DEA-C01 Data Operations and Support Practice Test 24 — 15 Questions

Question 1

A company is using AWS Glue to run ETL jobs that process data from Amazon S3 and load it into Amazon Redshift. The data engineer notices that the Glue job is failing with the error 'S3ServiceException: Access Denied' when writing to the staging S3 bucket. Which TWO actions should the engineer take to resolve this issue?

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

Accepted Answer

Verify that the IAM role used by the Glue job has the s3:PutObject permission for the staging bucket.. Options B and E are correct. The Glue job's IAM role must have s3:PutObject permission on the staging S3 bucket (option B). Additionally, the S3 bucket policy must explicitly allow the Glue job's IAM role to perform s3:PutObject (option E). Option C is incorrect because AWS Glue uses the IAM role attached to the job for S3 operations, not the Glue service principal; granting the service principal write access is unnecessary and not a standard practice. Option A is incorrect because the error is a permission issue, not a script path issue. Option D is incorrect because the error is about writing to the staging bucket, not reading from the source bucket.

Answer

Ensure that the Glue job script is correctly referencing the S3 bucket path.

Answer

Ensure that the S3 bucket has a bucket policy that allows the AWS Glue service principal to write objects.

Answer

Verify that the IAM role has s3:GetObject permission for the source bucket.

Question 2

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is then processed by a scheduled AWS Glue ETL job that loads it into an Amazon Redshift table. Recently, the Glue job has been failing with the error: 'S3ServiceException: Access Denied'. The Firehose delivery stream is configured with a prefix and error logging to the same S3 bucket. The Glue job uses the same IAM role that has s3:GetObject and s3:ListBucket permissions on the bucket. What is the most likely cause?

Accepted Answer

The S3 bucket has default encryption enabled with AWS KMS (SSE-KMS), and the Glue job's IAM role lacks kms:Decrypt permission.. Firehose uses SSE-S3 by default unless configured otherwise. If the S3 bucket has default encryption enabled with SSE-KMS, Firehose will use that encryption, but the Glue job's IAM role may lack kms:Decrypt permission for the KMS key. The error 'Access Denied' when reading from S3 often indicates encryption permission issues. Option A is wrong because the Glue job can read from S3 with the current permissions if no encryption is involved. Option B is wrong because the error is about access, not schema. Option C is wrong because the Glue job can use the same role as Firehose, but the role may not have KMS permissions.

Answer

The Glue job expects a different data format than what Firehose writes.

Answer

The Glue job's IAM role does not have s3:GetObjectVersion permission.

Answer

The Glue job is using the wrong IAM role that does not have permissions to the S3 bucket.

Question 3

A data engineer is setting up a data pipeline using Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data must be transformed using an AWS Lambda function before delivery. Which THREE steps are required to configure this?

Accepted Answer

Create an AWS Lambda function that transforms the data.. Options B, C, and E are correct. To configure Kinesis Data Firehose with Lambda transformation, you need to create a Lambda function that transforms the data (B), attach an IAM role to the Firehose delivery stream that allows Firehose to invoke the Lambda function (C), and configure the Firehose delivery stream to use the Lambda function as a data transformation source (E). Option A is wrong because Lambda@Edge is used with CloudFront, not Firehose. Option D is wrong because S3 event notifications are not used for Firehose transformation; they are used for other purposes like triggering Lambda on new S3 objects.

Answer

Create a Lambda@Edge function in the same Region.

Answer

Configure an S3 event notification to trigger the Lambda function when new data arrives.

Question 4

A company runs a data pipeline that uses Amazon EMR to process large datasets. The pipeline reads data from S3, processes it using Spark, and writes results back to S3. Recently, the pipeline has been failing with 'OutOfMemoryError' in the Spark executors. The EMR cluster is configured with 5 core nodes of type m5.xlarge (4 vCPU, 16 GB memory each). The Spark application uses dynamic allocation and default Spark configurations. The input data size is approximately 500 GB in Parquet format. What is the most cost-effective way to resolve the out-of-memory errors?

Accepted Answer

Change the core node instance type to r5.xlarge (32 GB memory) and keep 5 nodes.. The current cluster has limited memory per node (16 GB). By switching to memory-optimized instances like r5.xlarge (32 GB), each node has double the memory, reducing the chance of OOM. This is more cost-effective than adding more nodes because the total memory per node increases without increasing the number of instances. Option A is wrong because increasing the number of nodes adds more memory but also more cost; it might be more expensive than using fewer, larger nodes. Option C is wrong because it's generally not recommended to increase spark.executor.memory beyond the physical memory; it could cause YARN to kill containers. Option D is wrong because Parquet is already efficient; changing to a different format may not solve memory issues.

Answer

Increase the spark.executor.memory setting to 8 GB in the Spark configuration.

Answer

Increase the number of core nodes to 10 to distribute the data across more executors.

Answer

Change the input data format from Parquet to ORC to reduce memory footprint.

Question 5

A data engineer attempts to suspend versioning on an S3 bucket but receives the error shown. The engineer needs to suspend versioning to reduce storage costs. What should the engineer do FIRST?

Accepted Answer

Disable MFA Delete by using the AWS CLI with the --mfa parameter and then suspend versioning.. To suspend versioning on an S3 bucket with MFA Delete enabled, you must first disable MFA Delete using the root account (or an account with appropriate permissions). The AWS CLI with the --mfa parameter is used to authenticate with MFA when performing sensitive operations, but disabling MFA Delete requires the root account. After disabling MFA Delete, versioning can be suspended. Option B is incorrect because the AWS Management Console does not bypass MFA Delete; you still need to disable MFA Delete first. Option C is incorrect because deleting and recreating the bucket is unnecessarily disruptive and versioning can be suspended directly after disabling MFA Delete. Option D is incorrect because the error is due to MFA Delete being enabled, not a bucket policy issue.

Answer

Use the AWS Management Console to suspend versioning, as it bypasses MFA Delete.

Answer

Delete the bucket and recreate it without versioning.

Answer

Add a bucket policy to allow versioning suspension.

Question 6

A data engineer is troubleshooting an access issue. A user has the IAM policy shown in the exhibit. The user attempts to upload an object to `s3://data-lake-bucket/confidential/report.pdf`. What will happen?

Accepted Answer

The upload will fail with an 'Access Denied' error.. The explicit Deny statement in the IAM policy overrides the Allow statement, so the user is denied permission to upload to the 'confidential' path. Therefore, the upload will fail with an 'Access Denied' error, making option A correct.

Answer

The upload will succeed because the Deny statement is not valid without a condition.

Answer

The upload will succeed because the Allow statement is more specific than the Deny.

Answer

The upload will succeed because the user has s3:PutObject permission on the bucket.

Question 7

A company runs a data pipeline on Amazon EMR that processes terabytes of data daily. The pipeline reads from Amazon S3, performs transformations using Spark, and writes results back to S3. Recently, the data engineer noticed that the EMR cluster's spot instances are frequently reclaimed, causing job failures and delays. The cluster uses a mix of On-Demand and Spot instances. The engineer wants to minimize job interruptions while keeping costs low. The current configuration uses a single EMR cluster with a core node group of 10 On-Demand instances and a task node group of 20 Spot instances. The job failures occur during the shuffle phase when tasks on Spot instances are lost. The engineer has no control over when spot instances are reclaimed. Which action will MOST effectively reduce job failures while maintaining cost efficiency?

Accepted Answer

Enable EMR managed scaling to automatically add On-Demand instances when Spot instances are reclaimed.. Enabling EMR managed scaling allows the cluster to automatically add On-Demand instances when Spot instances are reclaimed, providing dynamic capacity to prevent job failures during the shuffle phase without significantly increasing costs. Option A is incorrect because increasing On-Demand instances in the core node group does not directly address the loss of Spot task nodes and can increase costs. Option B is incorrect because increasing the bid price to the On-Demand price does not guarantee avoidance of reclaims and still relies on Spot instances. Option C is incorrect because using only On-Demand instances would eliminate Spot savings and increase costs significantly.

Answer

Increase the number of On-Demand instances in the core node group to 20.

Answer

Configure the task node group to use only Spot instances and increase the bid price to the On-Demand price.

Answer

Change the task node group to use only On-Demand instances.

Question 8

A data engineer is troubleshooting an AWS Glue ETL job that fails with a 'java.lang.OutOfMemoryError: Java heap space' error. The job processes a 50 GB Parquet file from an S3 bucket. The job uses a G.1X DPU (16 GB memory) and default parameters. Which action should the engineer take to resolve the issue?

Accepted Answer

Change the worker type to G.2X (32 GB memory).. Changing the worker type to G.2X (32 GB memory) doubles the memory per worker, directly addressing the Java heap space error. Option B is incorrect because increasing the number of workers does not increase memory per worker; each G.1X DPU still has only 16 GB. Option C is incorrect because increasing the batch size would increase the amount of data loaded into memory per worker, potentially worsening the memory issue. Option D is incorrect because converting from Parquet to JSON typically increases file size and memory usage due to lack of compression and columnar storage.

Answer

Increase the number of workers from 2 to 4.

Answer

Increase the 'batch size' parameter in the DynamicFrame reader.

Answer

Convert the input data from Parquet to JSON format.

Question 9

A company runs a nightly AWS Glue ETL job that writes results to an Amazon Redshift table using the JDBC connector. Recently, the job has been failing with the error 'ERROR:  connection to server at ... failed: server closed the connection unexpectedly'. The Redshift cluster is in a private subnet with a VPC endpoint for S3. The Glue job runs in the same VPC with enhanced VPC routing enabled. Which is the most likely cause?

Accepted Answer

SSL is not enabled on the Redshift cluster.. The error 'server closed the connection unexpectedly' typically indicates that the Redshift cluster rejected the connection, often because SSL is required but not enabled in the Glue JDBC connection. Redshift clusters can be configured with the `require_ssl` parameter set to true, forcing all connections to use SSL. Option B is correct because without SSL, the server terminates the connection. Option A is incorrect because the 'redshift' compatibility mode is not a standard requirement; the JDBC driver works without it. Option C is incorrect because security group issues would cause a timeout or 'connection refused', not a server-side close. Option D is incorrect because Glue supports Redshift as a data source.

Answer

The JDBC driver is missing the 'redshift' compatibility mode setting.

Answer

The Glue job's security group does not allow outbound traffic to the Redshift cluster.

Answer

AWS Glue does not support Redshift as a data source.

Question 10

A data engineer is monitoring an Amazon Kinesis Data Stream with a shard count of 10. The stream receives 5 MB/s of write traffic and 10 MB/s of read traffic. The engineer notices that writes are throttled with ProvisionedThroughputExceededException errors. Which action should the engineer take to resolve the throttling?

Accepted Answer

Configure auto-scaling on the stream.. ProvisionedThroughputExceededException occurs when a shard's write throughput exceeds 1 MB/s, often due to hot shards from uneven partition key distribution. While increasing shard count (Option A) can help spread the load, it does not automatically fix the root cause if partition keys remain skewed. The best action is to configure auto-scaling (Option D), which in Amazon Kinesis Data Streams can be achieved by switching to on-demand mode. On-demand mode automatically scales capacity based on traffic patterns, eliminating throttling without manual intervention. Option B decreases write capacity, worsening the issue. Option C only improves read throughput, not write.

Answer

Increase the shard count to 20.

Answer

Decrease the shard count to 5.

Answer

Enable enhanced fan-out on the stream.

Question 11

A company uses AWS Lake Formation to manage permissions on a data lake stored in S3. A data analyst reports that they can see a table in the AWS Glue Data Catalog but cannot query it using Amazon Athena. The analyst has been granted 'SELECT' permission on the table in Lake Formation. The table's underlying S3 location is encrypted with AWS KMS. The IAM role used by Athena has the necessary S3 and KMS permissions. What is the most likely reason for the failure?

Accepted Answer

The analyst does not have 'DESCRIBE' permission on the table.. Lake Formation requires explicit grant of 'DESCRIBE' permission on the table for Athena to read metadata; SELECT alone is insufficient. Option B is incorrect because Athena can be integrated with Lake Formation. Option C is incorrect because KMS permissions are already in place. Option D is incorrect because the analyst can see the table, meaning DESCRIBE is not required at the database level.

Answer

Athena is not integrated with Lake Formation.

Answer

The KMS key policy does not allow the analyst's IAM role to decrypt.

Answer

The analyst does not have 'DESCRIBE' permission on the database.

Question 12

A data engineer is using AWS DMS to migrate a 2 TB Oracle database to Amazon Aurora PostgreSQL. The migration is running in full load mode with ongoing replication. After the full load completes, the ongoing replication task shows a 'TargetMetadata' error: 'ERROR:  duplicate key value violates unique constraint'. The engineer verifies that the target table already contains the data. What should the engineer do to resolve this issue?

Accepted Answer

Enable 'BatchApplyEnabled' and set 'TaskRecoveryTableEnabled' to false in the task settings.. Enabling 'BatchApplyEnabled' allows DMS to batch changes and reduces duplicate key errors, while setting 'TaskRecoveryTableEnabled' to false prevents recovery attempts that can reapply already committed transactions. Option B is wrong because disabling constraints compromises data integrity. Option C is wrong because truncating and restarting loses existing data and does not address the ongoing replication issue. Option D is wrong because dropping indexes does not prevent duplicate key violations.

Answer

Disable the unique constraint on the target table.

Answer

Truncate the target table and restart the full load.

Answer

Drop the indexes on the target table and recreate them after the migration.

Question 13

A data engineer is running an Amazon EMR cluster with Spark to process log files. The cluster uses instance fleets with m5.xlarge core nodes. The engineer observes that the Spark job is running slower than expected. CloudWatch metrics show that the cluster's CPU utilization is below 20% but memory utilization is near 90%. Which configuration change would most likely improve performance?

Accepted Answer

Use memory-optimized instances (r5.xlarge) for core nodes.. High memory utilization (90%) with low CPU (<20%) indicates that the data does not fit in memory, causing frequent spills to disk. Using memory-optimized instances (r5.xlarge) provides more memory per vCPU compared to m5.xlarge, allowing more data to be kept in memory and reducing spills. Option B is incorrect because increasing the number of core nodes adds more CPU and memory overall, but each node still has the same memory-to-CPU ratio (8 GB per 4 vCPUs for m5.xlarge), so memory pressure per node remains. Option C is incorrect because the issue is insufficient memory, not the number of shuffle partitions; adjusting shuffle partitions does not increase available memory. Option D is incorrect because decreasing the number of core nodes reduces total cluster memory, worsening the memory bottleneck.

Answer

Increase the number of core nodes from 5 to 10.

Answer

Increase the number of Spark shuffle partitions.

Answer

Decrease the number of core nodes to reduce overhead.

Question 14

A company uses Amazon Redshift for its data warehouse. A data engineer notices that queries are running slowly and the system's disk space is nearly full. The engineer runs the STV_PARTITIONS view and sees that many slices have high 'tossed' counts. What does this indicate, and what should the engineer do?

Accepted Answer

The tossed rows are deleted rows that need to be reclaimed by running VACUUM.. The STV_PARTITIONS view shows 'tossed' rows, which are rows that have been deleted or updated and are waiting to be reclaimed by a VACUUM operation. High tossed counts indicate wasted disk space, and running VACUUM reclaims that space, improving performance. Option D correctly describes this. Option A is incorrect because tossed rows are not permanent; they can be reclaimed. Option B is incorrect because tossed rows are not related to sort key optimization. Option C is incorrect because tossed rows are not caused by data skew; data skew is about uneven distribution.

Answer

The tossed rows are permanent and cannot be reclaimed; the engineer should perform a deep copy to a new table.

Answer

The tossed rows indicate that the sort key is not optimal; redefining the sort key will reduce tossed rows.

Answer

The tossed rows are due to data skew; redistribute the table on a different distribution key.

Question 15

A data engineer is troubleshooting an AWS Glue job that writes data to an Amazon S3 bucket in Parquet format. The job runs successfully but the output files are smaller than the configured 'groupFiles' size. The engineer has set 'groupFiles' to 'inPartition' and 'groupSize' to 1 GB. The input data is 10 GB in a single partition. What is the most likely reason for the small files?

Accepted Answer

The 'groupFiles' parameter only affects the input read phase, not the output write phase.. 'groupFiles' only works when the input data is already small and needs to be coalesced. However, if the input is large and the job writes output, the output file size is determined by the number of Spark partitions, not grouping. The grouping feature only applies to reading input files. Option A is wrong because the setting is correct. Option C is wrong because grouping is a read-time feature, not write-time. Option D is wrong because grouping does not require repartitioning.

Answer

The 'groupFiles' parameter is deprecated in the current Glue version.

Answer

The 'groupFiles' parameter is misspelled or set incorrectly.

Answer

The engineer must also set 'repartition' to 1 to merge output files.