DEA-C01 Practice Exam 65 — 20 Questions

Question 1

Refer to the exhibit. A data engineer has an IAM policy attached to an IAM role used by an AWS Glue job. The Glue job reads from S3 bucket 'example-bucket' and writes to an S3 bucket 'output-bucket'. The job fails with an 'Access Denied' error when writing to 'output-bucket'. What is the MOST likely cause?

Accepted Answer

The policy does not allow s3:PutObject on 'output-bucket'.. Option B is correct. The policy only allows s3:PutObject on 'example-bucket/*', not on 'output-bucket/*'. The job needs permission on the output bucket. Option A is incorrect because s3:PutObject is allowed on example-bucket, but not on output-bucket. Option C is incorrect because there is no condition that restricts PutObject to example-bucket. Option D is incorrect because the policy allows s3:GetObject on example-bucket, which is for reading.

Answer

The policy does not allow s3:PutObject on any bucket.

Answer

The policy does not allow s3:GetObject on 'output-bucket'.

Answer

The policy has a condition that restricts s3:PutObject to 'example-bucket'.

Question 2

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 3

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application is experiencing high latency and checkpoint failures. Which THREE actions should the data engineer take to improve performance and reliability? (Choose three.)

Accepted Answer

Increase the parallelism of the Flink application. Options A, C, and E are correct. Option A: Increasing parallelism improves throughput. Option C: Increasing checkpoint interval reduces checkpoint failures. Option E: Using event time helps with out-of-order data. Option B is wrong because decreasing parallelism reduces throughput. Option D is wrong because disabling checkpointing hurts reliability.

Answer

Decrease the parallelism to reduce resource contention

Answer

Disable checkpointing to avoid checkpoint failures

Question 4

A data engineer needs to schedule a daily ETL job that runs on Amazon EMR. The job should be triggered automatically and send an email on failure. Which AWS service should the engineer use to orchestrate the job?

Accepted Answer

AWS Step Functions. Option A is correct because AWS Step Functions can orchestrate EMR steps and integrate with Amazon SNS for notifications. Option B is wrong because Amazon CloudWatch Events can trigger Lambda but not directly orchestrate EMR steps. Option C is wrong because Amazon SQS is a message queue. Option D is wrong because Amazon EventBridge can trigger but not orchestrate complex workflows.

Answer

Amazon EventBridge

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon CloudWatch Events

Question 5

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an S3 bucket. The application is consistently running out of memory and failing. The operator has already increased the Parallelism and TaskManager memory. What is the next BEST step to troubleshoot?

Accepted Answer

Enable Apache Flink metrics in Amazon CloudWatch to monitor heap and checkpoint details. Option D is correct because enabling Apache Flink metrics on CloudWatch allows monitoring of heap usage, checkpoint sizes, and backpressure, which can diagnose the memory issue. Option A adds latency but doesn't diagnose memory. Option B changes processing semantics but doesn't address memory. Option C is unrelated.

Answer

Change the processing mode from exactly-once to at-least-once

Answer

Reduce the number of shards in the source stream

Answer

Increase the buffer timeout for the S3 sink

Question 6

A data pipeline uses AWS Glue ETL jobs to process data from Amazon RDS for MySQL to Amazon S3. Recently, the jobs have been failing with the error 'Communications link failure' during the connection phase. The RDS instance is in a private subnet, and the Glue job uses a VPC endpoint for S3. What is the most likely cause?

Accepted Answer

The Glue job does not have a network path to the RDS instance because it is not attached to the same VPC subnet.. Option B is correct because the Glue job needs a VPC endpoint for S3 to access S3, but for RDS, a VPC endpoint is not sufficient; the job must have network connectivity to the RDS subnet, typically via an ENI in the same VPC. Option A is wrong because KMS permissions would cause access denied, not connection failure. Option C is wrong because connection pooling is not relevant. Option D is wrong because the error is 'Communications link failure', not authentication.

Answer

The RDS database has reached the maximum number of connections.

Answer

The Glue job does not have IAM permissions to decrypt the RDS database using AWS KMS.

Answer

The JDBC driver used by Glue is incompatible with the MySQL version.

Question 7

A data pipeline uses Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The delivery stream is configured with a buffer size of 5 MB and a buffer interval of 60 seconds. The team notices that the S3 objects are much smaller than 5 MB. What is the most likely explanation?

Accepted Answer

The incoming data volume is low, so the 60-second buffer interval triggers delivery before the 5 MB buffer is filled.. Option A is correct because if the incoming data rate is low, the buffer interval (60 seconds) expires before the buffer size (5 MB) is reached, causing small objects. Option B is wrong because compression would reduce size but not cause small objects. Option C is wrong because S3 events are not related. Option D is wrong because S3 lifecycle would delete objects, not create small ones.

Answer

The S3 bucket has event notifications that split the objects.

Answer

The S3 bucket has a lifecycle policy that transitions objects to Glacier.

Answer

The delivery stream is using GZIP compression, which reduces the object size.

Question 8

A data engineer needs to implement a data pipeline that ingests data from an on-premises database using AWS DMS and loads it into Amazon S3 in Parquet format. The data should be encrypted at rest in S3 using a customer-managed KMS key. Which combination of actions should the engineer take? (Choose the correct course of action.)

Accepted Answer

Configure the DMS task to write to S3 in Parquet format, and specify the KMS key ID in the S3 endpoint settings.. Option D is correct because AWS DMS can write directly to S3 in Parquet format, and you can specify a KMS key for server-side encryption. Option A is incorrect because DMS does not support writing to S3 in JSON format without conversion, and encryption is handled by DMS settings. Option B is incorrect because DMS can write Parquet directly without an intermediate EC2 instance. Option C is incorrect because KMS encryption is set at the DMS task level, not via a bucket policy.

Answer

Set up an EC2 instance to run a script that reads from the source and writes Parquet to S3 with KMS encryption.

Answer

Use DMS to write JSON to S3, then use an AWS Glue job to convert to Parquet and enable KMS encryption on the Glue job.

Answer

Configure the S3 bucket policy to require KMS encryption for all objects, and use DMS with default settings.

Question 9

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. The migration is successful, but the ongoing replication task is experiencing high latency. Which configuration change is most likely to reduce latency?

Accepted Answer

Increase the size of the DMS replication instance.. Option B is correct because increasing the DMS replication instance size provides more CPU and memory, which can process changes faster. Option A (S3 as target) is not applicable as the target is Aurora. Option C (CDC from binary logs) is for MySQL source. Option D (decrease batch size) would likely increase latency.

Answer

Decrease the task's batch size and batch apply timeout.

Answer

Change the target endpoint to Amazon S3.

Answer

Enable Change Data Capture (CDC) from binary logs.

Question 10

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 11

A data pipeline using Amazon Kinesis Data Streams is experiencing high consumer lag. The stream has 10 shards. The consumer is an AWS Lambda function that processes each record and writes to Amazon DynamoDB. What is the MOST likely cause of the lag?

Accepted Answer

The Lambda function's reserved concurrency is set too low. Option C is correct because if the Lambda function's concurrency limit is reached, it cannot process all shards simultaneously, causing lag. Option A is wrong because increasing shards would increase parallelism but is not the root cause. Option B is wrong because Lambda can process from multiple shards. Option D is wrong because DynamoDB write capacity could be a bottleneck but is less likely than Lambda concurrency.

Answer

The DynamoDB table's write capacity is throttling writes

Answer

The number of shards is insufficient for the data volume

Answer

The Lambda function is not authorized to read from Kinesis

Question 12

A company is using Amazon Redshift for its data warehouse. A data engineer notices that COPY commands from S3 are failing intermittently with 'S3ServiceException: Access Denied'. The IAM role used by Redshift has the correct permissions. What is the MOST likely cause?

Accepted Answer

The S3 bucket policy denies access to the Redshift cluster's IP addresses.. Option D is correct because S3 bucket policies may deny access even if the role allows it. Option A is wrong because the role is already attached. Option B is wrong because encryption would cause different errors. Option C is wrong because if the role exists, it should work; the issue is likely external.

Answer

The IAM role is not attached to the Redshift cluster.

Answer

The S3 bucket uses SSE-KMS encryption and the role lacks kms:Decrypt.

Answer

The IAM role name contains a typo in the COPY command.

Question 13

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration completes successfully, but the target database has inconsistent data. What should the team do to ensure data consistency?

Accepted Answer

Configure the DMS task to use 'Full LOB mode' with parallel threads and enable 'BatchApply'.. Option C is correct because using LOB mode and parallel threads improves consistency and performance. Option A is wrong because full LOB mode can be slow but not cause inconsistency. Option B is wrong because limited LOB mode truncates data. Option D is wrong because task restart is not a solution for inconsistency.

Answer

Use 'Limited LOB mode' and set the maximum LOB size to a higher value.

Answer

Enable 'Full LOB mode' in the DMS task settings.

Answer

Restart the DMS task after truncating the target tables.

Question 14

A data engineer is monitoring an Amazon Redshift cluster and notices that some queries are experiencing high disk usage and slow performance. The engineer wants to identify the queries that are causing the most disk spills to temporary files. Which system table should the engineer query to get this information?

Accepted Answer

SVL_QUERY_SUMMARY. Option D is correct because the SVL_QUERY_SUMMARY system view provides information about disk spills for each query step, including the number of bytes spilled to disk. Option A is incorrect because STL_SCAN is for table scans, not spills. Option B is incorrect because STV_TBL_PERM shows permanent table storage, not temporary spills. Option C is incorrect because SYS_QUERY_DETAIL provides general query details but not spill information.

Answer

SYS_QUERY_DETAIL

Answer

STL_SCAN

Answer

STV_TBL_PERM

Question 15

A company runs a production Amazon Redshift cluster. The data engineering team notices that queries are running slowly during peak hours. The cluster's CPU utilization is consistently above 80%. Which action should the engineer take to improve query performance?

Accepted Answer

Perform an elastic resize to add more nodes.. Option C is correct because elastic resize allows adding nodes without downtime, addressing CPU bottleneck. Option A is incorrect because concurrency scaling only helps with many concurrent queries, not CPU. Option B is incorrect because distributing data on a different key may not reduce CPU. Option D is incorrect because spectrum offloads to S3 but does not reduce cluster CPU usage.

Answer

Move some tables to Amazon Redshift Spectrum.

Answer

Re-distribute the tables using a different distribution key.

Answer

Enable concurrency scaling.

Question 16

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 17

Refer to the exhibit. An IAM policy is attached to a user who needs to read objects from the 'example-bucket' S3 bucket. The user reports being unable to read any object under the 'confidential/' prefix. What is the reason for this access issue?

Accepted Answer

The explicit deny statement overrides the allow statement. Option B is correct because an explicit deny overrides any allow, even if the allow is more general. The policy allows all GetObject but denies GetObject for the confidential prefix. Option A is wrong because the order of statements does not matter; explicit deny always wins. Option C is wrong because there is no explicit allow for confidential; the deny applies. Option D is wrong because the resource ARN is correct.

Answer

The allow statement is evaluated before the deny statement

Answer

The deny statement is missing an explicit allow for the confidential prefix

Answer

The resource ARN in the deny statement is incorrect

Question 18

A company uses Amazon RDS for MySQL as a source for AWS DMS. The replication tasks are failing due to large transactions on the source. The team wants to reduce the impact of large transactions on DMS. Which THREE actions should the team take?

Accepted Answer

Increase the size of the source RDS instance and enable binary logging with ROW format.. Options A, C, and D are correct. BatchApply reduces apply time, tuning transaction size reduces memory pressure, and increasing the TLog size helps capture changes. Option B is wrong because parallel threads on source may increase CPU. Option E is wrong because switching to full load only is not a replication solution.

Answer

Increase the number of parallel threads on the source.

Answer

Use the 'full load only' migration type.

Question 19

A data engineer is troubleshooting a data pipeline that uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The engineer notices that the S3 bucket contains many small files (less than 1 MB). This is causing performance issues in downstream processing. What is the BEST way to reduce the number of small files?

Accepted Answer

Increase the buffer size to at least 128 MB in the Firehose delivery stream configuration.. Option C is correct because increasing the buffer size (e.g., to 128 MB) causes Firehose to deliver fewer, larger files. Option A is incorrect because reducing the buffer interval would create more small files. Option B is incorrect because changing the compression algorithm does not affect file size directly; it reduces storage size but not the number of files. Option D is incorrect because using a Lambda transformation does not inherently change buffering behavior.

Answer

Use an AWS Lambda function to transform the data before delivery.

Answer

Change the compression format from GZIP to Snappy.

Answer

Decrease the buffer interval in the Firehose delivery stream configuration.

Question 20

A data pipeline uses AWS DMS to replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The pipeline has been running successfully for months, but recently the DMS task status shows 'failed' with the error: 'The source database is running out of archive log space.' Which action should the engineer take to prevent this error?

Accepted Answer

Increase the amount of archive log space or reduce the log retention period on the source Oracle database.. Option D is correct because increasing the archive log retention on the source ensures DMS can read changes before logs are purged. Option A is incorrect because DMS uses CDC logs; adding more memory does not fix log space. Option B is incorrect because DMS tasks can only use one S3 bucket. Option C is incorrect because AWS does not manage on-premises database logs.

Answer

Configure multiple target S3 buckets to distribute the load.

Answer

Enable automatic log archiving on the DMS replication instance.

Answer

Increase the memory allocation for the DMS replication instance.

DEA-C01 Practice Exam 65 — 20 Questions

Exhibit

DEA-C01 Practice Exam 65 — 20 Questions

Exhibit