DEA-C01 Data Operations and Support Practice Test 24 — 15 Questions

Question 1

A data engineer is troubleshooting an AWS Glue ETL job that fails with a 'java.lang.OutOfMemoryError: Java heap space' error. The job processes a 50 GB Parquet file from an S3 bucket. The job uses a G.1X DPU (16 GB memory) and default parameters. Which action should the engineer take to resolve the issue?

Accepted Answer

Change the worker type to G.2X (32 GB memory).. Option B is correct because increasing the number of DPUs (e.g., to G.2X with 32 GB) provides more memory per worker, directly addressing the heap space error. Option A is wrong because increasing worker count without increasing worker type may not help if each worker still has insufficient memory. Option C is wrong because increasing batch size could increase memory pressure. Option D is wrong because converting to JSON typically increases file size and memory usage.

Answer

Increase the number of workers from 2 to 4.

Answer

Increase the 'batch size' parameter in the DynamicFrame reader.

Answer

Convert the input data from Parquet to JSON format.

Question 2

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

The JDBC driver is missing the 'redshift' compatibility mode setting.. Option D is correct because Redshift compatibility mode in the JDBC driver is required for Glue to properly handle connections. Without it, the driver may close the connection unexpectedly. Option A is wrong because Redshift does not enforce SSL by default unless configured. Option B is wrong because the Glue job is in the same VPC and uses enhanced VPC routing, so the connection should work. Option C is wrong because Glue supports Redshift as a data source.

Answer

SSL is not enabled on the Redshift cluster.

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 3

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

Increase the shard count to 20.. Option A is correct because each shard supports 1 MB/s write capacity. With 10 shards, total write capacity is 10 MB/s, but the stream receives 5 MB/s, so write capacity is sufficient. However, read capacity is 2 MB/s per shard (total 20 MB/s), and reads are 10 MB/s, so reads are fine. The throttling may be due to uneven partition key distribution. Increasing shards to 20 provides 20 MB/s write capacity, solving the issue. Option B is wrong because increasing shards reduces read capacity per shard. Option C is wrong because enabling enhanced fan-out increases read cost but does not affect write limits. Option D is wrong because Kinesis Data Streams does not auto-scale; you must manually update shard count.

Answer

Decrease the shard count to 5.

Answer

Enable enhanced fan-out on the stream.

Answer

Configure auto-scaling on the stream.

Question 4

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.. Option B is correct because Lake Formation requires explicit grant of 'DESCRIBE' permission on the table for Athena to read metadata; SELECT alone is insufficient. Option A is wrong because the analyst can see the table, meaning DESCRIBE is granted at the catalog level. Option C is wrong because KMS permissions are already in place. Option D is wrong because Lake Formation is designed to work with Athena.

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 5

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.. Option C is correct because the error indicates that the task is trying to insert rows that already exist. Enabling 'BatchApplyEnabled' and setting 'TaskRecoveryTableEnabled' to false prevents duplicate handling errors during replication. Option A is wrong because truncating would lose data. Option B is wrong because disabling constraints may cause data integrity issues. 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 6

A data engineer is running an Amazon EMR cluster with Spark to process log files. The cluster uses instance fleets with m5.xlarge core nodes. The engineer observes that the Spark job is running slower than expected. CloudWatch metrics show that the cluster's CPU utilization is below 20% but memory utilization is near 90%. Which configuration change would most likely improve performance?

Accepted Answer

Use memory-optimized instances (r5.xlarge) for core nodes.. Option D is correct because high memory usage with low CPU indicates that the data does not fit in memory, causing spills to disk. Using a memory-optimized instance type (e.g., r5.xlarge) provides more memory per core. Option A is wrong because increasing core nodes adds more CPU but does not address memory per node. Option B is wrong because reducing nodes reduces total memory. Option C is wrong because the issue is memory, not shuffle partitions.

Answer

Increase the number of core nodes from 5 to 10.

Answer

Increase the number of Spark shuffle partitions.

Answer

Decrease the number of core nodes to reduce overhead.

Question 7

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.. Option A is correct because 'tossed' rows indicate data that was deleted or updated and is waiting for VACUUM. A high tossed count means wasted space. Running VACUUM reclaims that space. Option B is wrong because 'tossed' does not indicate sort key issues. Option C is wrong because 'tossed' is not about distribution. Option D is wrong because deep copy is a more drastic alternative, but VACUUM is the standard action.

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.

Question 8

A data engineer is troubleshooting an AWS Glue job that writes data to an Amazon S3 bucket in Parquet format. The job runs successfully but the output files are smaller than the configured 'groupFiles' size. The engineer has set 'groupFiles' to 'inPartition' and 'groupSize' to 1 GB. The input data is 10 GB in a single partition. What is the most likely reason for the small files?

Accepted Answer

The 'groupFiles' parameter only affects the input read phase, not the output write phase.. Option B is correct because 'groupFiles' only works when the input data is already small and needs to be coalesced. However, if the input is large and the job writes output, the output file size is determined by the number of Spark partitions, not grouping. The grouping feature only applies to reading input files. Option A is wrong because the setting is correct. Option C is wrong because grouping is a read-time feature, not write-time. Option D is wrong because grouping does not require repartitioning.

Answer

The 'groupFiles' parameter is deprecated in the current Glue version.

Answer

The 'groupFiles' parameter is misspelled or set incorrectly.

Answer

The engineer must also set 'repartition' to 1 to merge output files.

Question 9

A company uses AWS Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. Recently, the delivery stream has been failing with the error 'S3 bucket does not exist'. The S3 bucket exists and the Firehose IAM role has s3:PutObject permissions. What is the most likely cause?

Accepted Answer

The S3 bucket is in a different AWS Region than the Firehose stream.. Option D is correct because Firehose requires the bucket to be in the same region as the delivery stream. If the bucket is in a different region, Firehose cannot write to it. Option A is wrong because the bucket exists. Option B is wrong because the role has permissions. Option C is wrong because encryption is not related to bucket existence errors.

Answer

The S3 bucket name is misspelled in the Firehose configuration.

Answer

The S3 bucket has default encryption enabled.

Answer

The IAM role does not have s3:ListBucket permission.

Question 10

A data engineer is setting up an Amazon Redshift cluster for a new data warehouse. The engineer needs to ensure that the cluster can automatically recover from failures and maintain high availability. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Configure automated snapshots with a retention period of 1 day and enable cluster recreation from snapshots.. Option A (enable Multi-AZ) and Option D (automated snapshots with cluster recreation) are correct. Multi-AZ provides synchronous replication across AZs for high availability. Automated snapshots allow point-in-time recovery and cluster replacement in case of failure. Option B is wrong because concurrency scaling improves query performance, not availability. Option C is wrong because manual snapshots require manual intervention. Option E is wrong because workload management (WLM) does not affect availability.

Answer

Configure workload management (WLM) queues to prioritize critical queries.

Answer

Enable concurrency scaling.

Answer

Configure manual snapshots with cross-region copy.

Question 11

A company is using AWS Glue DataBrew to clean and transform data from an S3 bucket. The data contains personally identifiable information (PII). The company wants to mask the PII columns before making the dataset available to analysts. Which THREE actions can the engineer perform using DataBrew to mask PII? (Choose THREE.)

Accepted Answer

Apply a 'Column masking' transformation to replace values with 'XXX'.. Options A, C, and D are correct. DataBrew provides built-in transformations for masking: 'Column masking' replaces values with a fixed pattern, 'Column hashing' replaces with a hash, and 'Column delete' removes the column. Option B is wrong because 'Column encryption' is not a DataBrew transformation; encryption is typically done at rest or with KMS. Option E is wrong because there is no 'Column tokenization' transformation in DataBrew; tokenization would require a custom recipe step.

Answer

Apply a 'Column tokenization' transformation to replace values with tokens.

Answer

Apply a 'Column encryption' transformation to encrypt the column values.

Question 12

A data engineer is monitoring an Amazon Kinesis Data Stream used to ingest clickstream data. The engineer notices that the stream's 'WriteProvisionedThroughputExceeded' metric is frequently above zero. Which TWO actions could help mitigate this issue? (Choose TWO.)

Accepted Answer

Increase the number of shards in the stream.. Options A and D are correct. Increasing the number of shards increases write capacity. Implementing a random prefix for partition keys distributes writes more evenly across shards. Option B is wrong because decreasing shards reduces capacity. Option C is wrong because enabling enhanced fan-out increases read capacity, not write. Option E is wrong because reducing the retention period does not affect write throughput.

Answer

Reduce the data retention period to free up capacity.

Answer

Decrease the number of shards to reduce overhead.

Answer

Enable enhanced fan-out on the stream.

Question 13

A data engineer reviews the above error log from an AWS Glue ETL job. The job uses a G.1X worker type (16 GB memory). The job processes a 30 GB CSV file from S3. What should the engineer do to resolve the memory error?

Accepted Answer

Set 'spark.executor.memory' to 12g in the job parameters.. Option B is correct because the error indicates that the executor ran out of memory (10 GB used of 10 GB limit). Increasing the Spark executor memory to 12 GB (since G.1X has 16 GB total, leaving room for overhead) will prevent the container from being killed. Option A is wrong because increasing the number of workers does not increase per-executor memory. Option C is wrong because reducing the number of workers reduces total memory but does not fix per-executor limits. Option D is wrong because converting to Parquet reduces file size but does not change the memory limit per executor.

Answer

Convert the input file from CSV to Parquet format.

Answer

Decrease the number of workers to 1 to reduce memory overhead.

Answer

Increase the number of workers from 2 to 4.

Question 14

A data engineer is troubleshooting an AWS Glue ETL job that fails with an 'Access Denied' error when trying to write to an S3 bucket. The IAM role used by the job has the policy shown in the exhibit. The bucket 'my-bucket' uses S3 default encryption with AWS KMS. What is the most likely missing permission?

Accepted Answer

kms:GenerateDataKey and kms:Decrypt. Option D is correct because when S3 default encryption uses KMS, the IAM role must have kms:GenerateDataKey and kms:Decrypt permissions on the KMS key. The policy in the exhibit does not include any KMS actions. Option A is wrong because s3:PutObject is already granted. Option B is wrong because s3:GetObject is allowed for reading. Option C is wrong because s3:ListBucket is allowed for listing. Option E is wrong because Glue GetObject is not a valid action.

Answer

s3:GetObjectVersion

Answer

glue:GetObject

Answer

s3:ListBucketMultipartUploads

Answer

s3:PutObjectAcl

Question 15

A company runs a data pipeline that ingests streaming data from an IoT fleet into Amazon Kinesis Data Streams (KDS) with 50 shards. A Lambda function processes records from the stream and writes them to an Amazon DynamoDB table for real-time analytics. The Lambda function is configured with a batch size of 100 and a maximum batching window of 60 seconds. Recently, the company has been seeing an increasing number of 'WriteProvisionedThroughputExceededException' errors from DynamoDB, causing Lambda to retry and eventually send records to a dead-letter queue (DLQ). The DynamoDB table is provisioned with 5000 read capacity units (RCU) and 5000 write capacity units (WCU). The average item size is 1 KB. The KDS stream receives an average of 8000 records per second, each 2 KB in size. The Lambda function performs a simple transformation and writes each record individually to DynamoDB. The company wants to reduce the throttling errors without increasing the DynamoDB WCU provision. Which course of action is most likely to achieve this?

Accepted Answer

Modify the Lambda function to use DynamoDB BatchWriteItem to write records in batches of 25.. Option A is correct because writing records in batches using DynamoDB's BatchWriteItem API reduces the number of write requests, lowering the consumed WCU per request (since each batch consumes WCU for all items but with fewer API calls, reducing overhead). This can reduce throttling without increasing WCU. Option B is wrong because increasing Lambda batch size would cause more records to be processed per invocation, but if each record is still written individually, the number of write requests remains the same. Option C is wrong because increasing Lambda concurrency would increase the number of concurrent invocations, potentially increasing throttling. Option D is wrong because increasing Lambda timeout does not affect the rate of writes.

Answer

Increase the Lambda function's reserved concurrency to 1000.

Answer

Increase the Lambda function timeout to 5 minutes to allow more time for retries.

Answer

Increase the Lambda batch size to 500 and reduce the batching window to 30 seconds.