DEA-C01 Data Operations and Support Practice Test 22 — 15 Questions

Question 1

Refer to the exhibit. This log snippet is from a failed AWS Glue job. The job processes a large dataset in memory. What is the MOST likely cause of the OutOfMemoryError?

Accepted Answer

The Glue job is running with insufficient DPUs or worker type.. An OutOfMemoryError in AWS Glue typically occurs when the allocated DPUs or worker type are insufficient for the in-memory processing of a large dataset. Option B is incorrect because unsupported file formats cause parsing errors, not memory errors. Option C is incorrect because mismatched keys in a join cause data skew or incorrect results, but not directly an OutOfMemoryError. Option D is incorrect because too many partitions usually lead to small file overhead, not heap space exhaustion.

Answer

The input data is in an unsupported file format.

Answer

The job is attempting to join two tables with mismatched keys.

Answer

The job has too many partitions.

Question 2

A company uses Amazon S3 to store data for analytics. The data engineer needs to ensure that the S3 bucket is protected against accidental deletion of objects. Which THREE actions should the engineer take? (Choose THREE.)

Accepted Answer

Create an S3 bucket policy that explicitly denies the s3:DeleteObject action.. To protect against accidental deletion of objects in Amazon S3, three effective measures are: creating a bucket policy that explicitly denies the s3:DeleteObject action (option B), enabling versioning (option D), and enabling MFA Delete (option E). Option B ensures that no principal can delete objects unless an explicit allow overrides it. Option D preserves all object versions, allowing recovery of deleted objects. Option E requires multi-factor authentication for deletion operations, adding an extra security layer. Option A (server access logging) is for auditing, not prevention. Option C (lifecycle policy to Glacier) does not prevent deletion; it transitions objects to cheaper storage but objects can still be deleted by lifecycle rules or manually.

Answer

Enable server access logging for the S3 bucket.

Answer

Configure a lifecycle policy to transition objects to Glacier.

Question 3

Which TWO actions are effective ways to monitor the health of an Amazon DynamoDB table? (Choose two.)

Accepted Answer

Enable DynamoDB Streams and process with Lambda to detect failures.. Options C and D are correct. DynamoDB Streams with Lambda can detect failures by processing change events, and CloudWatch alarms on ConsumedReadCapacityUnits help monitor throughput and potential throttling. Option A is wrong because S3 inventory tracks S3 objects, not DynamoDB. Option B is wrong because EC2 instance status checks are for EC2 instances, not DynamoDB tables. Option E is wrong because there is no 'TableHealth' metric in CloudWatch; DynamoDB health is monitored via metrics like ConsumedReadCapacityUnits, ThrottledRequests, and SystemErrors.

Answer

Use AWS S3 inventory to track table size.

Answer

Use EC2 instance status checks.

Answer

Monitor the 'TableHealth' metric in CloudWatch.

Question 4

A data engineer creates an IAM policy as shown in the exhibit. The engineer then attaches this policy to an IAM role used by an application that uploads objects to the S3 bucket 'my-bucket'. When the application uploads an object without specifying server-side encryption, what happens?

Accepted Answer

The upload fails with a 403 Access Denied error.. The IAM policy includes a condition that requires the `s3:x-amz-server-side-encryption` header to be present and set to `AES256`. If the application uploads an object without specifying server-side encryption, this condition is not met, so the request is denied with a 403 Access Denied error. Options A, C, and D are incorrect because the upload fails and the object is not stored at all.

Answer

The object is uploaded with SSE-S3 encryption by default.

Answer

The object is uploaded without encryption.

Answer

The object is uploaded with SSE-C encryption.

Question 5

Which THREE are best practices for managing data in Amazon S3 for a data lake? (Choose three.)

Accepted Answer

Enable S3 Versioning to protect against accidental deletions.. Enabling S3 Versioning is a best practice for data lakes because it protects against accidental deletions or overwrites by preserving all versions of an object, including deletions (which are recorded as delete markers). This allows you to recover previous object states and is essential for data governance and auditability in a data lake environment.

Answer

Enable S3 Snapshot for point-in-time recovery.

Answer

Disable S3 server access logging to reduce costs.

Question 6

A data engineer runs an AWS Glue ETL job that transforms data in Amazon S3. The job fails with the error shown in the exhibit. Which action will MOST likely fix the issue?

Accepted Answer

Change the worker type from G.1X to G.2X.. The error indicates an out-of-memory error in the Spark executor. This occurs when the executor memory is insufficient for the data being processed. Option D, changing the worker type from G.1X to G.2X, doubles the memory per worker (from 4 GB to 8 GB for Spark executors), addressing the memory issue directly. Option A is incorrect because decreasing the number of workers reduces the total memory and parallelism, worsening the problem. Option B is incorrect because the error is not related to IAM permissions; an IAM policy for S3 write access would resolve a different error (e.g., AccessDenied). Option C is incorrect because increasing the number of workers from 2 to 4 adds more executors but does not increase memory per executor, and the error is likely due to insufficient per-executor memory.

Answer

Decrease the number of workers from 2 to 1.

Answer

Add an IAM policy that grants the Glue job permission to write to S3.

Answer

Increase the number of workers from 2 to 4.

Question 7

Which TWO are valid approaches to troubleshoot a slow Amazon Redshift query? (Choose two.)

Accepted Answer

Check for table locks using STV_LOCKS.. Options A and C are correct. Checking for table locks using STV_LOCKS (A) helps identify concurrency issues that slow queries, and using EXPLAIN (C) reveals the query execution plan to spot inefficient operations. Option B is incorrect because enabling encryption does not affect query performance. Option D is incorrect because VACUUM reclaims disk space and improves storage but is not a direct troubleshooting step for slow queries. Option E is incorrect because changing DISTSTYLE is a design optimization, not a troubleshooting action.

Answer

Enable encryption on the cluster.

Answer

Run VACUUM on the table.

Answer

Alter the table to change DISTSTYLE to KEY.

Question 8

A data engineer uses AWS CloudTrail to investigate a security incident. The engineer runs the command shown in the exhibit. What does the output indicate?

Accepted Answer

A file was uploaded to the S3 bucket.. The CloudTrail event shows EventName as PutObject, which indicates an object was uploaded to S3. The resource name includes the bucket and the object key 'sales_2024-07-01.csv', confirming a single file upload. Option A is incorrect because the event is PutObject, not GetObject. Option B is incorrect because PutObject is an upload, not a deletion. Option C is incorrect because PutObject represents uploading a single object, not listing a batch of files.

Answer

A file was downloaded from the S3 bucket.

Answer

A file was deleted from the S3 bucket.

Answer

A batch of files was listed from the S3 bucket.

Question 9

Which THREE are valid considerations when troubleshooting data loss in an AWS Glue ETL job? (Choose three.)

Accepted Answer

Job bookmarks may be skipping new data if not configured properly.. Options A, D, and E are correct. Job bookmarks may skip new data if not configured properly (A). Dynamic frame transformations can drop rows if errors occur during processing (D). Incorrect mapping of source columns to target columns can cause data loss (E). Option B is incorrect because disabling server-side encryption on S3 does not cause data loss; it affects data at rest encryption but not data integrity during ETL. Option C is incorrect because setting the job timeout too low may cause the job to fail prematurely, but it does not directly cause data loss; data can be reprocessed after adjusting the timeout.

Answer

Server-side encryption is disabled on the S3 bucket.

Answer

The job timeout is set too low.

Question 10

A company runs a data pipeline that ingests user activity logs from an API gateway into an Amazon Kinesis Data Firehose delivery stream. The Firehose stream writes data to an S3 bucket. The data is then processed by a scheduled AWS Glue job that runs every hour. Recently, the company noticed that the data in S3 is incomplete: some logs from the API are missing. The Glue job processes all files in the S3 bucket. The Firehose stream has a buffer size of 5 MB and a buffer interval of 60 seconds. The API sends data at a rate of approximately 2 MB per minute. What should the company do to reduce data loss?

Accepted Answer

Configure a Dead Letter Queue (DLQ) for the Firehose stream.. Configuring a Dead Letter Queue (DLQ) for the Kinesis Data Firehose delivery stream allows failed records to be captured and stored for later analysis and reprocessing. While Firehose automatically retries delivery, persistent failures can lead to data loss. A DLQ (or error backup destination) ensures that records that cannot be delivered are preserved. Option A: Decreasing the buffer interval from 60 to 30 seconds would cause more frequent deliveries, increasing the likelihood of transient failures affecting smaller batches and not reducing overall data loss. Option B: Increasing the buffer size to 10 MB does not prevent data loss; it only changes the threshold for flushing data. Since the data rate is 2 MB/min, the buffer size is rarely the trigger (interval is more likely). Option D: Enabling data transformation with AWS Lambda to compress data reduces the size of records but does not address delivery failures; it could even introduce additional failure points from the Lambda function. Thus, only C directly addresses data loss by capturing failed records.

Answer

Decrease the buffer interval to 30 seconds.

Answer

Increase the Firehose buffer size to 10 MB.

Answer

Enable data transformation with AWS Lambda to compress data.

Question 11

A data engineer is managing an Amazon RDS for PostgreSQL instance that serves as a source for change data capture (CDC) using AWS DMS. The DMS task is a full load followed by ongoing replication. The full load completed successfully, but the ongoing replication is failing with the error 'Value too long for character type'. The engineer has verified that the target database schema matches the source. The source table has a VARCHAR(256) column, and the target has VARCHAR(256) as well. However, some source rows contain values longer than 256 characters. What should the engineer do to resolve the issue?

Accepted Answer

Alter the target table column to a larger data type, such as VARCHAR(512).. The error indicates that source data exceeds the column length. The source column definition may not enforce the length, or the data was inserted bypassing constraints. The engineer should alter the target column to a larger size, such as VARCHAR(512), to accommodate the actual data. Option A is wrong because truncating data may cause data loss. Option B is wrong because renaming would cause mapping issues. Option C is wrong because the error is not about character set; using CLOB may not be compatible with the CDC process and would change the data type.

Answer

Modify the DMS task to truncate data that exceeds the column length.

Answer

Rename the target column to match a different source column.

Answer

Change the target column to a CLOB data type.

Question 12

A company is ingesting streaming data from thousands of IoT devices into Amazon Kinesis Data Streams. The data is processed by a Kinesis Data Analytics application. Recently, the application started reporting high iterator age (millisBehindLatest). Which action would BEST reduce the iterator age?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Increasing the number of shards increases the stream's throughput capacity, allowing the Kinesis Data Analytics application to consume data faster and reduce the iterator age (millisBehindLatest). Option A is incorrect: decreasing the data retention period does not improve processing speed; it only reduces the time data is stored. Option B is incorrect: increasing retention also does not affect processing speed. Option C is incorrect: the record size limit is fixed (1 MB) and cannot be increased; increasing shards is the appropriate scaling action.

Answer

Decrease the data retention period of the Kinesis stream.

Answer

Increase the data retention period of the Kinesis stream.

Answer

Increase the record size limit in the Kinesis stream.

Question 13

A company uses Amazon S3 to store raw data and AWS Glue to run ETL jobs. The data is partitioned by date in the format 'year=YYYY/month=MM/day=DD'. A new data source started sending data with a different date format 'YYYY-MM-DD'. The Glue crawler is configured to create a single table for the entire bucket. The crawler runs daily, but it is not detecting the new partitions from the new data source. The existing partitions are in the format 'year=2024/month=05/day=10', while the new data is stored as '2024-05-10/' without the key-value structure. How should the engineer modify the data pipeline to include the new data?

Accepted Answer

Modify the new data source to store data in the same Hive-style partition format as the existing data.. The new data uses a flat date folder (YYYY-MM-DD) instead of the existing Hive-style partition layout (year=YYYY/month=MM/day=DD). AWS Glue crawlers expect Hive-style partitions to automatically infer partitions. To include the new data without breaking the existing pipeline, the simplest solution is to store the new data in the same Hive-style format as the existing data. Option A is incorrect because partition indexes help with query performance, not with partition format mismatch. Option B is incorrect because custom classifiers affect schema inference, not partition structure. Option D is incorrect because converting to Parquet does not change the partition folder structure.

Answer

Run the crawler with the 'Create partition indexes' option enabled.

Answer

Configure the crawler to add a custom classifier for date formats.

Answer

Convert the new data to Parquet format.

Question 14

A data engineer is troubleshooting a slow Amazon Redshift query. The query scans a large table with interleaved sort keys. The engineer notices that the query plan shows a sequential scan instead of a range-restricted scan. What is the MOST likely reason?

Accepted Answer

The table has not been vacuumed and reindexed after large data loads.. Interleaved sort keys require periodic VACUUM REINDEX to maintain sort order. Without it, Redshift may fall back to sequential scans. Option B is incorrect because DISTKEY affects data distribution, not sort key usage. Option C is incorrect because compression does not prevent range-restricted scans. Option D is incorrect because WLM queue slots affect concurrency, not scan type.

Answer

The table has a poor distribution key (DISTKEY) causing data skew.

Answer

The table uses compression encodings that prevent range-restricted scans.

Answer

The workload management (WLM) queue is configured with too few query slots.

Question 15

A data engineering team uses Amazon S3 to store raw data files. They have an AWS Glue ETL job that reads from an S3 bucket, transforms the data, and writes to a Redshift cluster. The job runs daily and has been failing intermittently with the error: 'An error occurred while calling o143.pyWriteDynamicFrame. S3 Access Denied'. The team has confirmed that the IAM role used by the Glue job has s3:GetObject and s3:PutObject permissions on the bucket and all objects. The Redshift cluster is in the same VPC and the Glue connection is configured correctly. What is the most likely cause of the failure?

Accepted Answer

The Glue job's IAM role lacks permission to write to the Glue temporary file bucket (aws-glue-*).. Glue jobs use a special S3 bucket for bookkeeping and temporary data. The job's IAM role must have s3:PutObject permission on the bucket used for temporary files, which is often 'aws-glue-*' for the same region. If this permission is missing, the job fails with access denied. Option A is wrong because the error is an S3 access issue, not a network timeout. Option B is wrong because the error is not related to schema mismatch. Option C is wrong because the error is an S3 access issue, not a Glue job timeout.

Answer

The Redshift cluster is not publicly accessible and the Glue job does not have a VPC endpoint to Redshift.

Answer

The Glue job has exceeded the maximum execution time and is being killed by AWS.

Answer

The Glue job is using the wrong JDBC driver version for Redshift.