DEA-C01 Data Operations and Support Practice Test 12 — 15 Questions

Question 1

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. Option D is correct because the error shows OOM in the write task, which typically occurs when writing large files. Spark tries to write a large Parquet file that exceeds the executor memory. Option A would cause different errors. Option B is about reading, not writing. Option C is about data skew, which would cause OOM in shuffle, not in write.

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 2

A data engineer is designing a data pipeline that ingests data from an on-premises database into Amazon S3 using AWS Database Migration Service (DMS). The data must be encrypted at rest in S3 using SSE-S3. The engineer also needs to track changes to the source database in real time. Which DMS configuration should the engineer use?

Accepted Answer

Use DMS with ongoing replication (change data capture) enabled.. Option A is correct because DMS with CDC captures ongoing changes. Option B is wrong because full load only captures a snapshot. Option C is wrong because DMS supports CDC without needing Kinesis. Option D is wrong because restoring from a snapshot is not real-time.

Answer

Use DMS with a snapshot of the source database.

Answer

Use DMS with a full load task only.

Answer

Use DMS with a full load task and then stream to Amazon Kinesis.

Question 3

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. Option C is correct because the exhibit shows tiny files (50 bytes), which cause high metadata overhead and slow query performance. Option A is about compression, which cannot be determined. Option B is about partitioning, which is fine. Option D is about format, but CSV is standard.

Answer

The files are not compressed

Answer

The partition columns are not appropriately chosen

Answer

The data format is CSV instead of Parquet

Question 4

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.. Option B is correct because if partitions are not defined in the table, Athena cannot perform partition pruning. Option A is wrong because S3 storage class does not affect scanning. Option C is wrong because too many partitions improve pruning, not hinder. 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 5

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.. Option C is correct because using an S3 buffer with a Lambda function that batches records and writes to Kinesis can smooth out traffic spikes and reduce throttling. Option A is wrong because increasing shard count manually is not automatic. Option B is wrong because Kinesis Data Firehose is for delivery, not buffering for Kinesis streams. Option D is wrong because Kinesis Client Library is for consumers, not producers.

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 6

A data engineer is monitoring an Amazon Redshift cluster and notices that the 'WLM query wait time' metric is consistently high during peak hours. The cluster uses automatic WLM. The engineer wants to reduce query wait times without changing the cluster size. Which action is MOST effective?

Accepted Answer

Enable concurrency scaling.. Option C is correct because enabling concurrency scaling adds transient cluster capacity to handle bursts. Option A is wrong because short query acceleration helps with short queries, not necessarily wait times. Option B is wrong because manual WLM requires tuning queues. Option D is wrong because increased concurrency without resources may worsen contention.

Answer

Change WLM to manual mode and increase the number of queues.

Answer

Increase the maximum number of queries per queue.

Answer

Enable short query acceleration (SQA).

Question 7

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 8

A company runs a data pipeline that uses AWS Glue to process data from an Amazon DynamoDB table and write results to Amazon S3. The Glue job runs on a schedule every hour. Recently, the job started failing intermittently with 'ProvisionedThroughputExceededException' errors from DynamoDB. What is the BEST solution?

Accepted Answer

Implement exponential backoff and retries in the Glue job for DynamoDB operations.. Option D is correct because implementing exponential backoff in the Glue job will handle throttling gracefully. Option A is wrong because increasing provisioned capacity increases cost and may not be necessary. Option B is wrong because using DynamoDB Accelerator does not affect throughput limits. Option C is wrong because changing the schedule may not solve the issue.

Answer

Use DynamoDB Accelerator (DAX) to reduce read latency.

Answer

Change the Glue job schedule to run every 2 hours.

Answer

Increase the read capacity units of the DynamoDB table.

Question 9

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.. Option A is correct because S3 event notifications to SQS decouple the producer and consumer, allowing Lambda to poll at its own pace and process in batches. Option B is wrong because SNS is push-based and can still cause timeouts. Option C is wrong because increasing Lambda timeout is a temporary fix. Option D is wrong because AWS Batch is for long-running batch jobs, not event-driven processing.

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 10

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.. Options B and D are correct. B: Setting the Lambda batch size to 1 and window to 0 ensures one record per invocation, so if the function fails, it will not retry the same record (since at-most-once means no retries). D: Setting the S3 bucket to use Replace or a unique key prevents overwriting. Option A is wrong because increasing batch size increases chance of partial failures. Option C is wrong because enabling retries violates at-most-once. Option E is wrong because checkpointing is for at-least-once.

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 11

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.. Option C is correct because a schema change (e.g., new column or changed data type) can cause partition metadata to be inconsistent with actual data, leading to split errors. Option A is wrong because S3 permissions would cause access denied. Option B is wrong because the crawler can handle partitions. Option D is wrong because concurrent queries do not cause split errors.

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 12

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.. Options A, C, and D are correct. A: S3 Select can reduce data scanned. C: Data compression reduces network transfer. D: Larger object sizes improve throughput due to parallel requests. Option B is wrong because S3 Transfer Acceleration improves upload speed, not read performance. Option E is wrong because S3 Object Lock does not affect read performance.

Answer

Use of S3 Transfer Acceleration.

Answer

Use of S3 Object Lock.

Question 13

A team uses Amazon Redshift for analytics. They notice that some queries are slow and the system shows high disk usage. The team wants to improve query performance without adding more nodes. Which action should they take first?

Accepted Answer

Run the VACUUM and ANALYZE commands on the tables.. Option B is correct because VACUUM and ANALYZE reclaim space and update statistics, which can significantly improve query performance. Option A is wrong because distribution key changes require table recreation. Option C is wrong because WLM queues affect concurrency, not disk usage. Option D is wrong because compression encoding is set at table creation.

Answer

Enable compression on all columns.

Answer

Redistribute the tables by changing the distribution key to a column with high cardinality.

Answer

Modify the workload management (WLM) queue to increase concurrency.

Question 14

A data engineer is setting up a new Amazon Redshift cluster for a data warehouse. The engineer wants to ensure data durability and high availability. Which THREE features should the engineer consider? (Choose three.)

Accepted Answer

Cross-Region snapshot copy.. Options A, C, and E are correct. A: Automated snapshots provide point-in-time recovery. C: Cross-Region snapshot copy protects against regional failures. E: Multi-node clusters provide data replication within the cluster. Option B is wrong because Multi-AZ is not a feature of Redshift; it uses automatic failover within a single AZ. Option D is wrong because S3 Cross-Region Replication is for S3, not Redshift.

Answer

S3 Cross-Region Replication for Redshift data.

Answer

Multi-AZ deployment for automatic failover.

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'.. 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.

DEA-C01 Data Operations and Support Practice Test 12 — 15 Questions

Exhibit

DEA-C01 Data Operations and Support Practice Test 12 — 15 Questions

Exhibit