DEA-C01 Data Operations and Support Practice Test 6 — 15 Questions

Question 1

Refer to the exhibit. A CloudFormation template is used to create a DynamoDB table. After creation, a data engineer wants to restore the table to a point in time from 3 hours ago. Which action is required?

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

Accepted Answer

Use the AWS CLI or Console to initiate a point-in-time restore specifying the desired timestamp.. To restore a DynamoDB table to a point in time from 3 hours ago, you can use the AWS CLI or Console to initiate a point-in-time restore specifying the desired timestamp. Point-in-time recovery (PITR) must be enabled on the table, and it allows restoration to any second within the last 35 days.

Answer

Create a manual backup of the table first.

Answer

Enable AWS Backup to schedule automatic backups.

Answer

Ensure the table has at least one on-demand backup.

Question 2

A data engineer is troubleshooting a nightly ETL job that extracts data from an Amazon RDS MySQL instance and loads it into an Amazon S3 bucket in Parquet format. The job runs on an Amazon EMR cluster and has been failing with the error 'Access Denied' when writing to S3. The IAM role attached to the EMR cluster has permissions for S3 PutObject. What is the MOST likely cause?

Accepted Answer

The S3 bucket policy denies access to the EMR cluster's IAM role.. The error 'Access Denied' when writing to S3, despite the IAM role having s3:PutObject permission, is most likely caused by an explicit deny in the S3 bucket policy. S3 bucket policies can deny access to specific principals or roles, and an explicit deny overrides any allow permissions. Therefore, the bucket policy denying access to the EMR cluster's IAM role is the most likely cause.

Answer

The S3 bucket uses SSE-KMS encryption and the EMR role lacks kms:GenerateDataKey permission.

Answer

The S3 bucket has a Lifecycle rule that expires objects too quickly.

Answer

The EMR cluster was terminated before the write operation completed.

Question 3

Which THREE considerations are important when designing a data pipeline that uses AWS Glue to process streaming data from Amazon Kinesis Data Streams? (Choose 3.)

Accepted Answer

Set the number of Glue workers to match the number of shards for optimal parallelism. Option A is correct because AWS Glue streaming ETL reads from Kinesis in parallel across shards, so sizing the number of Glue workers to align with the shard count (or a multiple that matches the parallelism needed) ensures each shard can be consumed concurrently and avoids under- or over-provisioning of DPU capacity. Option B is correct because Kinesis Data Streams scales throughput per shard (1 MB/s or 1,000 records/s ingress per shard), so the stream must be provisioned with enough shards to absorb the expected record rate without throttling or ProvisionedThroughputExceededException. Option C is correct because Glue streaming jobs use checkpointing (stored in the job's checkpoint location, typically in Amazon S3) to track processed Kinesis sequence numbers, allowing the job to resume from the last committed position and avoid data loss or duplication after a failure or restart. Option D is not required because the batch window is a Lambda event source mapping feature, not a Glue streaming configuration, and Glue micro-batches are controlled by the job's window size and trigger interval. Option E is not required because Avro is a storage/serialization choice that may improve compression and schema handling but is not a core design consideration specific to consuming Kinesis streams with Glue.

Answer

Use batch window to accumulate data before processing

Answer

Convert data to Avro format for better compression

Question 4

A company runs a data pipeline on AWS Glue that processes streaming data from Amazon Kinesis Data Streams and writes results to an Amazon Redshift cluster. The pipeline has been running smoothly, but recently the Glue job started failing with 'ResourceNotFoundException' for the Redshift table. What should the data engineer check first?

Accepted Answer

Verify that the target Redshift table exists and was not dropped or renamed.. The error indicates the table does not exist or was deleted. Option B is wrong because IAM role issues would cause Access Denied, not ResourceNotFoundException. Option C is wrong because network issues would cause timeout or connection refused. Option D is wrong because schema changes could cause type mismatch but not ResourceNotFoundException.

Answer

Ensure the Redshift table schema matches the Glue job output.

Answer

Check the IAM role permissions for the Glue job to access Redshift.

Answer

Review security group rules for the Redshift cluster.

Question 5

Which TWO AWS services can be used to schedule and orchestrate ETL workflows that involve multiple steps and dependencies? (Choose 2.)

Accepted Answer

AWS Step Functions. AWS Step Functions (D) is correct because it is a serverless orchestration service whose state machines explicitly model multi-step workflows with sequencing, branching, parallelism, retries, and error handling, making it ideal for coordinating dependent ETL tasks across services like Lambda, Glue, and EMR. Amazon Managed Workflows for Apache Airflow (E) is correct because MWAA runs managed Apache Airflow, where DAGs define task dependencies and schedules, the standard way to orchestrate complex multi-step ETL pipelines. AWS Batch (A) only schedules and runs batch compute jobs and lacks native multi-step dependency orchestration, AWS Lambda (B) executes individual functions but is not itself a workflow scheduler/orchestrator, and AWS Data Pipeline (C) is a legacy service that is no longer the recommended tool for new multi-step ETL orchestration.

Answer

AWS Batch

Answer

AWS Lambda

Answer

AWS Data Pipeline

Question 6

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from and writes to the S3 bucket 'example-bucket'. The job's IAM role has the policy shown in the exhibit. The job fails with an Access Denied error when writing to a prefix 'output/'. Which permission is MISSING?

Accepted Answer

s3:DeleteObject. The IAM policy grants s3:GetObject and s3:PutObject on 'example-bucket/*', which includes the 'output/' prefix, so write access is sufficient. However, AWS Glue ETL jobs often create temporary files or need to clean up staging data, requiring s3:DeleteObject permission. Without it, the job may fail with an Access Denied error when attempting to delete objects during the write process or when cleaning up after a failure. Option D (s3:DeleteObject) is the missing permission.

Answer

s3:PutObjectAcl

Answer

s3:GetBucketAcl

Answer

s3:ListBucket on the output prefix

Question 7

A data engineer is running an AWS Glue ETL job that converts CSV files to Parquet. The job fails with the error shown in the exhibit. The input files are about 500 MB each. The job uses 5 workers of type G.1X (16 GB memory each). What is the MOST likely cause?

Accepted Answer

The output Parquet file size is too large for the executor memory. The most likely cause is that the output Parquet file size is too large for the executor memory. With 5 G.1X workers (16 GB each), the executor memory is limited, and if the output Parquet files are large (e.g., due to not repartitioning), the executor may run out of memory during the write operation. The error shown in the exhibit likely indicates an out-of-memory issue.

Answer

The data is highly skewed causing a single partition to receive too much data

Answer

The Spark driver does not have enough memory to handle the schema inference

Answer

The input CSV files are corrupt or have inconsistent schema

Question 8

A data engineer is investigating why Amazon Athena queries on the 'my-data-lake' bucket are slow. The table is partitioned by year/month/day. The exhibit shows the objects in one partition. What is the MOST likely cause of poor query performance?

Accepted Answer

The files are too small, causing excessive read overhead. The most likely cause of slow Athena queries is that the files are too small, causing excessive read overhead. When a partition contains many small files, Athena must open and read each file individually, which increases latency and reduces throughput. This is a common performance issue in data lakes, especially when data is ingested frequently in small batches.

Answer

The files are not compressed

Answer

The partition columns are not appropriately chosen

Answer

The data format is CSV instead of Parquet

Question 9

A company uses Amazon Athena to query data stored in an S3 bucket. The data is partitioned by year, month, day, and hour. The data engineer notices that queries are scanning a large amount of data even with a WHERE clause on the partition columns. What is the MOST likely cause?

Accepted Answer

The table does not have partitions defined in the AWS Glue Data Catalog.. If partitions are not defined in the table, Athena cannot perform partition pruning. Option A is wrong because too many partitions improve pruning, not hinder scanning. Option C is wrong because S3 storage class does not affect scanning. Option D is wrong because compressed files reduce scan size, not increase.

Answer

The data has too many partitions, causing overhead.

Answer

The S3 bucket uses the S3 Glacier storage class.

Answer

The data files are compressed with GZIP.

Question 10

A data engineering team notices that an Amazon Kinesis Data Stream is frequently exceeding its shard write throughput limit, causing throttling. The team needs a long-term solution to handle variable write traffic without manual intervention. Which action should the team take?

Accepted Answer

Implement a buffer using Amazon S3 and AWS Lambda that aggregates records and writes to Kinesis in batches.. The correct solution is to implement a buffer using Amazon S3 and AWS Lambda that aggregates records and writes to Kinesis in batches. This approach decouples the producers from the stream, allowing Lambda to accumulate records in S3 and then write them in larger, more efficient batches, which reduces the number of PutRecords calls and stays within shard limits. It also handles variable traffic automatically because Lambda scales with the volume of S3 events, and the buffering smooths out spikes without manual shard adjustments. This is a long-term, serverless solution that requires no manual intervention.

Answer

Configure the Kinesis Client Library to throttle consumption.

Answer

Increase the number of shards manually during peak hours.

Answer

Use Amazon Kinesis Data Firehose to buffer records before delivery to the stream.

Question 11

A company uses Amazon S3 to store raw data and AWS Lambda to process files as they arrive. The Lambda function sometimes times out when processing large files. The team wants to improve reliability and scalability. Which approach should the team take?

Accepted Answer

Use Amazon S3 event notifications to send events to an Amazon SQS queue, and then have Lambda poll the queue in batches.. Using S3 event notifications to send events to an SQS queue decouples the file upload from processing. Lambda can then poll the queue in batches, processing multiple events per invocation. This improves reliability by allowing retries and scalability by handling spikes in file arrivals without timing out. Option A is incorrect because AWS Batch is designed for long-running batch jobs, not event-driven processing triggered by S3 events. Option B is incorrect because SNS is push-based and can still overwhelm Lambda, leading to timeouts. Option C is incorrect because increasing Lambda timeout and memory only postpones the problem without addressing the root cause of scaling and reliability.

Answer

Replace Lambda with AWS Batch and use S3 event notifications to trigger the batch job.

Answer

Use Amazon S3 event notifications to send events to an Amazon SNS topic, which triggers Lambda.

Answer

Increase the Lambda function timeout to 15 minutes and memory to 3 GB.

Question 12

A data engineer is designing a data pipeline that ingests JSON data from Amazon Kinesis Data Streams and processes it using AWS Lambda. The Lambda function writes the processed data to an Amazon S3 bucket. The engineer needs to ensure at-most-once processing semantics. Which TWO configurations should the engineer implement? (Choose two.)

Accepted Answer

Use S3 PutObject with a unique object key (e.g., include a UUID) and overwrite set to false.. To achieve at-most-once processing semantics, the correct configurations are A and E. Option A ensures idempotency by using a unique object key (e.g., UUID) with overwrite set to false, so duplicate writes to S3 are prevented. Option E reduces the batch size to 1 and batch window to 0, minimizing the risk of reprocessing records and allowing idempotency from option A to handle any retries. Options B, C, and D are incorrect: B uses DynamoDB checkpointing for at-least-once, C increases batch size which can lead to partial failures, and D enables retries that violate at-most-once semantics.

Answer

Use DynamoDB for checkpointing to track processed records.

Answer

Set the Lambda function's batch size to 100 to process records in larger batches.

Answer

Enable function-level retries in the Lambda function for transient errors.

Question 13

A data engineer runs an AWS Glue Crawler that updates a table in the AWS Glue Data Catalog. The table is used by Amazon Athena queries. After the crawler runs, some queries start failing with the error 'HIVE_CANNOT_OPEN_SPLIT'. What is the most likely cause?

Accepted Answer

The crawler updated the schema and the partition metadata is inconsistent with the actual data.. The error 'HIVE_CANNOT_OPEN_SPLIT' occurs when Athena attempts to read a partition but the metadata in the Glue Data Catalog does not match the actual data in S3. This is commonly caused by running an AWS Glue Crawler that updates the schema of a table (e.g., adding, removing, or changing column types) without updating the partition metadata accordingly. The crawler may update the table schema but leave existing partition metadata unchanged, leading to a mismatch. Option A correctly identifies this cause. Option B is incorrect because a permissions issue would result in an access denied error, not a split error. Option C is incorrect because while Athena has a limit on the number of partitions per table (currently 20,000), this limit would cause a different error or query failure, not 'HIVE_CANNOT_OPEN_SPLIT'. Option D is incorrect because concurrent queries can cause throttling or other issues but not this specific split error.

Answer

The crawler does not have IAM permissions to read the S3 location.

Answer

The crawler created too many partitions, exceeding the Athena limit.

Answer

There are concurrent queries accessing the same table.

Question 14

A company uses Amazon EMR to run Spark jobs on data stored in Amazon S3. The data engineer notices that the jobs are running slower than expected. The engineer suspects that the S3 storage class might be affecting performance. Which THREE factors can impact read performance from S3? (Choose three.)

Accepted Answer

Use of S3 Select to retrieve only a subset of data.. Option B is correct because S3 Select lets Spark retrieve only a subset of an object's data using SQL expressions, reducing the bytes transferred and thus improving read performance. Option D is correct because average object size directly affects read throughput: many small objects incur more per-request overhead and limit parallelism, while larger objects (e.g., 128 MB–1 GB) allow more efficient sequential reads. Option E is correct because storing data in compressed formats such as GZIP or Snappy reduces the volume of bytes read from S3 and can significantly speed up Spark jobs, especially with splittable codecs like Snappy. Option A is not correct here because S3 Transfer Acceleration speeds up uploads and cross-region transfers over the internet, not the in-region read performance of EMR Spark jobs. Option C is not correct because S3 Object Lock is a retention/compliance feature (WORM) and does not affect read throughput or latency.

Answer

Use of S3 Transfer Acceleration.

Answer

Use of S3 Object Lock.

Question 15

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'.. Using 'Full LOB mode' ensures that large objects are migrated without truncation, parallel threads improve throughput, and 'BatchApply' applies changes in batches to maintain transactional consistency. Option A is incorrect because 'Limited LOB mode' can truncate LOB data if the size exceeds the configured maximum, leading to data loss. Option B is incorrect because merely enabling 'Full LOB mode' without parallel threads and 'BatchApply' may not handle large volumes efficiently, potentially causing inconsistency under heavy load. Option C is incorrect because truncating target tables and restarting the task is a destructive approach that does not resolve the underlying migration issues and can cause data loss or downtime.

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.