DEA-C01 Data Operations and Support Practice Test 17 — 15 Questions

Question 1

A data engineer has this IAM policy attached to their user. They are trying to create an Amazon EMR cluster with a custom service role 'EMR_CustomRole'. What will happen?

Accepted Answer

The cluster creation will fail with an 'Access Denied' error for iam:PassRole.. Option B is correct because the policy only allows iam:PassRole for the specific role 'EMR_DefaultRole', not for 'EMR_CustomRole'. The elasticmapreduce:* action allows creating clusters, but the PassRole will fail. Option A (full success) is incorrect. Option C (EMR not allowed) is false. Option D (PassRole not needed) is false.

Answer

The cluster creation will fail because elasticmapreduce:* is too broad.

Answer

The cluster creation will succeed because elasticmapreduce:* is allowed.

Answer

The cluster creation will succeed because PassRole is not required for EMR.

Question 2

A company uses Amazon Athena to query data in an S3 bucket. A data engineer notices that a query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Error opening Hive split s3://bucket/path/file.parquet (Path does not exist)'. However, the file exists in S3. What is the most likely cause?

Accepted Answer

Another process deleted the file after Athena listed the files but before reading.. Option C is correct because concurrent delete operations can cause a split to be read after deletion. Option A is incorrect because the error mentions the file path, not table metadata. Option B is incorrect because ACLs would cause permission errors. Option D is incorrect because S3 is strongly consistent for new objects but deletions can race.

Answer

The file was uploaded using S3 multipart upload and is incomplete.

Answer

The table's metadata in the Glue Data Catalog is outdated.

Answer

The S3 bucket has a bucket policy that denies access to the Athena principal.

Question 3

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 4

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. The consumer application is falling behind and the iterator age is increasing. Which action would most effectively improve throughput?

Accepted Answer

Increase the number of shards in the stream. Option C is correct because increasing shards increases parallelism. Option A is wrong because it reduces batch size. Option B is wrong because it doesn't help throughput. Option D is wrong because it adds latency.

Answer

Switch from Kinesis Data Streams to Kinesis Data Firehose

Answer

Decrease the batch size in the consumer

Answer

Enable enhanced fan-out for the consumer

Question 5

A data engineer is designing a disaster recovery strategy for an Amazon Redshift data warehouse. The RPO (Recovery Point Objective) is 1 hour, and the RTO (Recovery Time Objective) is 2 hours. Which approach meets these requirements with the least operational overhead?

Accepted Answer

Configure automated cross-region snapshot copy to another region.. Option B is correct because automated snapshots to S3 with cross-region copy provide up-to-the-hour recovery and fast restore. Option A is incorrect because automated snapshots are default but not cross-region. Option C and D have higher overhead and may not meet RTO.

Answer

Set up a secondary Redshift cluster in another region and use AWS DMS for continuous replication.

Answer

Export the data to S3 daily using UNLOAD and copy to another region.

Answer

Enable automated snapshots with a retention period of 1 day.

Question 6

A data engineer notices that an Amazon Athena query on a partitioned table in S3 scans more data than expected. The table is partitioned by year, month, day. The query includes a WHERE clause on a non-partition column but also filters on day='2023-01-01'. What is the most likely cause of the excessive data scan?

Accepted Answer

The partition column data type in the table definition does not match the actual partition folder names. Option D is correct because mismatched data types cause partition pruning to fail. Option A is wrong because partition pruning works with proper types. Option B is wrong because it would not affect scan size. Option C is wrong because sorting is irrelevant.

Answer

The data is stored in JSON format instead of Parquet

Answer

The table is not partitioned by the column used in the WHERE clause

Answer

The data is not sorted within partitions

Question 7

A data engineer is troubleshooting a slow Amazon Redshift query. The query plan shows a large number of 'DS_DIST_ALL_INNER' and 'DS_BCAST_INNER' operations. Which TWO actions would likely improve query performance?

Accepted Answer

Change the distribution style of large tables to KEY on the join column.. Option A is correct because using DISTSTYLE KEY on join columns can reduce data redistribution. Option D is correct because increasing the number of slices distributes data across more compute nodes. Option B is incorrect because SORTKEY helps with range restriction, not joins. Option C is incorrect because DISTSTYLE ALL on both tables would broadcast both, which is inefficient. Option E is incorrect because dropping and recreating tables is disruptive and may not help.

Answer

Set DISTSTYLE to ALL for both tables.

Answer

Define SORTKEYs on the join columns.

Answer

Drop and recreate the tables with the same DDL.

Question 8

A data engineer needs to set up a data pipeline that ingests CSV files from an S3 bucket, transforms them using AWS Glue, and loads the results into Amazon Redshift. The pipeline must handle schema evolution and data quality checks. Which combination of services is most appropriate?

Accepted Answer

Use AWS Glue ETL jobs with Glue DataBrew for data quality and write to Redshift. Option B is correct because Glue handles schema evolution and Deequ provides data quality checks. Option A is wrong because Lambda is not ideal for large transforms. Option C is wrong because Athena cannot write to Redshift. Option D is wrong because Kinesis is for streaming.

Answer

Use S3 Events to trigger an AWS Lambda function that writes directly to Redshift

Answer

Use Amazon Athena to query data in S3 and insert results into Redshift via CTAS

Answer

Use Amazon Kinesis Data Firehose to transform and load data into Redshift

Question 9

A company runs an Amazon EMR cluster processing data from S3. The data engineer notices that the cluster's task nodes are underutilized while core nodes are fully utilized. Which TWO steps should the engineer take to improve resource utilization?

Accepted Answer

Increase the number of core nodes.. Option A is correct because increasing the number of core nodes adds more capacity for processing. Option D is correct because enabling task nodes with spot instances can offload work from core nodes. Option B is incorrect because instance store is temporary and not suitable for HDFS. Option C is incorrect because consolidating tasks may not help. Option E is incorrect because reducing core nodes would worsen utilization.

Answer

Consolidate multiple small tasks into larger tasks.

Answer

Reduce the number of core nodes and increase the number of task nodes.

Answer

Move HDFS data from EBS to instance store volumes.

Question 10

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 11

A data engineer is setting up a data pipeline to ingest streaming data from an IoT fleet. The data must be processed in near real-time and stored in Amazon S3 for analytics. Which THREE AWS services should the engineer consider using?

Accepted Answer

Amazon Kinesis Data Firehose. Option A is correct because Kinesis Data Streams ingests streaming data. Option C is correct because Kinesis Data Firehose can deliver data to S3. Option D is correct because Lambda can process records in near real-time. Option B is incorrect because Glue is batch-oriented. Option E is incorrect because EMR is for big data processing, not streaming ingestion.

Answer

Amazon EMR

Answer

AWS Glue

Question 12

A data engineer is monitoring an Amazon EMR cluster and notices that one core node is running out of disk space. The cluster is running a Spark job that processes large Parquet files. What should the engineer do to prevent the issue?

Accepted Answer

Enable Snappy compression for intermediate data. Option C is correct because enabling Snappy compression for intermediate data reduces the volume of data written to disk during Spark shuffle operations, directly addressing the disk space issue on the core node. Snappy provides a good balance between compression ratio and speed, minimizing I/O overhead while conserving storage. This is a standard tuning practice in Amazon EMR for Spark jobs that process large Parquet files.

Answer

Terminate the core node and replace it with a larger instance type

Answer

Use Spark's in-memory processing to avoid writing intermediate data to disk

Answer

Increase the number of core nodes

Question 13

A data pipeline uses AWS Glue to process data from Amazon S3 and write results to Amazon Redshift. The pipeline fails intermittently with the error 'S3ServiceException: Access Denied'. The IAM role used by Glue has permissions to read from the S3 bucket. What is the most likely cause of this error?

Accepted Answer

The S3 bucket policy denies access to the Glue job's IAM role. Option C is correct because S3 bucket policies can explicitly deny access even if the IAM role allows it. Option A is wrong because S3 Transfer Acceleration is not related to access denied errors. Option B is wrong because S3 is not region-specific for the error. Option D is wrong because S3 Server Access Logging does not affect access permissions.

Answer

The S3 bucket is in a different AWS Region than the Glue job

Answer

S3 Server Access Logging is enabled and blocking requests

Answer

The S3 bucket has S3 Transfer Acceleration enabled

Question 14

A company uses AWS DMS to migrate an on-premises Oracle database to Amazon Aurora PostgreSQL. The migration is ongoing with continuous replication. The data engineer notices that the target Aurora database has a higher lag than expected. Which action would most likely reduce the lag?

Accepted Answer

Increase the number of parallel tasks in the DMS task settings. Option D is correct because increasing the number of parallel tasks improves throughput. Option A is wrong because turning off validation reduces reliability but may help slightly. Option B is wrong because it does not affect replication lag. Option C is wrong because batch-optimized apply is not for Aurora.

Answer

Increase the size of the S3 bucket used for staging

Answer

Enable Batch Optimized Apply on the DMS task

Answer

Disable validation of data on the target

Question 15

A data engineer is designing a data lake on Amazon S3. The data includes sensitive personally identifiable information (PII). Which combination of services would provide the most comprehensive data protection?

Accepted Answer

Enable S3 Block Public Access and use Amazon Macie to discover and classify PII. Option B is correct because S3 Block Public Access prevents exposure and Macie identifies sensitive data. Option A is wrong because KMS only encrypts. Option C is wrong because CloudWatch does not protect data. Option D is wrong because S3 Transfer Acceleration is for speed.

Answer

Use S3 Transfer Acceleration and enable versioning

Answer

Enable S3 server-side encryption with AWS KMS

Answer

Use Amazon CloudWatch Logs to monitor access and enable MFA Delete

DEA-C01 Data Operations and Support Practice Test 17 — 15 Questions

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DEA-C01 Data Operations and Support Practice Test 17 — 15 Questions

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