DEA-C01 Data Operations and Support Practice Test 29 — 15 Questions

Question 1

A data engineer is monitoring an Amazon Redshift cluster and notices that the disk space usage is increasing rapidly. The engineer wants to reclaim space from deleted rows. Which command should the engineer run?

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

Accepted Answer

VACUUM. VACUUM in Amazon Redshift reclaims disk space occupied by rows marked for deletion and re-sorts rows to restore sort order. When rows are deleted, they are only logically marked until VACUUM runs, so disk usage keeps growing. Running VACUUM (or VACUUM DELETE ONLY) releases that space back to the system.

Answer

ANALYZE

Answer

UNLOAD

Answer

COPY

Question 2

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 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.. Option C is correct because DynamoDB Streams captures item-level changes in near real time, and a Lambda consumer can inspect those records to detect anomalies or failed writes, providing an event-driven health signal for the table. Option D is correct because CloudWatch publishes DynamoDB metrics such as ConsumedReadCapacityUnits (and ConsumedWriteCapacityUnits), and alarms on these metrics reveal throttling or unexpected capacity consumption that indicates table health issues. Option A is wrong because S3 Inventory reports on S3 bucket objects, not DynamoDB table size or health. Option B is wrong because EC2 instance status checks only reflect the health of EC2 instances, not a managed DynamoDB table. Option E is wrong because DynamoDB does not expose a 'TableHealth' metric in CloudWatch; health must be inferred from real metrics like throttled requests, consumed capacity, and latency.

Answer

Use AWS S3 inventory to track table size.

Answer

Use EC2 instance status checks.

Answer

Monitor the 'TableHealth' metric in CloudWatch.

Question 4

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 5

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

Accepted Answer

Check for table locks using STV_LOCKS.. Option A is correct because STV_LOCKS is the Amazon Redshift system view that shows current table locks, and a query can appear slow simply because it is blocked waiting on a lock held by another transaction, so checking it is a valid troubleshooting step. Option C is correct because the EXPLAIN command returns the query execution plan, revealing expensive operations such as sequential scans, nested loops, or large data redistributions and broadcasts, which directly identifies why a query is slow. Option B is not a troubleshooting approach for slow queries; enabling cluster encryption is a security configuration and does not affect query performance. Option D is not a diagnostic step but a maintenance action, and running VACUUM blindly is not a valid troubleshooting approach since it can be costly and should follow diagnosis. Option E is likewise a remediation action that changes the table's distribution style, not a way to investigate the cause of slowness, and it should only be applied after identifying a distribution problem.

Answer

Enable encryption on the cluster.

Answer

Run VACUUM on the table.

Answer

Alter the table to change DISTSTYLE to KEY.

Question 6

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 shown is an S3 PutObject API call, which is the operation used to upload an object to an S3 bucket. The event name 'PutObject' and the presence of request parameters like 'bucketName' and 'key' confirm that a file was successfully written to the bucket. Therefore, the output indicates that a file was uploaded to the S3 bucket.

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 7

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.. Option A is correct because AWS Glue job bookmarks track previously processed data; if a bookmark is misconfigured, stale, or reset, the job can skip new or changed records, causing apparent data loss. Option D is correct because Glue DynamicFrame transformations and write operations can drop or quarantine rows that fail schema or type validation, so records with errors may silently disappear unless error handling is configured. Option E is correct because an incorrect ApplyMapping or column mapping can write source columns into the wrong target fields or omit them, making data appear lost in the target. Option B is not a valid consideration because S3 server-side encryption protects data at rest and does not cause records to be dropped during ETL processing. Option C is not a valid consideration because a low job timeout causes the job to fail or stop, which is a job failure rather than silent data loss.

Answer

Server-side encryption is disabled on the S3 bucket.

Answer

The job timeout is set too low.

Question 8

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 9

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 AWS Glue crawler relies on Hive-style partitioning (e.g., 'year=YYYY/month=MM/day=DD') to automatically detect and create partitions. The new data source uses a different format ('YYYY-MM-DD/') that does not follow the key-value structure, so the crawler cannot recognize it as partitions. The engineer must modify the new data source to store data in the same Hive-style partition format as the existing data.

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 10

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 in Amazon Redshift rely on zone maps that must be refreshed via VACUUM REINDEX after significant data changes. Without reindexing, the zone maps become stale and the query planner cannot skip blocks, forcing a sequential scan instead of a range-restricted scan. Running VACUUM REINDEX rebuilds the interleaved sort metadata so range predicates can prune blocks effectively.

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 11

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-*).. AWS Glue writes intermediate data to a temporary S3 bucket (aws-glue-* in the same region) before loading into Redshift. Even though the job has S3 permissions on the source bucket, the IAM role must also have s3:GetObject, s3:PutObject, and s3:DeleteObject on the Glue temporary bucket. The 'S3 Access Denied' error during pyWriteDynamicFrame indicates the write to this temp bucket is failing.

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.

Question 12

A company runs a daily batch processing job on Amazon EMR that reads data from Amazon S3 and writes results back to S3. The job takes longer than expected. The engineer wants to monitor the job's resource utilization. Which AWS service should be used to collect and visualize metrics such as CPU and memory usage of the EMR cluster's nodes?

Accepted Answer

Amazon CloudWatch with the CloudWatch Agent installed on the EMR nodes.. Amazon CloudWatch is the AWS service for monitoring and visualizing metrics. While EMR automatically sends some metrics (like CPU utilization) to CloudWatch, memory usage is not a default metric. To collect memory metrics (and other OS-level metrics) from EMR nodes, the CloudWatch Agent must be installed on each node. This agent collects the desired metrics and publishes them to CloudWatch, where they can be graphed and alarmed. Thus, CloudWatch with the CloudWatch Agent is the correct solution for monitoring CPU and memory usage of EMR cluster nodes.

Answer

AWS Config to record configuration changes in the EMR cluster.

Answer

Amazon Athena to query EMR job logs stored in S3.

Answer

AWS CloudTrail to log API calls made by the EMR job.

Question 13

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that processes each record and writes to an Amazon DynamoDB table. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' from DynamoDB. The Lambda function uses the AWS SDK to batch write items in batches of 25. The DynamoDB table has on-demand capacity mode. The stream has 10 shards, and the Lambda function is configured with a batch size of 100 and 5 concurrent invocations per shard. What step should the team take to resolve the issue?

Accepted Answer

Reduce the Lambda batch size to 25 and implement exponential backoff with jitter in the Lambda code.. The issue is DynamoDB throttling due to high write traffic from Lambda. The DynamoDB table is on-demand, which can throttle if bursts exceed sustained limits. Reducing the Lambda batch size from 100 to 25 decreases the number of records processed per invocation, lowering the instantaneous write rate. Implementing exponential backoff with jitter in the Lambda code allows retries on throttled requests, making the system more resilient. Option A is not required because on-demand mode automatically scales, and switching to provisioned can be costly. Option C is incorrect because increasing shards would increase parallelism and worsen throttling. Option D is incorrect because increasing concurrency would also increase write pressure on DynamoDB.

Answer

Switch the DynamoDB table from on-demand to provisioned capacity with a high write capacity unit (WCU) value.

Answer

Increase the number of Kinesis shards to 20 to reduce the load per shard.

Answer

Increase the Lambda function's reserved concurrency to allow more parallel executions.

Question 14

A data engineer is designing a data pipeline that processes sensitive personal data. The data is ingested via Amazon Kinesis Data Firehose and stored in Amazon S3. The pipeline must ensure that the data is encrypted at rest and in transit. The engineer also needs to audit access to the data. Which combination of services meets these requirements?

Accepted Answer

S3 server-side encryption (SSE-S3) for at-rest encryption, HTTPS for in-transit encryption, and AWS CloudTrail for auditing.. Option C correctly combines S3 server-side encryption (SSE-S3) for data at rest, HTTPS (TLS) for data in transit, and AWS CloudTrail for auditing access. SSE-S3 provides AES-256 encryption managed by S3, HTTPS ensures secure ingestion and retrieval, and CloudTrail logs all API calls to S3, enabling audit trails. This meets all three requirements: encryption at rest, encryption in transit, and auditability.

Answer

AWS KMS for encryption at rest, Kinesis Data Analytics for in-transit encryption, and AWS CloudTrail for auditing.

Answer

AWS KMS for encryption at rest, Amazon CloudWatch Logs for auditing, and TLS for in-transit encryption.

Answer

S3 client-side encryption, AWS Config for auditing, and TLS for in-transit encryption.

Question 15

A data analyst needs to query a large Amazon S3 bucket containing CSV files using Amazon Athena. The bucket has millions of small files (less than 1 MB each). The analyst reports that queries are very slow and often time out. The data is partitioned by date and the partition columns are defined in the table. What is the most effective way to improve query performance?

Accepted Answer

Run a compaction job to consolidate small files into fewer larger files (e.g., 128 MB each).. Many small files (under 1 MB) cause high overhead because each file requires a separate read operation and metadata call. Consolidating them into fewer larger files (e.g., 128 MB each) reduces the number of read operations and improves I/O efficiency, directly addressing the root cause of slowdowns and timeouts. Option A (converting to Parquet) improves storage efficiency and query performance but does not reduce the file count; it is a beneficial addition but not the most effective standalone fix for the small file problem. Option C (adding more partitions) would increase overhead by creating even more directories/files to scan. Option D (S3 Select) applies within individual files and does not mitigate overhead from file quantity.

Answer

Convert the files to Apache Parquet format using an AWS Glue ETL job.

Answer

Add more partitions by including hour and minute as partition keys.

Answer

Use S3 Select to push down filtering to S3 before Athena processes the data.