DEA-C01 Practice Exam 58 — 20 Questions

Question 1

A company runs a nightly Amazon EMR job that processes data from S3 and writes results back to S3. The job fails with 'OutOfMemoryError' in the reduce phase. The cluster currently uses 5 m5.xlarge instances. Which cost-effective change should the data engineer make?

Accepted Answer

Increase the number of reducers (mapreduce.reduce.tasks) and keep the same instance type.. Option A is correct because increasing the number of reducers distributes the memory load, and m5.xlarge instances are cost-effective. Option B is wrong because r5 instances are memory-optimized but more expensive. Option C is wrong because increasing instance count may not help if reducer memory is the issue. Option D is wrong because reducing input size may affect completeness.

Answer

Increase the number of core nodes to 10.

Answer

Reduce the input data size by filtering early in the job.

Answer

Switch to r5.xlarge instances for more memory per instance.

Question 2

Refer to the exhibit. A data engineer runs a Glue ETL job that reads from a CSV file and writes to a Redshift table. The job fails with the error shown. What is the most likely cause?

Accepted Answer

The source CSV file has fewer columns than the target table.. Option A is correct because the error indicates a column count mismatch. The source file has 10 columns but the target expects 12. Option B is wrong because the error is about column count, not data type. Option C is wrong because there is no mention of partition count. Option D is wrong because the error is about validation, not permissions.

Answer

The IAM role for the Glue job does not have permission to write to Redshift.

Answer

The target Redshift table has mismatched data types for some columns.

Answer

The Glue job is using an incorrect number of partitions for the source data.

Question 3

A data engineer is monitoring an Amazon EMR cluster and notices that the cluster is running out of disk space on the core nodes. Which action can be taken to resolve this issue?

Accepted Answer

Increase the EBS volume size attached to core nodes. Option A is correct because increasing the volume size of EBS attached to core nodes provides more storage. Option B is wrong because changing instance type affects memory/CPU, not disk. Option C is wrong because Spot Instances are cheaper but do not add disk. Option D is wrong because reducing data retention is not always feasible and may lose data.

Answer

Reduce the retention period of data stored on HDFS

Answer

Change the core node instance type to a compute-optimized type

Answer

Use Spot Instances for core nodes

Question 4

A data engineer is troubleshooting a slow-running Amazon Athena query on a large dataset stored in S3. The query scans many small files. Which TWO actions can improve query performance?

Accepted Answer

Concatenate small files into larger files. Option A is correct because compacting small files into larger ones reduces overhead. Option C is correct because partitioning limits data scanned. Option B is wrong because converting to Parquet helps but is not one of the two selected here (though it is also good). Option D is wrong because more files increase overhead. Option E is wrong because disabling encryption does not affect performance.

Answer

Increase the number of files to increase parallelism

Answer

Disable S3 server-side encryption

Answer

Convert files from CSV to JSON

Question 5

A data engineer is troubleshooting a nightly ETL job that reads data from an RDS MySQL instance and writes to an S3 bucket in Parquet format. The job runs on an EMR cluster and uses PySpark. Recently, the job started failing with 'OutOfMemoryError' in the executor logs. The data volume has grown 30% in the last month. Which is the MOST efficient solution to resolve this issue without changing the code?

Accepted Answer

Increase the executor memory and memoryOverhead in the Spark configuration.. Option A is correct because increasing executor memory and adjusting the spark.executor.memoryOverhead setting addresses memory limitations for large data processing. Option B is wrong because switching to Glue may not directly resolve memory issues and requires code changes. Option C is wrong because increasing cluster size adds cost and may not fix memory per executor. Option D is wrong because using a larger instance type is less flexible than tuning Spark configurations.

Answer

Change the RDS instance to a larger size to reduce load.

Answer

Switch the ETL job to use AWS Glue with a larger WorkerType.

Answer

Increase the number of core nodes in the EMR cluster.

Question 6

A data engineer is migrating a legacy data warehouse to Amazon Redshift. The engineer needs to load data from multiple sources efficiently. Which THREE services can be used to load data into Redshift? (Choose THREE.)

Accepted Answer

Use the COPY command to load from Amazon DynamoDB.. Options A, B, and D are correct: COPY from S3, COPY from DynamoDB, and Kinesis Data Firehose directly load into Redshift. Option C is wrong because S3 Transfer Acceleration is for uploading to S3, not loading into Redshift. Option E is wrong because AWS DMS can migrate data but is not a direct load service for Redshift; it can load into Redshift as a target.

Answer

Use AWS DMS to replicate data continuously.

Answer

Use S3 Transfer Acceleration.

Question 7

A company uses AWS Lake Formation to manage data lake permissions. A data analyst cannot query a table in Athena, although the table appears in the catalog. The analyst has IAM permissions to run Athena. What is the MOST likely cause?

Accepted Answer

The analyst lacks Lake Formation permissions on the table.. Option B is correct because Lake Formation permissions are separate from IAM; the analyst needs SELECT permissions granted via Lake Formation. Option A is wrong because S3 bucket policy may allow access but Lake Formation can override. Option C is wrong because the table appears, so catalog permissions are likely fine. Option D is wrong because Athena workgroup permissions are not related.

Answer

The Glue Data Catalog does not have the table registered.

Answer

The S3 bucket policy denies access to the analyst's IAM role.

Answer

The Athena workgroup is not configured with the correct output location.

Question 8

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda function that writes to an S3 bucket. Recently, the Lambda function started timing out. Which step should be taken to resolve this issue?

Accepted Answer

Increase the Lambda function timeout. Option D is correct because increasing the Lambda timeout gives the function more time to process records. Option A is wrong because increasing shards increases throughput but does not fix timeout. Option B is wrong because it may reduce concurrency but does not extend processing time. Option C is wrong because decreasing batch size reduces records per invocation but does not extend timeout.

Answer

Set the Lambda reserved concurrency to 1

Answer

Decrease the batch size in the event source mapping

Answer

Increase the number of shards in the Kinesis stream

Question 9

A data engineer notices that an AWS Glue ETL job is failing with an OutOfMemory error when processing a large dataset. The job uses a Standard worker type. Which action is MOST effective to resolve this issue without changing the job script?

Accepted Answer

Increase the number of DPUs per worker. Option B is correct because increasing the number of DPUs per worker provides more memory per task, addressing OutOfMemory errors directly. Option A is wrong because G.1X worker type has less memory than Standard. Option C is wrong because increasing the number of workers does not increase memory per worker. Option D is wrong because changing to G.2X may help but is not as direct as increasing DPUs per worker, and it may also increase cost unnecessarily.

Answer

Increase the number of workers

Answer

Switch to G.1X worker type

Answer

Change to G.2X worker type

Question 10

A data engineer is troubleshooting a step function that orchestrates ETL jobs. The state machine fails with 'State Machine Execution Throttled' error. What should the engineer do to resolve this?

Accepted Answer

Request a service quota increase for concurrent executions.. Option C is correct because the throttling is likely due to exceeding the default concurrent execution limit. Requesting a limit increase from AWS Support is the proper solution. Option A is wrong because reducing the number of steps does not affect concurrent execution limits. Option B is wrong because CloudWatch alarms only monitor, not resolve throttling. Option D is wrong because the error is about execution throttling, not API throttling, so adjusting API rate limits is not relevant.

Answer

Reduce the number of steps in the state machine.

Answer

Set up a CloudWatch alarm to detect throttling and retry.

Answer

Adjust the API rate limits in the state machine definition.

Question 11

A data engineer needs to monitor the number of records processed by an Amazon Kinesis Data Analytics application and trigger an alarm if the count drops below a threshold over 5 minutes. Which CloudWatch metric should be used?

Accepted Answer

millisBehindLatest (from KinesisDataAnalytics). Option C is correct because KinesisDataAnalytics publishes 'millisBehindLatest' for application progress. Option A is for Kinesis Streams. Option B is for Firehose. Option D is for Glue.

Answer

IncomingRecords (from Kinesis Streams)

Answer

DPUCount (from Glue)

Answer

IncomingBytes (from Kinesis Firehose)

Question 12

A company runs a data warehouse on Amazon Redshift. The data engineer notices that some queries are running slowly. Upon reviewing the system tables, the engineer finds that the 'svv_table_info' shows high 'unsorted' percentage for several large tables. What is the MOST effective action to improve query performance?

Accepted Answer

Run the VACUUM command on the tables.. Option B is correct because VACUUM sorts the data and reclaims space, improving query performance. Option A is wrong because ANALYZE updates statistics but does not sort. Option C is wrong because increasing the number of nodes may help but is not the most direct fix for unsorted data. Option D is wrong because changing distribution style would require recreating the table.

Answer

Run the ANALYZE command on the tables.

Answer

Change the distribution style of the tables to ALL.

Answer

Increase the number of nodes in the Redshift cluster.

Question 13

An AWS Glue job that performs data transformation on large Parquet files in Amazon S3 is taking a long time to complete. The job uses the default number of DPUs. Which change would most likely improve the job's performance?

Accepted Answer

Increase 'Max capacity' (number of DPUs) for the job.. Option B is correct because increasing DPUs adds parallelism and memory, speeding up processing. Option A is incorrect because Parquet is already efficient; CSV would be slower. Option C is incorrect because reducing partitions may cause OOM. Option D is incorrect because consolidating files into fewer, larger files can reduce parallelism.

Answer

Use 'coalesce' to reduce the number of output files.

Answer

Reduce the number of partitions in the source data.

Answer

Change the input format from Parquet to CSV.

Question 14

A company ingests IoT sensor data into an S3 bucket. Daily, a Lambda function reads new objects, processes them, and writes results to a DynamoDB table. Recently, the Lambda function started timing out after 15 minutes. The data volume has increased, and the function processes records one by one. Which solution would improve performance without significant cost increase?

Accepted Answer

Use S3 Batch Operations to invoke the Lambda function in parallel for each object.. Option B is correct because using S3 Batch Operations invokes a Lambda function for each object in parallel, handling large volumes efficiently. Option A is wrong because increasing Lambda timeout does not address the root cause of sequential processing. Option C is wrong because Glue jobs have a startup overhead and may cost more. Option D is wrong because increasing DynamoDB write capacity does not speed up the Lambda processing.

Answer

Replace Lambda with an AWS Glue ETL job.

Answer

Increase the Lambda function timeout to 30 minutes.

Answer

Increase the DynamoDB write capacity units.

Question 15

A data engineer needs to transfer 50 TB of data from an on-premises HDFS cluster to Amazon S3. The on-premises network has a 1 Gbps link to AWS. Which AWS service should be used to perform the transfer efficiently?

Accepted Answer

AWS DataSync. Option B is correct because AWS DataSync is designed for large-scale data transfers over the network. Option A is wrong because S3 Transfer Acceleration speeds up uploads but is not a transfer service. Option C is wrong because AWS Snowball is for offline data transfer, but the network link is sufficient. Option D is wrong because AWS Direct Connect is a network connection, not a data transfer service.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball Edge

Answer

AWS Direct Connect

Question 16

A data pipeline uses AWS Step Functions to orchestrate multiple Lambda functions for data transformation. The pipeline occasionally fails with a 'StateMachineExecutionLimitExceeded' error. What is the MOST likely cause?

Accepted Answer

The account has reached the maximum number of concurrent state machine executions.. Option C is correct because Step Functions has a default limit on concurrent executions (e.g., 1 million per account per region). Option A is wrong because Lambda concurrency limits would produce a different error. Option B is wrong because API Gateway is not involved. Option D is wrong because state machine definition does not affect execution limits.

Answer

The API Gateway endpoint used by Step Functions has a rate limit.

Answer

The Lambda functions have reached their concurrent execution limit.

Answer

The state machine definition has a syntax error causing infinite loops.

Question 17

A data engineer is designing a data pipeline that uses AWS Glue to process data from an RDS MySQL database. The pipeline must capture only incremental changes (inserts and updates) and run every hour. Which approach is most cost-effective and reliable?

Accepted Answer

Use Glue job bookmarks to track and process only new and updated records. Option B is correct because Glue job bookmarks track processed data and enable incremental processing without reprocessing full tables. Option A is wrong because CDC from DMS adds complexity and cost. Option C is wrong because full scans are inefficient and costly. Option D is wrong because querying by timestamp may miss updates if timestamps are not indexed or updated.

Answer

Use AWS DMS with change data capture (CDC) to replicate changes to S3

Answer

Add a timestamp column and query rows where timestamp > last run

Answer

Perform a full table scan each hour and compare with previous snapshot

Question 18

A company uses AWS DMS to migrate a 2 TB Oracle database to Amazon RDS for PostgreSQL. The migration completes successfully, but data validation shows some tables have missing rows. The task is configured for ongoing replication using change data capture (CDC). What is the MOST likely cause of the missing rows?

Accepted Answer

Source tables missing primary keys. Option C is correct because if a table lacks a primary key, DMS cannot uniquely identify rows for CDC, leading to missed changes. Option A is wrong because the endpoint connection is valid (migration completed). Option B is wrong because CDC captures changes from redo logs, not the source database directly. Option D is wrong because DMS supports large objects with proper configuration.

Answer

Source database archive log retention period too short

Answer

Large objects (LOBs) not supported by the target

Answer

Insufficient storage on the DMS replication instance

Question 19

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. Option A is correct because data validation at ingestion catches issues early. Option C is correct because schema enforcement prevents data type mismatches. Option E is correct because monitoring with CloudWatch allows proactive detection of failures. Option B is wrong because manual sampling is not scalable. Option D is wrong because compressing data does not ensure quality.

Answer

Compress data files to reduce storage costs

Answer

Use manual sampling to check data quality periodically

Question 20

A company uses Amazon Athena to query data in S3. Recently, queries have become slow. The data is stored as CSV files in a partitioned table. What is the most effective way to improve query performance?

Accepted Answer

Convert the data to Parquet format and optimize partitioning.. Option C is correct because converting to Parquet and partitioning improves compression, columnar scanning, and partition pruning. Option A is wrong because increasing file size alone may not help; large CSV files still require full scans. Option B is wrong because converting to JSON would likely worsen performance. Option D is wrong because more nodes only help with distributed processing, but Athena manages resources automatically.

Answer

Increase the number of nodes in the Athena query engine.

Answer

Convert the data to JSON format.

Answer

Increase the size of the CSV files to reduce the number of files.