DEA-C01 Data Operations and Support Practice Test 18 — 15 Questions

Question 1

A company uses Amazon DynamoDB as the primary data store for a high-traffic application. Recently, read latency has increased significantly. The DynamoDB table has on-demand capacity mode. Which action is MOST effective to reduce read latency?

Accepted Answer

Add a DynamoDB Accelerator (DAX) cluster in front of the table. Option B is correct because adding a DynamoDB Accelerator (DAX) cluster provides an in-memory cache, reducing read latency significantly. Option A is wrong because on-demand capacity already scales automatically. Option C is wrong because increasing read capacity units is not applicable in on-demand mode. Option D is wrong because Global Tables do not reduce read latency for local reads.

Answer

Switch the table to provisioned capacity mode with higher read capacity

Answer

Increase the read capacity units in the table's auto scaling settings

Answer

Enable DynamoDB Global Tables to distribute reads across regions

Question 2

A data engineer is troubleshooting a slow-running Amazon Redshift query. The query involves a large fact table with a distribution style of EVEN and a sort key on date. The table has 10 slices. The engineer notices that the query is performing a broadcast join with a small dimension table. Which change would most improve performance?

Accepted Answer

Change the fact table to DISTSTYLE KEY on the join column. Option C is correct because changing the fact table to KEY distribution on the join column reduces data movement. Option A is wrong because ALL distribution on the dimension table is better but not listed. Option B is wrong because it does not reduce the broadcast. Option D is wrong because removing sort key would hurt performance for range queries.

Answer

Remove the sort key and use a compound sort key on the join column

Answer

Change the dimension table to DISTSTYLE ALL

Answer

Increase the number of slices by resizing the cluster

Question 3

A data engineer needs to monitor the number of records processed by an AWS Glue ETL job. Which CloudWatch metric should the engineer use?

Accepted Answer

glue.driver.aggregate.numRecords. Option B is correct because Glue emits a 'glue.driver.aggregate.numRecords' metric for the number of records processed. Option A is wrong because 'glue.driver.aggregate.elapsedTime' is for time. Option C is wrong because 'glue.driver.aggregate.bytesRead' is for bytes. Option D is wrong because 'glue.driver.aggregate.recordsRead' is not a standard metric.

Answer

glue.driver.aggregate.elapsedTime

Answer

glue.driver.aggregate.bytesRead

Answer

glue.driver.aggregate.recordsRead

Question 4

A data engineer is designing a data pipeline using AWS Step Functions to orchestrate multiple AWS Glue ETL jobs. The pipeline must handle failures and retries. Which TWO configurations should the engineer use to ensure the pipeline is resilient? (Choose two.)

Accepted Answer

Configure the state machine to use a 'Catch' rule to handle specific errors and transition to a fallback state. Options A and D are correct. Option A: Adding a Catch rule to the state machine handles errors. Option D: Setting a timeout prevents stuck executions. Option B is wrong because exponential backoff is for retry, not catch. Option C is wrong because parallel state is for concurrency, not resilience. Option E is wrong because DLQ is for Lambda, not state machines.

Answer

Configure a dead-letter queue (DLQ) for the state machine

Answer

Set the 'Retry' interval to a fixed value instead of exponential backoff

Answer

Use a 'Parallel' state to run multiple Glue jobs simultaneously

Question 5

A company stores sensitive data in Amazon S3 and requires that all data be encrypted at rest. The data is accessed by multiple AWS services. Which solution meets the encryption requirement with the LEAST operational overhead?

Accepted Answer

Enable S3 default encryption with SSE-S3. Option D is correct because enabling S3 default encryption with SSE-S3 provides server-side encryption automatically without any key management overhead. Option A is wrong because client-side encryption requires managing keys. Option B is wrong because SSE-KMS requires managing KMS keys. Option C is wrong because SSE-C requires managing your own keys.

Answer

Use server-side encryption with AWS KMS (SSE-KMS)

Answer

Use client-side encryption with AWS KMS

Answer

Use server-side encryption with customer-provided keys (SSE-C)

Question 6

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application is experiencing high latency and checkpoint failures. Which THREE actions should the data engineer take to improve performance and reliability? (Choose three.)

Accepted Answer

Increase the parallelism of the Flink application. Options A, C, and E are correct. Option A: Increasing parallelism improves throughput. Option C: Increasing checkpoint interval reduces checkpoint failures. Option E: Using event time helps with out-of-order data. Option B is wrong because decreasing parallelism reduces throughput. Option D is wrong because disabling checkpointing hurts reliability.

Answer

Decrease the parallelism to reduce resource contention

Answer

Disable checkpointing to avoid checkpoint failures

Question 7

A data pipeline using Amazon Kinesis Data Streams is experiencing high consumer lag. The stream has 10 shards. The consumer is an AWS Lambda function that processes each record and writes to Amazon DynamoDB. What is the MOST likely cause of the lag?

Accepted Answer

The Lambda function's reserved concurrency is set too low. Option C is correct because if the Lambda function's concurrency limit is reached, it cannot process all shards simultaneously, causing lag. Option A is wrong because increasing shards would increase parallelism but is not the root cause. Option B is wrong because Lambda can process from multiple shards. Option D is wrong because DynamoDB write capacity could be a bottleneck but is less likely than Lambda concurrency.

Answer

The DynamoDB table's write capacity is throttling writes

Answer

The number of shards is insufficient for the data volume

Answer

The Lambda function is not authorized to read from Kinesis

Question 8

A data engineer is setting up Amazon CloudWatch alarms for an Amazon Redshift cluster. The engineer wants to be alerted when the disk space usage exceeds 80% for more than 5 minutes and when the CPU utilization exceeds 90% for more than 10 minutes. Which TWO CloudWatch metrics and conditions should the engineer use? (Choose two.)

Accepted Answer

Metric: CPUUtilization; Condition: > 90 for 10 minutes. Options B and D are correct. Option B: PercentageDiskSpace metric with threshold 80. Option D: CPUUtilization metric with threshold 90. Option A is wrong because WLM is not for disk. Option C is wrong because NetworkReceiveThroughput is not relevant. Option E is wrong because DatabaseConnections is not about CPU.

Answer

Metric: DatabaseConnections; Condition: > 500 for 5 minutes

Answer

Metric: NetworkReceiveThroughput; Condition: > 1 GB for 10 minutes

Answer

Metric: WLMQueueLength; Condition: > 100 for 5 minutes

Question 9

A data engineer needs to transform a large dataset stored in Amazon S3 using Apache Spark. The engineer wants to minimize startup time and use a serverless approach. Which AWS service should the engineer use?

Accepted Answer

AWS Glue. Option A is correct because AWS Glue provides a serverless Spark environment with fast startup. Option B is wrong because Amazon EMR requires cluster provisioning. Option C is wrong because Amazon Redshift is a data warehouse, not a Spark environment. Option D is wrong because Amazon Athena is for querying, not transforming with Spark.

Answer

Amazon Redshift

Answer

Amazon EMR

Answer

Amazon Athena

Question 10

A data engineer is responsible for a data pipeline that uses Amazon S3 as a data lake, AWS Glue for ETL, and Amazon Athena for ad-hoc queries. The pipeline ingests CSV files from an external partner via SFTP into an S3 bucket. The files are then processed by a Glue job that converts them to Parquet and writes to a separate S3 bucket partitioned by date. The Glue job runs daily and is triggered by a scheduled CloudWatch Events rule. Recently, the data engineer noticed that some days the Glue job fails because of memory errors, and on those days the Athena queries that rely on the data return incomplete results. The engineer needs to ensure that the pipeline is resilient and that Athena queries always see a complete view of the data, even if the Glue job fails mid-run. The engineer also needs to minimize re-processing of data. Which course of action should the engineer take?

Accepted Answer

Modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully.. Option B is correct. Using Glue job bookmarks enables incremental processing and the ability to resume from the last successful checkpoint. Staging the data in a temporary location and moving it atomically ensures that Athena sees only complete data. Option A is wrong because increasing worker capacity does not prevent partial writes. Option C is wrong because using Lambda for conversion is less scalable and error-prone. Option D is wrong because partition projection does not solve the atomicity issue.

Answer

Increase the number of workers and the worker type to G.2X to handle the memory errors, and enable job retries.

Answer

Replace the Glue job with an AWS Lambda function that processes the CSV files and writes Parquet to S3, and use S3 Event Notifications to trigger the function.

Answer

Use Athena partition projection to automatically discover partitions and set up a retry mechanism using AWS Step Functions.

Question 11

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. After the migration, the data in Aurora is inconsistent with the source. The engineer needs to ensure ongoing replication with minimal downtime. Which solution should the engineer implement?

Accepted Answer

Configure a DMS task with change data capture (CDC). Option B is correct because using DMS with change data capture (CDC) captures ongoing changes and replicates them with minimal downtime. Option A is wrong because full load only captures a snapshot. Option C is wrong because AWS Schema Conversion Tool does not handle data replication. Option D is wrong because exporting and importing does not provide ongoing replication.

Answer

Use AWS Schema Conversion Tool (SCT) to convert the schema

Answer

Export the data from Oracle and import into Aurora using mysqldump

Answer

Perform a full load migration again

Question 12

A data engineering team notices that an AWS Glue ETL job, which processes hourly data from an S3 bucket, is taking progressively longer to run. The job reads Parquet files partitioned by date and hour. Which action is MOST likely to improve the job's performance?

Accepted Answer

Enable pushdown predicate filtering on the job's data source.. Option C is correct because enabling pushdown predicate filtering in Spark reduces the amount of data read by pruning partitions early. Option A is wrong because Glue jobs already use distributed processing. Option B is wrong because increasing the number of DPUs can help but is not the most direct fix for the described symptom. Option D is wrong because converting to CSV would increase I/O and processing time.

Answer

Convert Parquet files to CSV to improve read performance.

Answer

Increase the number of DPUs for the job.

Answer

Switch from Spark to Python shell for simpler processing.

Question 13

A company runs a time-series forecasting model that writes results to an S3 bucket every 5 minutes. A downstream ETL job reads this data, but sometimes fails because it encounters incomplete files (zero bytes). What is the MOST reliable way to ensure the ETL job only processes complete files?

Accepted Answer

Use S3 Event Notifications to trigger a Lambda function that checks file size and then moves the file to a 'ready' prefix.. Option B is correct because S3 Event Notifications with a Lambda function can process only after a final PUT, and writing to a temporary prefix then renaming ensures atomicity. Option A is wrong because S3 Copy can't detect completeness. Option C is wrong because S3 Select still reads incomplete files. Option D is wrong because S3 Lifecycle policies manage lifecycle, not completeness.

Answer

Set an S3 Lifecycle policy to delete files smaller than 1 MB.

Answer

Use S3 Copy to move files to a 'processed' folder after the ETL job reads them.

Answer

Configure S3 Select to query the files and only return rows if the file is complete.

Question 14

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error 'java.lang.OutOfMemoryError: Java heap space'. The job processes a large number of small files in Amazon S3. Which action would MOST effectively resolve the issue?

Accepted Answer

Enable S3 groupFiles option in the Glue job. Option D is correct because grouping small files into fewer, larger splits reduces the number of Spark partitions and memory overhead. Option A is wrong because increasing the number of workers increases parallelism but may not fix heap space per worker. Option B is wrong because using a different instance type with more memory per worker could help, but grouping files is more effective. Option C is wrong because changing worker type to G.1X increases memory but may not solve the root cause of too many small files.

Answer

Change the worker type to G.1X

Answer

Increase the number of workers in the Glue job

Answer

Use a G.2X worker type with more memory

Question 15

A data engineer needs to troubleshoot why an AWS Glue job is failing with a 'Insufficient Memory' error. The job processes a 10 GB dataset. Which step should the engineer take FIRST?

Accepted Answer

Increase the number of DPUs allocated to the job.. Option A is correct because increasing the DPU count allocates more memory per worker. Option B is wrong because Glue supports Python and Scala, not Java. Option C is wrong because repartitioning may or may not help; memory is the immediate issue. Option D is wrong because using Apache Spark is already the default; switching to Python shell would not handle the volume.

Answer

Switch from using Apache Spark to Python shell.

Answer

Repartition the data into more partitions within the job.

Answer

Change the job type from Python to Java.