it the correct choice for monitoring record count and setting threshold alarms. Other metrics either measure API call success, delivery outcomes, or byte volume, not record count.

Answer

PutRecord.Success

Answer

DeliveryToS3.Success

Answer

IncomingBytes

Question 9

A company runs a Redshift cluster for analytics. The data engineering team notices that COPY commands from S3 are failing for large files (>1 GB) with the error 'S3ServiceException: SlowDown'. What is the most effective solution?

Accepted Answer

Split the large files into smaller parts (e.g., 100 MB each) and use parallel COPY.. The 'SlowDown' error from S3 occurs when too many requests are made to the same S3 prefix, causing throttling. Splitting large files into smaller parts (e.g., 100 MB) and using parallel COPY distributes the load across multiple slices in Redshift, reducing the request rate per prefix and avoiding throttling.

Answer

Use Redshift Spectrum to query the data directly in S3.

Answer

Enable automatic compression on the target tables.

Answer

Increase the number of Redshift nodes to distribute the load.

Question 10

A data engineer is troubleshooting a step function that orchestrates ETL jobs. The state machine fails with 'State Machine Execution Throttled' error. What should the engineer do to resolve this?

Accepted Answer

Request a service quota increase for concurrent executions.. The 'State Machine Execution Throttled' error in AWS Step Functions occurs when the account hits the service quota for concurrent state machine executions (default 1,000 per region, or lower for certain execution types). The correct remediation is to request a service quota increase via the AWS Service Quotas console for the 'Concurrent executions' quota of Step Functions. This directly addresses the throttling cause rather than masking it.

Answer

Reduce the number of steps in the state machine.

Answer

Set up a CloudWatch alarm to detect throttling and retry.

Answer

Adjust the API rate limits in the state machine definition.

Question 11

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. To determine whether an S3 object is eligible for lifecycle transition to S3 Glacier based on age, you need the object's creation date (LastModified) and the current date to compute its age. The exhibit presumably shows the object's LastModified timestamp, so the missing piece is the current date to calculate how many days old the object is relative to the lifecycle rule's transition threshold.

Answer

The ETag value

Answer

The metadata archive flag

Answer

The ContentLength value

Question 12

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.. The correct answer. Parquet is a columnar storage format that allows Athena to read only the columns needed for a query, reducing I/O and improving performance. Combined with effective partitioning, it enables partition pruning, which further limits the data scanned. CSV files are row-based and require full scans, even with partitioning. Option A is incorrect because Athena is serverless and users cannot increase nodes; resources are managed automatically. Option C is incorrect: JSON is also row-based and verbose, making it even slower than CSV. Option D is incorrect because larger CSV files still lead to full scans; Parquet's columnar nature is more impactful than file size.

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 13

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:GetClusterCredentials. AWS Glue jobs that write to Amazon Redshift using the native Redshift connection (not the Redshift Data API) authenticate by calling redshift:GetClusterCredentials to obtain temporary database credentials for the job role. Without that permission, the Glue connection cannot obtain a username/password and the job fails with an access-denied error even though the S3 read side works.

Answer

Add redshift:DataAPI

Answer

Add redshift:ModifyCluster

Answer

Add redshift:DescribeStatement

Question 14

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.. ORA-1555 'snapshot too old' occurs when Oracle cannot reconstruct a consistent read image because the required undo data has been overwritten. Increasing the UNDO tablespace size and raising UNDO_RETENTION gives Oracle more undo history to retain, allowing the long-running DMS full-load read to complete without losing its snapshot. This directly addresses the root cause of insufficient undo retention on the source.

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 15

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