DEA-C01 Data Operations and Support Practice Test 12 — 15 Questions

Question 1

A data engineer is troubleshooting an Amazon Redshift cluster that is running slowly. The cluster has 4 dc2.large nodes. The engineer runs a query that scans a large table and notices that the query uses only a single slice instead of all slices. The table is distributed with DISTSTYLE ALL. What is the most likely reason for the query using only one slice?

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

Accepted Answer

The table uses DISTSTYLE ALL, which stores the entire table on a single slice per node.. With DISTSTYLE ALL, Amazon Redshift places a full copy of the table on every compute node, but each node stores that copy on only one of its slices (specifically the slice with slice number 0). When a query scans a DISTSTYLE ALL table, only that single slice per node is engaged, so the query effectively runs on one slice per node rather than parallelizing across all slices. This is the expected behavior of DISTSTYLE ALL and explains why the engineer sees a single slice being used.

Answer

The query is running on the leader node instead of the compute nodes.

Answer

The workload management (WLM) queue is configured with a single query slot.

Answer

The table does not have a sort key defined.

Question 2

A company uses Amazon DynamoDB as a data store for a real-time dashboard application. The application performs point lookups and range queries on a table that has a partition key and sort key. The table uses on-demand capacity mode. Recently, the application's response time has increased, and CloudWatch metrics show high 'ThrottledRequests' for the table. The application uses the AWS SDK with default retry settings. The data access pattern is read-heavy with occasional spikes. What is the most effective way to reduce throttling?

Accepted Answer

Implement exponential backoff with jitter in the application code.. The table is in on-demand capacity mode, which automatically scales to accommodate workload, but throttling can still occur during sudden spikes because on-demand scaling is not instantaneous. The application uses default retry settings, which may not handle throttling gracefully. Implementing exponential backoff with jitter in the application code is the most effective way to reduce throttling because it spreads out retries and reduces the chance of repeated collisions, allowing the table to scale. This is a best practice for DynamoDB and is recommended in the AWS SDK.

Answer

Switch the table to provisioned capacity and set the read capacity units to a high value.

Answer

Enable DynamoDB Accelerator (DAX) to cache frequently read items.

Answer

Increase the read capacity units to a higher value.

Question 3

A company uses Amazon S3 to store log files from multiple applications. The logs are encrypted with AWS KMS (SSE-KMS). A data engineer needs to grant a new IAM user read-only access to the logs. The engineer attaches an S3 bucket policy that allows s3:GetObject and a KMS key policy that allows kms:Decrypt. However, the user still receives an 'Access Denied' error when trying to download an object. What is the MOST likely missing permission?

Accepted Answer

The user's IAM policy does not include kms:Decrypt permission.. To use SSE-KMS, the user needs kms:Decrypt, but also the IAM policy must allow kms:Decrypt, not just the key policy. The key policy alone is not sufficient if the IAM user's policy denies or does not allow the action. Option A is incorrect because s3:ListBucket is for listing, not downloading. Option B is incorrect because s3:GetObjectVersion is for versioned buckets. Option D is incorrect because kms:GenerateDataKey is for encryption, not decryption.

Answer

The user does not have s3:ListBucket permission on the bucket.

Answer

The user does not have s3:GetObjectVersion permission.

Answer

The user does not have kms:GenerateDataKey permission.

Question 4

A company runs a data lake on Amazon S3 with AWS Lake Formation for access control. The data lake contains sensitive customer information. A data scientist needs to query the data using Amazon Athena. The data scientist has been granted SELECT permission on the database and tables via Lake Formation. However, when the data scientist runs a query in Athena, they receive an error: 'Access denied. Please check your permissions.' The IAM role used by Athena has the following permissions: s3:GetObject, s3:ListBucket, and lakeformation:GetDataAccess. The Lake Formation admin has verified that the data scientist is a member of a Lake Formation data lake location and has been granted 'Describe' and 'Select' permissions on the table. What is the most likely reason for the access denied error?

Accepted Answer

The S3 bucket policy does not grant the Athena IAM role access to the S3 location.. The access denied error occurs because the IAM role used by Athena lacks the necessary S3 permissions, even though Lake Formation has granted SELECT and DESCRIBE permissions. Lake Formation handles permissions at the metadata level, but the actual data access still requires S3 bucket policy or IAM policy to allow s3:GetObject and s3:ListBucket for the specific S3 location. If the bucket policy does not grant the Athena IAM role access to the S3 location, the request will be denied. Option B correctly identifies this issue. Option A is incorrect because Lake Formation tag-based access control is not required for this scenario; the data scientist already has direct table permissions. Option C is incorrect because lakeformation:GetEffectivePermissions is not needed; the role already has lakeformation:GetDataAccess. Option D is incorrect because Athena uses the IAM role assigned to the workgroup, not the data scientist's individual IAM user.

Answer

The data scientist is not assigned to the correct Lake Formation tag.

Answer

The Athena IAM role is missing lakeformation:GetEffectivePermissions permission.

Answer

The data scientist's IAM user lacks the necessary S3 permissions.

Question 5

A data engineer is designing a disaster recovery plan for an Amazon RDS for MySQL database. The database must have a Recovery Point Objective (RPO) of less than 5 minutes and a Recovery Time Objective (RTO) of less than 30 minutes. Which TWO actions should the engineer take to meet these requirements?

Accepted Answer

Enable automated backups with a 1-day retention period and point-in-time recovery.. Option A is correct because automated backups with point-in-time recovery let you restore an RDS for MySQL instance to any second within the retention window (up to the latest 5-minute transaction log backup), which supports an RPO under 5 minutes. Option B is correct because a Multi-AZ deployment maintains a synchronously replicated standby in a second Availability Zone and automatically fails over in typically 60–120 seconds, satisfying the RTO of under 30 minutes. Option C is wrong because RDS backup retention is specified in days (1–35), not minutes, so a 5-minute retention period is not a valid configuration. Option D is wrong because a cross-Region read replica uses asynchronous replication with a much larger RPO and requires manual promotion, which cannot guarantee an RPO under 5 minutes or an RTO under 30 minutes. Option E is wrong because a single-AZ instance with a standby in another Region is not a supported RDS architecture and provides no automatic failover.

Answer

Enable automated backups with a 5-minute retention period.

Answer

Create a cross-Region read replica.

Answer

Use a single-AZ instance with a standby in another Region.

Question 6

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?

Accepted Answer

Verify that the IAM role used by the Glue job has the s3:PutObject permission for the staging bucket.. Option B is correct because the 'S3ServiceException: Access Denied' error on writing to S3 is typically caused by the Glue job's IAM role lacking the necessary write permission; the role must have s3:PutObject (and often s3:AbortMultipartUpload, s3:ListMultipartUploadParts) on the staging bucket to upload objects. Option E is correct because even if the IAM role grants s3:PutObject, an explicit Deny or a restrictive bucket policy on the staging bucket can block the write; the bucket policy must allow the Glue job's IAM role to perform s3:PutObject. Option A is not correct because an incorrect S3 path would produce a NoSuchBucket or 404-style error, not Access Denied. Option C is not correct because AWS Glue assumes the job's IAM role to access S3, so granting permissions to the 'glue.amazonaws.com' service principal directly is not the right fix. Option D is not correct because s3:GetObject on the source bucket relates to reading input data, not to the write failure on the staging 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 7

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 OutOfMemoryError in Spark executors is due to insufficient memory per executor. The current m5.xlarge nodes have 16 GB memory, which is shared among multiple executors and overhead. Switching to r5.xlarge (32 GB memory) doubles the memory per node, allowing executors to have more memory without increasing the number of nodes, which is cost-effective because it addresses the root cause (memory) rather than adding more nodes with the same memory constraints.

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 8

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.. The error indicates that MFA Delete is enabled on the S3 bucket. MFA Delete is a security feature that requires multi-factor authentication to permanently delete object versions or change the versioning state of the bucket. To suspend versioning, the engineer must first disable MFA Delete by using the AWS CLI with the --mfa parameter, providing a valid MFA device serial number and authentication code. Only after MFA Delete is disabled can versioning be suspended.

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 9

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.. In IAM policy evaluation, an explicit Deny always overrides any Allow. If the policy contains a Deny statement that matches the action (s3:PutObject) and resource (the confidential prefix), the upload will be denied regardless of any Allow statement. The exhibit likely shows a Deny for the confidential prefix, so the upload fails with 'Access Denied'.

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 10

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 11

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 12

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 13

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.. In Lake Formation, granting SELECT on a table is not sufficient for Athena queries — the principal also needs DESCRIBE permission on the table (and typically on the database) so that Athena can retrieve metadata from the Glue Data Catalog. Without DESCRIBE, Athena cannot resolve the table schema and the query fails, even though SELECT is granted and S3/KMS permissions are in place.

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 14

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 15

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.. In Amazon Redshift, the STV_PARTITIONS view shows information about slices, including 'tossed' rows. Tossed rows are rows that were previously deleted but not yet reclaimed by VACUUM. High tossed counts indicate that disk space is being wasted by deleted rows, and running VACUUM will reclaim that space and improve query performance. The other options are incorrect: tossed rows are not permanent, not related to sort keys, and not due to data skew.

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.