DEA-C01 Practice Test 10 — 10 Questions

Question 1

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?

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

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 2

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.. The correct first step is to increase the number of DPUs allocated to the job. The 'Insufficient Memory' error indicates that the current memory per worker is insufficient for processing the 10 GB dataset. Increasing DPUs provides more memory and compute resources, directly addressing the issue. Option A is incorrect because Python shell jobs are single-threaded and cannot handle large datasets efficiently; Glue Spark jobs are designed for distributed processing. Option B (repartitioning) can improve performance but does not add memory; it may help after resource allocation is adequate. Option C is invalid because AWS Glue does not support Java jobs; it supports Python and Scala.

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.

Question 3

A company is designing a data lake on AWS using Amazon S3. The data includes sensitive customer information that must be encrypted at rest. The company requires that encryption keys be managed by AWS, but the keys must be rotated automatically every year. Which TWO options meet these requirements? (Choose TWO.)

Accepted Answer

Use Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3).. SSE-S3 is correct because it provides server-side encryption where AWS manages the encryption keys entirely, including automatic annual rotation. This meets the requirement of encryption at rest with AWS-managed keys and automatic yearly rotation without any additional configuration.

Answer

Use client-side encryption with an AWS KMS key.

Answer

Use Server-Side Encryption with Customer-Provided Keys (SSE-C).

Answer

Use Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) with manual key rotation.

Question 4

A data engineer uses AWS Database Migration Service (DMS) to migrate an on-premises Oracle database to Amazon Aurora MySQL. The migration is successful, but the engineer notices that the target Aurora cluster has a higher CPU utilization than expected during the full load phase. What is the MOST likely cause?

Accepted Answer

The DMS task is configured to use multiple parallel threads to load data, overwhelming the target instance.. During full load, DMS uses multiple parallel threads (by default up to 4 or more) to maximize throughput, which can overwhelm the target Aurora cluster's CPU. Option A is incorrect: 'Full LOB mode' affects how large objects are handled and may increase latency, but it is not the primary cause of high CPU utilization. Option B is incorrect: DMS performs data validation after the full load phase, not during it. Option C is incorrect: DMS reads from the source (on-premises Oracle), not from an Aurora read replica; moreover, read replicas are not used for writing.

Answer

The DMS task has LOB mode set to 'Full LOB mode', causing additional processing.

Answer

DMS is performing data validation during the full load phase.

Answer

DMS is reading from an Amazon Aurora read replica instead of the primary instance.

Question 5

A company runs an Amazon Redshift cluster for data warehousing. The data engineering team notices that the 'Amazon Redshift Data API' is timing out when executing long-running queries. The queries typically take more than 10 minutes to complete. The team wants to ensure that the queries can complete without timeout and that the results are retrievable. Which TWO steps should the team take? (Choose TWO.)

Accepted Answer

Use the 'GetStatementResult' operation to retrieve results after the query completes.. Options C and E are correct. The Amazon Redshift Data API has a default timeout of 10 minutes for a single API call. To handle queries that take longer, you can run the query asynchronously by using the StatementName parameter (option E). This allows the query to continue running even if the initial API call times out, and you can poll for completion using the DescribeStatement operation. Once the query completes, you can retrieve the results using the GetStatementResult operation (option C). Option A is incorrect because QueryExecutionTimeout is not a valid parameter for the Data API. Option B is incorrect because increasing the timeout parameter in the Redshift cluster configuration does not affect the Data API timeout. Option D is incorrect because there is no max_execution_time parameter in Redshift; the relevant parameter is statement_timeout, which controls how long a query can run before being canceled, but it does not address the Data API timeout.

Answer

Set the 'QueryExecutionTimeout' parameter in the Data API call to 30 minutes.

Answer

Increase the 'timeout' parameter in the Redshift cluster configuration.

Answer

Set the 'max_execution_time' parameter in the Redshift parameter group to 30 minutes.

Question 6

A data engineer is designing a disaster recovery plan for an Amazon RDS for PostgreSQL database. The database is 500 GB and has a multi-AZ deployment. The recovery point objective (RPO) is 5 minutes, and the recovery time objective (RTO) is 2 hours. Which THREE actions should the engineer take to meet these objectives?

Accepted Answer

Enable Multi-AZ deployment for automatic failover.. Multi-AZ deployment provides automatic failover to a standby in another Availability Zone, meeting the RTO of 2 hours. Option B is correct because automated backups enable point-in-time recovery within the retention period, supporting the RPO of 5 minutes (default backup retention is 1 day, which is sufficient). Option E is correct because a cross-region read replica can be promoted to a standalone database in another region for faster disaster recovery if the primary region fails. Option C is wrong because manual snapshots exported to S3 are for long-term archival and not fast enough for a 2-hour RTO. Option D is wrong because disabling automated backups would prevent point-in-time recovery and violate the RPO of 5 minutes.

Answer

Take daily manual snapshots and export them to Amazon S3.

Answer

Disable automatic backups to reduce storage costs.

Question 7

A data engineer needs to monitor the performance of an Amazon Redshift cluster. Which Amazon CloudWatch metric should the engineer monitor to detect disk space issues?

Accepted Answer

PercentageDiskSpace. Option C. PercentageDiskSpace is a direct CloudWatch metric that tracks the percentage of disk space used on the Redshift cluster, making it ideal for detecting disk space issues. ReadIOPS and WriteIOPS measure I/O operations per second and are not indicators of disk space usage. NetworkThroughput measures network traffic, and CPUUtilization measures compute usage; neither relates to disk space.

Answer

ReadIOPS

Answer

WriteIOPS

Answer

NetworkThroughput

Answer

CPUUtilization

Question 8

A data engineer needs to ensure that an S3 bucket is not publicly accessible. Which S3 block public access setting should be applied to achieve this?

Accepted Answer

BlockPublicPolicy. (BlockPublicPolicy) prevents any public bucket policies from being applied, which is the primary way to grant public access to an S3 bucket. With public ACLs disabled by default for new buckets, blocking public policies effectively ensures the bucket is not publicly accessible. Option A (BlockPublicAcls both new and existing) only blocks public ACLs, leaving the bucket vulnerable to public policies, so it is insufficient by itself.

Answer

BlockPublicAcls (both new and existing)

Answer

IgnorePublicAcls

Answer

BlockPublicAcls (new ACLs)

Question 9

Refer to the exhibit. An IAM policy includes this statement. What access does it grant?

Accepted Answer

It allows GetObject access to the bucket only from IP addresses in 10.0.0.0/8. The policy statement allows the s3:GetObject action on the specified bucket, with a condition that the request must originate from an IP address within the 10.0.0.0/8 range. Therefore, it grants GetObject access only from IP addresses in that range. Option A is incorrect because the policy allows, not denies. Option B is incorrect because the condition is based on IP address, not VPC. Option C is incorrect because the action is GetObject, not PutObject. Option D correctly describes the access granted.

Answer

It denies GetObject access to the bucket from IP addresses in 10.0.0.0/8

Answer

It allows GetObject access to the bucket only from a specific VPC

Answer

It allows PutObject access to the bucket from any IP address

Question 10

A data engineer needs to migrate an on-premises Apache Hadoop cluster to AWS. The cluster stores data in HDFS and runs MapReduce jobs. The company wants to minimize operational overhead and leverage serverless technologies where possible. Which AWS service should the data engineer use to replace HDFS storage?

Accepted Answer

Amazon S3. Amazon S3 is the correct replacement for HDFS because it provides highly durable, scalable, and serverless object storage that can be used as the primary storage layer for Amazon EMR. Unlike HDFS, S3 decouples storage from compute, eliminating the need to manage cluster storage and allowing jobs to run on ephemeral clusters, which minimizes operational overhead. S3 integrates with EMR via the EMR File System (EMRFS), enabling MapReduce jobs to read/write data directly from S3 as if it were HDFS.

Answer

Amazon EBS

Answer

Amazon EMR

Answer

Amazon Redshift