DEA-C01 Practice Exam 68 — 20 Questions

Question 1

A company uses Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function is failing with 'ProvisionedThroughputExceededException' when writing to a DynamoDB table. Which action should the data engineer take to resolve this without losing data?

Accepted Answer

Configure a dead-letter queue (DLQ) on the Lambda function and increase the DynamoDB write capacity.. Option B is correct because adding a dead-letter queue (DLQ) on the Lambda function captures failed records for later reprocessing, preventing data loss. Option A (disable retries) would lose data. Option C (increase DynamoDB write capacity) addresses the throughput but does not handle failures that may still occur. Option D (reduce shards) would reduce the ingestion rate but may not solve the issue and could cause data loss.

Answer

Reduce the number of Kinesis shards to lower the ingestion rate.

Answer

Increase the DynamoDB table's read capacity.

Answer

Disable retries on the Lambda function to avoid throttling.

Question 2

A company is running a critical Amazon RDS for MySQL database. They need to implement a backup strategy that allows point-in-time recovery (PITR) with a recovery time objective (RTO) of 15 minutes and a recovery point objective (RPO) of 5 minutes. Which solution meets these requirements?

Accepted Answer

Enable automated backups with 1-day retention and enable Multi-AZ deployment. Option A is correct because automated backups with 5-minute transaction logs meet RPO and a Multi-AZ failover meets RTO. Option B is wrong because manual snapshots cannot achieve 5-minute RPO. Option C is wrong because cross-Region replication adds latency. Option D is wrong because Read Replicas are not for failover.

Answer

Use cross-Region automated backups and promote the replica

Answer

Take manual snapshots every 5 minutes and restore from the latest snapshot

Answer

Enable a Read Replica and promote it during a disaster

Question 3

A data engineer is troubleshooting an AWS Glue ETL job that suddenly started failing with 'An error occurred while calling o103.pyWriteDynamicFrame. Unknown error'. The job writes data to an Amazon Redshift table. Which step should the engineer take FIRST?

Accepted Answer

Review the job's CloudWatch Logs for detailed error messages.. Option A is correct because the error is generic; checking CloudWatch Logs provides more details. Option B is wrong because it assumes a JDBC driver issue without evidence. Option C is wrong because testing a small dataset may not reproduce the issue. Option D is wrong because the error is not specific to table structure.

Answer

Recreate the Redshift table with a different distribution style.

Answer

Test the job with a small sample dataset to isolate the issue.

Answer

Update the Redshift JDBC driver version in the Glue job.

Question 4

Which TWO actions are effective ways to monitor the health of an Amazon DynamoDB table? (Choose two.)

Accepted Answer

Enable DynamoDB Streams and process with Lambda to detect failures.. Options A and D are correct. CloudWatch metrics like ConsumedReadCapacityUnits and ThrottledRequests provide insight into table health. Option B is wrong because there is no 'TableHealth' metric. Option C is wrong because S3 is not for DynamoDB monitoring. Option E is wrong because EC2 is not relevant.

Answer

Use AWS S3 inventory to track table size.

Answer

Use EC2 instance status checks.

Answer

Monitor the 'TableHealth' metric in CloudWatch.

Question 5

A company is ingesting streaming data from thousands of IoT devices into Amazon Kinesis Data Streams. The data is processed by a Kinesis Data Analytics application. Recently, the application started reporting high iterator age (millisBehindLatest). Which action would BEST reduce the iterator age?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Option C is correct because increasing the number of shards increases throughput and reduces iterator age. Option A is incorrect because increasing retention does not affect processing speed. Option B is incorrect because decreasing retention may cause data loss. Option D is incorrect as a larger record size could increase processing time.

Answer

Decrease the data retention period of the Kinesis stream.

Answer

Increase the data retention period of the Kinesis stream.

Answer

Increase the record size limit in the Kinesis stream.

Question 6

A data engineer is designing a data pipeline that processes streaming data. The pipeline must be able to handle duplicate records and ensure exactly-once processing semantics. Which THREE AWS services or features should the engineer consider? (Choose three.)

Accepted Answer

Amazon EMR with Apache Flink for exactly-once semantics.. Option A is correct because Kinesis Data Streams provides sequence numbers for records, enabling deduplication. Option C is correct because Kinesis Data Analytics uses the concept of 'idempotent' output to achieve exactly-once processing. Option D is correct because Apache Flink on Amazon EMR provides exactly-once processing through checkpointing. Option B is incorrect because Kinesis Firehose delivers at-least-once, not exactly-once. Option E is incorrect because DynamoDB Streams is at-least-once.

Answer

Amazon Kinesis Data Firehose with automatic retries.

Answer

Amazon DynamoDB Streams for change data capture.

Question 7

A data engineer notices that an AWS Glue ETL job is failing with a 'MemoryError' when processing a large dataset. Which approach should the engineer take to resolve this issue?

Accepted Answer

Increase the number of DPUs for the job.. Option A is correct because increasing the number of DPUs (Data Processing Units) allocates more memory and processing capacity to the job, which can resolve memory errors for large datasets. Option B is incorrect because S3 Select does not help with memory in Glue jobs. Option C is incorrect because reducing the number of partitions may increase memory pressure. Option D is incorrect because changing the file format to JSON typically increases memory usage.

Answer

Change the source file format from Parquet to JSON.

Answer

Reduce the number of partitions in the source data.

Answer

Use S3 Select to filter data before processing.

Question 8

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 9

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed in real-time using a custom Lambda function. Which TWO steps are required to enable this? (Choose TWO)

Accepted Answer

Configure Kinesis Data Firehose to use a Lambda function for data transformation. Options A and D are correct. Kinesis Data Firehose can invoke a Lambda function for transformation. The Lambda function must return the transformed records to Firehose. Option B is wrong because Kinesis Data Analytics is for analytics, not transformation. Option C is wrong because the transformation is done by Lambda, not within Firehose. Option E is wrong because Kinesis Data Streams is not required for Firehose.

Answer

Create a Kinesis Data Analytics application to transform the data

Answer

Write the transformation logic directly in the Firehose delivery stream configuration

Answer

Use Kinesis Data Streams as the source for Firehose

Question 10

A data engineer needs to transfer 50 TB of data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 500 Mbps. Which TWO methods are appropriate for this transfer? (Choose TWO.)

Accepted Answer

Order an AWS Snowball Edge device to physically ship the data.. Options A and C are correct. AWS Snowball Edge is a physical device for large data transfers over slow networks. AWS DataSync can transfer data over the network with optimization. Option B is wrong because S3 Transfer Acceleration speeds up transfers but still requires network bandwidth. Option D is wrong because AWS Direct Connect is a dedicated network connection, not a transfer method. Option E is wrong because Amazon Kinesis is for streaming data, not bulk transfer.

Answer

Set up an AWS Direct Connect connection for higher bandwidth.

Answer

Use S3 Transfer Acceleration to upload over the internet.

Answer

Use Amazon Kinesis Data Firehose to stream the data.

Question 11

A data engineer has attached the IAM policy shown in the exhibit to a role used by an AWS Glue ETL job. The job fails when trying to write to the S3 bucket 'example-bucket' with the error: 'Access Denied'. What is the MOST likely reason?

Accepted Answer

The S3 bucket policy denies the PutObject action for the role.. Option C is correct because the Glue job's IAM role may not have permission to call the s3:PutObject action, but the error could also be due to the bucket policy denying access. However, the policy shown allows s3:PutObject on the bucket. The most common issue is that the Glue job's role does not have the necessary trust policy or the bucket policy blocks the request. But based on the exhibit, the policy appears correct. The error could be due to missing permissions on the Glue job's execution role. Option A is incorrect because the policy includes s3:PutObject. Option B is incorrect because the policy includes the bucket ARN. Option D is incorrect because the error is access denied, not a bucket policy issue. Actually, the correct answer is that the role may also need s3:PutObject on the bucket itself (not just objects) for certain operations like multipart uploads. But the most likely reason is that the bucket policy denies the request. Given the exhibit, the IAM policy is correct, so the issue is likely the bucket policy. Option C is the best answer.

Answer

The IAM policy does not include the bucket ARN for write operations.

Answer

The IAM role's trust policy does not allow Glue to assume the role.

Answer

The IAM policy does not grant s3:PutObject permission.

Question 12

A data engineer is setting up an AWS Glue job to process data from an Amazon S3 bucket. The job fails with an 'Access Denied' error. Which TWO IAM permissions are MOST likely missing from the Glue job's IAM role?

Accepted Answer

s3:PutObject. Options A and C are correct. Glue needs s3:GetObject to read from S3 and s3:PutObject to write output. Option B is incorrect because glue:StartJobRun is not needed for the job itself. Option D is incorrect because kms:Decrypt is needed only if S3 uses KMS encryption. Option E is incorrect because dynamodb:GetItem is not relevant unless accessing DynamoDB.

Answer

kms:Decrypt

Answer

dynamodb:GetItem

Answer

glue:StartJobRun

Question 13

A data engineer runs the above AWS CLI command to investigate who uploaded a file to an S3 bucket. The output shows the event was recorded. Which additional step is needed to confirm the identity of the user?

Accepted Answer

No additional step is needed; the 'Username' field already identifies the IAM user.. Option C is correct because the 'Username' field shows the ARN of the IAM user, which is the identity that made the API call. Option A (access key ID) is not shown in the output. Option B (IP address) is not in the output. Option D (MFA) is not recorded in this field.

Answer

Use the 'MFA' field to check if multi-factor authentication was used.

Answer

View the 'accessKeyId' field in the CloudTrailEvent JSON.

Answer

Look up the 'sourceIPAddress' in the CloudTrailEvent.

Question 14

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 15

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 16

Refer to the exhibit. A data engineer is reviewing the configuration of an Amazon Redshift cluster. The engineer wants to ensure that the cluster can be restored to a point in time up to 35 days in the past. Based on the exhibit, what change is needed?

Accepted Answer

Increase the automated snapshot retention period to 35 days.. Option C is correct. The AutomatedSnapshotRetentionPeriod is set to 1 day, which allows only 1 day of point-in-time recovery. To support 35 days, this value must be increased to 35. Option A is incorrect because the cluster is already encrypted. Option B is incorrect because the number of nodes does not affect snapshot retention. Option D is incorrect because the subnet group does not affect retention.

Answer

Change the cluster subnet group to a custom one.

Answer

Enable encryption on the cluster.

Answer

Increase the number of nodes to 6.

Question 17

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 logs show a permission error. Which IAM policy change would resolve the issue?

Accepted Answer

Add redshift:DataAPI access to the Glue job's IAM role. Option B is correct because the Glue job needs permission to write to Redshift. Option A is wrong because the job already reads from S3. Option C is wrong because Redshift needs its own IAM role. Option D is wrong because KMS is not mentioned.

Answer

Enable encryption on the S3 bucket using AWS KMS

Answer

Add s3:GetObject permission to the Glue job's IAM role

Answer

Attach an IAM role with redshift:GetClusterCredentials to the Redshift cluster

Question 18

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 19

A company runs a data lake on Amazon S3 with AWS Glue for ETL. The data is stored in Parquet format and partitioned by date. The data engineer notices that queries using Amazon Athena are scanning large amounts of data even when filtering on the partition column. Which TWO actions would improve query performance? (Choose TWO)

Accepted Answer

Ensure that the WHERE clause uses the partition column correctly. Option B and D are correct. Partition pruning is most effective when the partition column is used correctly in the WHERE clause, and using columnar formats like Parquet with predicate pushdown reduces data scanned. Option A is wrong because adding more partitions may not help if the filter is not applied. Option C is wrong because converting to CSV would increase data scanned. Option E is wrong because using a different file format may not help if partitioning is not leveraged.

Answer

Use a different file format like Avro

Answer

Convert the data from Parquet to CSV for better compression

Answer

Increase the number of partitions by adding a second partition column

Question 20

A data engineer has set up an AWS Lambda function that processes files uploaded to an S3 bucket. The function is triggered by S3 event notifications. However, the function is not being invoked when a file is uploaded. The engineer checks the Lambda function's CloudWatch Logs and finds no execution logs. What should the engineer check FIRST?

Accepted Answer

Verify that the S3 bucket has an event notification configured for the Lambda function.. Option A is correct because the S3 bucket must have an event notification configured to trigger the Lambda function. Option B is wrong because function code errors would appear in logs after invocation. Option C is wrong because the IAM role affects execution, not invocation trigger. Option D is wrong because VPC configuration affects network access, not whether the function is triggered.

Answer

Check the Lambda function's code for errors.

Answer

Verify that the Lambda function's IAM role has permissions to read from S3.

Answer

Check if the Lambda function is attached to a VPC.