DEA-C01 Data Operations and Support Practice Test 10 — 15 Questions

Question 1

A data engineer is troubleshooting an AWS Step Functions workflow that calls a Lambda function to process data. The workflow sometimes fails with a 'StateMachineExecutionLimitExceeded' error. What is the MOST likely cause?

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

Accepted Answer

Number of concurrent executions exceeds the account limit. The error 'StateMachineExecutionLimitExceeded' indicates that the account's limit for concurrent Step Functions executions has been exceeded. Option A correctly identifies this as the most likely cause. Option B would result in an 'ExecutionTimedOut' error, not a limit error. Option C would cause a Lambda-specific error, such as 'MemorySize', not a Step Functions limit error. Option D would result in a Lambda throttling error, not a state machine execution limit error.

Answer

Execution time exceeds the maximum allowed duration

Answer

Lambda function memory limit exceeded

Answer

Lambda function concurrency limit reached

Question 2

A data engineer is monitoring an Amazon Redshift cluster using Amazon CloudWatch. The engineer notices that the 'WriteThroughput' metric is consistently below the provisioned IOPS for the cluster's EBS volumes. The query performance is slower than expected. Which action is MOST likely to improve write performance?

Accepted Answer

Upgrade to a larger node type with more CPU and memory.. The 'WriteThroughput' metric being consistently below the provisioned IOPS indicates that the EBS volumes are not the bottleneck; the bottleneck is more likely insufficient compute resources (CPU/memory). Upgrading to a larger node type (e.g., from DC2 to RA3 or a higher node size) increases CPU and memory, which can improve query processing and write performance. Option A is incorrect because reducing concurrent queries may help with contention, but if the underlying node lacks resources, it won't fully address the low throughput. Option C is incorrect because sort keys primarily optimize read performance (e.g., range-restricted scans), not write throughput. Option D is incorrect because increasing IOPS on EBS volumes will not help when the provisioned IOPS are already not being fully utilized.

Answer

Reduce the number of concurrent queries to the database.

Answer

Add sort keys to the tables to improve data distribution.

Answer

Increase the provisioned IOPS on the EBS volumes.

Question 3

A company stores sensitive data in Amazon S3. To meet compliance requirements, they need to ensure that any data older than 1 year is automatically moved to a lower-cost storage class. Which S3 feature should they use?

Accepted Answer

S3 Lifecycle policies. S3 Lifecycle policies allow you to define rules to automatically transition objects between storage classes based on age. Option B is correct. Option A is incorrect because S3 Replication is used to replicate objects across buckets, not to transition storage classes. Option C is incorrect because S3 Glacier is a storage class, not a feature that automates transitions. Option D is incorrect because S3 Intelligent-Tiering automatically moves data between access tiers based on usage, not on a fixed age threshold like 1 year.

Answer

S3 Replication

Answer

S3 Glacier

Answer

S3 Intelligent-Tiering

Question 4

A data engineer uses Amazon EMR to run a Spark job that reads from S3 and writes to HDFS on the cluster. The job fails with an 'OutOfMemoryError: Java heap space' error in the executors. Which parameter adjustment should be made to resolve this?

Accepted Answer

Increase spark.executor.memory. The 'OutOfMemoryError: Java heap space' in executors indicates that the executor memory is insufficient for the data being processed. Increasing spark.executor.memory allocates more heap space to each executor, directly addressing the issue. Option A (spark.default.parallelism) controls the number of tasks, not memory. Option B (spark.sql.shuffle.partitions) affects shuffle partitions but does not increase memory. Option D (spark.driver.memory) is for the driver, not executors.

Answer

Increase spark.default.parallelism

Answer

Increase spark.sql.shuffle.partitions

Answer

Increase spark.driver.memory

Question 5

A data engineer is designing a disaster recovery strategy for an Amazon RDS for PostgreSQL database that is used in a data pipeline. The database must have a Recovery Point Objective (RPO) of less than 1 minute and a Recovery Time Objective (RTO) of less than 5 minutes. Which TWO actions should the engineer take?

Accepted Answer

Enable automated backups with point-in-time recovery.. Options B and C are correct. Multi-AZ deployment with a standby instance enables automatic failover, typically achieving an RTO of under 1-2 minutes. Combined with automated backups and point-in-time recovery, which allow database restoration to any point within seconds (RPO of less than 1 minute), the requirements are met. Option A is incorrect because manual snapshots are not frequent enough to guarantee RPO under 1 minute and restoring from a snapshot takes longer than 5 minutes. Option D is incorrect because a read replica in a different Availability Zone is not designed for automatic failover; it is for read scaling and requires manual promotion. Option E is incorrect because Amazon Aurora Global Database is a different service; this question is about Amazon RDS for PostgreSQL, and cross-Region replication for RDS does not provide automatic failover and typically has higher RTO.

Answer

Take frequent manual snapshots and copy them to another Region.

Answer

Create a read replica in a different Availability Zone.

Answer

Use cross-Region replication with Amazon Aurora Global Database.

Question 6

A company runs a data lake on Amazon S3 with AWS Glue and Amazon Athena. The data engineer notices that queries are slow and scanning large amounts of data. Which THREE actions should the engineer take to optimize query performance and reduce costs?

Accepted Answer

Compress data files using gzip or snappy.. The correct actions to optimize query performance and reduce costs are C, D, and E. Compressing data (C) reduces the amount of data scanned, lowering costs and improving I/O. Partitioning (D) by frequently filtered columns (e.g., date, region) allows Athena to prune partitions, scanning only relevant data. Using columnar formats like Parquet or ORC (E) improves compression and enables column pruning, reducing scan size and improving performance. Option A (increasing query timeout) does not reduce data scanned or improve performance; it only allows queries to run longer before failing. Option B (increasing DPUs in a Glue job) is unrelated to Athena query performance; DPUs are for Glue ETL jobs, not Athena queries.

Answer

Increase the query timeout in Athena.

Answer

Increase the number of DPUs in the Glue job.

Question 7

A company uses AWS Lake Formation to manage access to data in S3. A data analyst reports being unable to query a table in Amazon Athena, receiving an 'Access Denied' error. The analyst has SELECT permission on the table in Lake Formation. What additional configuration is MOST likely causing the issue?

Accepted Answer

The IAM role used by Athena does not have S3 GetObject permission on the underlying data. In AWS Lake Formation, even when a user has SELECT permission on a table, the IAM role that Athena uses must have S3 GetObject permission on the underlying data files. Lake Formation manages permissions at the metadata level, but the actual data access is enforced by S3 bucket policies and IAM. Without GetObject permission, Athena fails with 'Access Denied'. Option A is incorrect because Athena typically has access to the Glue Data Catalog if the table is visible; the error is about data access. Option C is incorrect because SELECT permission usually includes DESCRIBE, and the error message is about access denied, not missing describe. Option D is incorrect because the table is registered with Lake Formation since the analyst has SELECT permission.

Answer

Athena does not have permission to access the Glue Data Catalog

Answer

The analyst does not have DESCRIBE permission

Answer

The table is not registered with Lake Formation

Question 8

A data engineer is troubleshooting an Amazon EMR cluster that has been running for several days. The cluster uses Amazon S3 as the data source and HDFS for intermediate storage. The engineer notices that some tasks fail with 'Java heap space' errors. Which TWO actions should the engineer take to resolve this issue?

Accepted Answer

Increase the maximum Java heap size for the task nodes (mapreduce.map.java.opts).. Options C and D are correct. Increasing the maximum Java heap size (mapreduce.map.java.opts) gives each task more memory to avoid heap space errors. Increasing YARN memory overhead (yarn.nodemanager.resource.memory-mb) allows containers to request more memory from YARN. Option A is incorrect because EMRFS consistent view addresses S3 consistency, not memory. Option B is incorrect because increasing the number of containers per node without increasing total memory reduces memory per container, worsening heap issues. Option E is incorrect because decreasing YARN container size reduces available memory, counterproductive for out-of-memory errors.

Answer

Enable EMRFS consistent view for S3.

Answer

Increase the number of containers per node.

Answer

Decrease the YARN container size.

Question 9

A company uses Amazon Redshift for data warehousing. They notice that query performance has degraded over time. Which maintenance operation should be performed to improve performance?

Accepted Answer

Run the VACUUM command. The VACUUM command re-sorts rows according to the sort key and reclaims disk space from deleted rows, which can improve query performance. Option B is incorrect because dropping and recreating the table is a heavy operation that requires redefining the table and reloading data; it is not a standard maintenance operation for performance. Option C is incorrect because Redshift uses sort keys and distribution keys instead of indexes; there is no REINDEX command in Redshift. Option D is incorrect because the ANALYZE command updates table statistics to help the query optimizer, but it does not physically reorganize the data; thus it may help with query planning but does not directly improve performance from data fragmentation.

Answer

Drop and recreate the table

Answer

Run the REINDEX command

Answer

Run the ANALYZE command

Question 10

Refer to the exhibit. An AWS Glue job is failing with 'AccessDenied' when trying to write to the 'data-lake-bucket' which is encrypted with an AWS KMS key. The IAM role used by the Glue job has the attached policy shown. What is the MOST likely cause of the failure?

Accepted Answer

The policy does not include kms:GenerateDataKey or kms:Encrypt permission.. The policy allows s3:PutObject but does not include kms:GenerateDataKey or kms:Encrypt permissions, which are required to write to an SSE-KMS encrypted S3 bucket. Option A is incorrect because ListBucket is allowed. Option B is incorrect because GetObject is allowed. Option C is incorrect because the KMS key ARN in the policy is correct; the issue is missing KMS actions.

Answer

The policy does not include s3:ListBucket permission.

Answer

The policy does not include s3:GetObject permission.

Answer

The KMS key ARN in the policy is incorrect.

Question 11

A company uses Amazon DynamoDB as the primary data store for a web application. The application experiences high read latency. Which TWO actions can improve read performance?

Accepted Answer

Enable DynamoDB Global Tables. (Enable DynamoDB Global Tables) is correct because Global Tables provide local read replicas in multiple AWS Regions, reducing read latency for users accessing from different geographic locations. Option C (Enable DynamoDB Accelerator (DAX)) is correct because DAX is an in-memory cache that significantly reduces read latency for frequently accessed items. Option A is incorrect because adding a Global Secondary Index (GSI) improves query flexibility but does not directly reduce read latency for primary key lookups. Option D is incorrect because DynamoDB Streams capture changes to the table for event-driven processing, not for reducing read latency. Option E is incorrect because increasing write capacity units only affects write throughput, not read performance.

Answer

Add a Global Secondary Index (GSI)

Answer

Enable DynamoDB Streams

Answer

Increase the write capacity units

Question 12

Refer to the exhibit. A data engineer is troubleshooting an AWS Lambda function that processes data from Amazon S3. The function is triggered by S3 events, but no logs appear in CloudWatch Logs. The engineer runs the AWS CLI command shown. What is the MOST likely reason for the missing logs?

Accepted Answer

The Lambda execution role does not have permissions to create log groups and write logs.. The CLI command output shows the log group exists but `storedBytes: 0`, meaning no logs have been written. The most common cause is that the Lambda execution role lacks the required permissions (`logs:CreateLogStream`, `logs:PutLogEvents`). Option B is incorrect because even if the function logged to a different group, logs for this group would still be written if permissions allowed. Option C is incorrect because the function could be invoked but unable to write logs. Option D is incorrect because a retention policy does not prevent logs from being written; it only determines how long they are kept.

Answer

The Lambda function is configured to log to a different log group.

Answer

The Lambda function is not being invoked by S3 events.

Answer

The log retention policy is set to 7 days, causing logs to expire immediately.

Question 13

A data engineer is designing a data pipeline that ingests data from multiple sources into Amazon S3, then processes it with AWS Glue and loads it into Amazon Redshift. Which THREE practices should be implemented to ensure data quality?

Accepted Answer

Implement data validation checks at the ingestion stage. Implementing data validation checks at the ingestion stage ensures that only valid data enters the pipeline, catching issues early. Option B is correct because AWS Glue DataBrew provides data profiling and schema enforcement capabilities that help maintain data quality by identifying anomalies and enforcing schemas. Option E is correct because setting up Amazon CloudWatch alarms allows proactive monitoring of pipeline failures and data anomalies, enabling timely responses. Option C is incorrect because compressing data files reduces storage costs but does not directly address data quality. Option D is incorrect because manual sampling is not scalable and cannot consistently ensure data quality in large-scale pipelines.

Answer

Compress data files to reduce storage costs

Answer

Use manual sampling to check data quality periodically

Question 14

Refer to the exhibit. A data engineer runs the command on an Amazon S3 bucket used for data lake storage. The engineer is concerned about accidental overwrites of objects. What does the output indicate?

Accepted Answer

Versioning is enabled, so previous versions of objects are preserved.. The Status 'Enabled' indicates versioning is turned on for the bucket, preserving previous versions. Option B is wrong because versioning does not automatically delete old versions; they are retained until explicitly deleted. Option C is wrong because versioning does not enable encryption; encryption is a separate setting. Option D is wrong because MFA Delete is not displayed in this status output; it is a different bucket property.

Answer

Old versions will be automatically deleted after a retention period.

Answer

Objects are encrypted at rest by default.

Answer

MFA Delete is disabled, meaning anyone can delete objects permanently.

Question 15

A company is using AWS Glue to process data stored in Amazon S3. The Glue job runs successfully but takes longer than expected. Which TWO actions can reduce the job runtime?

Accepted Answer

Increase the number of DPUs allocated to the job. Increasing DPUs (Data Processing Units) allocates more compute resources to the Glue job, allowing it to process data faster, thus reducing runtime. Option E is correct: Partitioning the input data in S3 allows Glue to use partition pruning, reading only the necessary partitions instead of scanning the entire dataset, which reduces I/O and processing time. Option A is incorrect: Disabling job bookmarking does not reduce runtime; it only affects how Glue tracks processed data and might cause reprocessing. Option C is incorrect: Reducing the number of workers would decrease parallelism, likely increasing runtime. Option D is incorrect: Changing from Spark to Python Shell would likely increase runtime because Python Shell is single-threaded and not designed for large-scale data processing compared to Spark.

Answer

Disable job bookmarking

Answer

Reduce the number of workers

Answer

Change the job type from Spark to Python shell