DEA-C01 Data Operations and Support — Timed 20 Questions

Question 1

An Amazon Kinesis Data Streams application is lagging behind. The data records are small (1 KB) and the shard count is 10. The consumer uses the KCL with default configuration. Which action will MOST effectively reduce the consumer lag?

20 minute time limit — choose an answer to begin.

Accepted Answer

Use Enhanced Fan-Out to provide dedicated throughput.. Enhanced Fan-Out provides each consumer with dedicated throughput (2 MB/s per shard) and eliminates the need for polling, which reduces latency and lag. Given that the consumer is lagging with default configuration and small records, Enhanced Fan-Out directly addresses the consumer-side bottleneck by providing dedicated read throughput, making it the most effective option. Option A is incorrect because the KCL does not support multiple workers per shard; each shard is processed by a single worker in a single-threaded manner. Option C may help if the shard is saturated with incoming data, but the problem is consumer lag, not data ingestion. Option D reduces data size but does not address the consumer processing speed.

Answer

Increase the number of KCL workers per shard (e.g., 2 workers per shard).

Answer

Increase the number of shards to 20.

Answer

Reduce the record size by compressing the data.

Question 2

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from a JDBC source. The error log shows 'java.sql.SQLException: Connection timed out'. The job previously ran successfully. Which of the following is the MOST likely cause?

Accepted Answer

The security group for the source database no longer allows traffic from the Glue job's IP range.. The error 'Connection timed out' indicates a network-level failure, not an authentication or schema issue. Since the job previously ran successfully, the most likely cause is that the security group for the source database no longer allows inbound traffic from the Glue job's IP range. AWS Glue ETL jobs run in a VPC with elastic network interfaces, and the security group rules must permit traffic on the JDBC port (e.g., 5432 for PostgreSQL, 3306 for MySQL).

Answer

The JDBC connection string has incorrect credentials.

Answer

The source database schema has changed.

Answer

The Glue job's timeout setting is too low.

Question 3

A data engineer is designing a solution to move data from an on-premises Oracle database to Amazon S3 using AWS DMS. The engineer needs to ensure that data changes are replicated continuously with minimal latency. Which DMS configuration is most appropriate?

Accepted Answer

Use a full-load task with ongoing replication (CDC). DMS with continuous replication (CDC) captures ongoing changes with low latency. Option A is wrong because AWS SCT is used for schema conversion, not data movement; DMS with full load only does an initial copy. Option C is wrong because a daily full-load task does not provide continuous replication or low latency. Option D is wrong because it describes Kinesis Data Streams, which is not a DMS configuration—DMS itself supports CDC to S3.

Answer

Use AWS SCT to convert the schema and then use DMS for full load

Answer

Use a full-load task that runs daily

Answer

Use Kinesis Data Streams to capture changes and write to S3

Question 4

Refer to the exhibit. A data engineer runs this CloudWatch Logs Insights query on a log group but gets no results. What is the most likely reason?

Accepted Answer

There are no log events containing the string 'ERROR' in the log group. The query 'fields @timestamp, @message | filter @message like /ERROR/ | sort @timestamp desc | limit 20' is syntactically correct (so option A is wrong). The limit of 20 would still return results if matching log events existed (so B is wrong). Although CloudWatch Logs Insights defaults to the last 15 minutes, that alone doesn't explain zero results—if errors occurred in that window, they would appear; the lack of results suggests no 'ERROR' events exist in the scanned logs (so C is wrong). The most likely reason is that the log group contains no log events with the string 'ERROR', making option D correct.

Answer

The query syntax is invalid

Answer

The query limit of 20 is too low

Answer

The time range is not specified and defaults to the last 15 minutes

Question 5

A data engineer notices that an AWS Glue ETL job is failing with an OutOfMemory error when processing a large dataset. The job uses a Standard worker type. Which action is MOST effective to resolve this issue without changing the job script?

Accepted Answer

Change to G.2X worker type. The most effective action is to switch to the G.2X worker type (Option C). In AWS Glue, worker types determine the DPU and memory allocated per worker. Standard workers provide 0.25 DPU (4 GB memory). An OutOfMemory error indicates insufficient memory per worker. Changing to G.2X allocates 2 DPU (16 GB memory) per worker, directly resolving the issue without modifying the script. Option A (increasing workers) does not increase per-worker memory. Option B (G.1X) provides 1 DPU (8 GB), which may help but is less effective than G.2X. Option D is invalid because DPU per worker is not a configurable parameter; it is defined by the worker type.

Answer

Increase the number of workers

Answer

Switch to G.1X worker type

Answer

Increase the number of DPUs per worker

Question 6

A data engineer is troubleshooting a failed AWS Glue job that writes results to Amazon S3. The error log shows 'AccessDenied' when trying to list the bucket. Which IAM policy statement should the engineer add to the Glue job's role?

Accepted Answer

s3:ListBucket. Listing a bucket requires the s3:ListBucket permission. Option B is wrong because s3:PutObject is for writing, not listing. Option C is wrong because s3:DeleteObject is not needed. Option D is wrong because s3:GetObject is for reading objects, not listing.

Answer

s3:PutObject

Answer

s3:DeleteObject

Answer

s3:GetObject

Question 7

A data engineer is monitoring an Amazon Kinesis Data Analytics application that processes real-time clickstream data. The application uses a Flink application with multiple operators. The engineer notices that the 'millisBehindLatest' metric is increasing steadily. Which action is MOST likely to reduce the lag?

Accepted Answer

Increase the parallelism of the Flink application.. Increasing parallelism in the Flink application allows more tasks to process data concurrently, which can reduce the 'millisBehindLatest' metric (lag). Decreasing batch size (Option A) would reduce throughput per task, worsening lag. Switching to GZIP compression (Option B) may reduce data size but adds decompression overhead and does not directly address processing parallelism. Increasing the retention period (Option D) does not affect processing speed; it only keeps data longer in the stream.

Answer

Decrease the batch size in the Flink application.

Answer

Switch the source stream to use GZIP compression.

Answer

Increase the retention period of the Kinesis stream.

Question 8

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 9

A data engineer is monitoring an Amazon Kinesis Data Stream and notices that the 'WriteProvisionedThroughputExceeded' metric is frequently elevated. The stream has 5 shards and is used by multiple producers. What is the BEST action to resolve this issue?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. WriteProvisionedThroughputExceeded indicates that the write rate exceeds the shards' capacity. Increasing the number of shards increases the total write capacity. Option A is incorrect because increasing the consumer's processing speed does not affect write throttling; it addresses read-side lag. Option C is incorrect because reducing the retention period does not affect write throughput. Option D is incorrect because implementing exponential backoff and retries in the producer applications helps with transient failures but does not resolve the root cause of insufficient capacity.

Answer

Increase the consumer's processing speed to reduce lag.

Answer

Reduce the data retention period of the stream.

Answer

Implement exponential backoff and retries in the producer applications.

Question 10

A data engineer has attached the IAM policy shown in the exhibit to a role used by an AWS Glue ETL job. The job fails when trying to write to the S3 bucket 'example-bucket' with the error: 'Access Denied'. What is the MOST likely reason?

Accepted Answer

The S3 bucket policy denies the PutObject action for the role.. Even if the IAM policy allows s3:PutObject on the bucket, the S3 bucket policy can explicitly deny the action for the role, resulting in 'Access Denied'. The exhibit shows the IAM policy grants s3:PutObject, so the error must stem from the bucket policy. Option A is incorrect because the IAM policy does include the bucket ARN for write operations. Option B is incorrect because a trust policy failure would prevent role assumption and produce a different error, not an S3 access denied. Option D is incorrect because the IAM policy does grant s3:PutObject permission.

Answer

The IAM policy does not include the bucket ARN for write operations.

Answer

The IAM role's trust policy does not allow Glue to assume the role.

Answer

The IAM policy does not grant s3:PutObject permission.

Question 11

An Amazon CloudWatch alarm is configured to monitor the CPUUtilization of an EC2 instance. The alarm state is 'INSUFFICIENT_DATA'. What is the most likely cause?

Accepted Answer

The EC2 instance is not sending metric data to CloudWatch.. INSUFFICIENT_DATA means no data points are available, often due to no metrics being published. Option B (evaluation periods set too low) would not cause this state; it might affect alarm transitions but not insufficient data. Option C (threshold set too high) is irrelevant to data availability. Option D (no alarm actions configured) does not affect data collection or alarm state.

Answer

The evaluation periods are set too low.

Answer

The alarm threshold is set too high.

Answer

The alarm does not have any actions configured.

Question 12

A data engineer notices that an AWS Glue ETL job is failing with a 'MemoryError' when processing a large dataset. Which approach should the engineer take to resolve this issue?

Accepted Answer

Increase the number of DPUs for the job.. Increasing the number of DPUs allocates more memory and processing capacity, which can resolve memory errors. Option B is incorrect because changing from Parquet to JSON typically increases memory usage due to less efficient encoding. Option C is incorrect because reducing partitions may increase memory pressure per partition. Option D is incorrect because S3 Select can filter data before loading but does not directly address the memory limitation in Glue; increasing DPUs is the more direct solution.

Answer

Change the source file format from Parquet to JSON.

Answer

Reduce the number of partitions in the source data.

Answer

Use S3 Select to filter data before processing.

Question 13

A data engineer is troubleshooting a failed AWS Glue job that reads from an S3 bucket and writes to an Amazon Redshift table. The error message indicates 'Access Denied'. Which TWO permissions are likely missing? (Choose TWO.)

Accepted Answer

s3:GetObject on the source S3 bucket. Options A and B are required for the Glue job to read from S3 and write to Redshift. Option C (kms:Decrypt) is only needed if the S3 bucket or Redshift cluster uses KMS encryption. Option D (ec2:DescribeInstances) is unrelated to the Glue job's read/write permissions. Option E (lambda:InvokeFunction) is not used in this S3-to-Redshift data transfer.

Answer

kms:Decrypt on a KMS key

Answer

ec2:DescribeInstances on the Redshift cluster

Answer

lambda:InvokeFunction on a Lambda function

Question 14

A data engineer is designing a disaster recovery strategy for an Amazon Redshift data warehouse. The RPO (Recovery Point Objective) is 1 hour, and the RTO (Recovery Time Objective) is 2 hours. Which approach meets these requirements with the least operational overhead?

Accepted Answer

Configure automated cross-region snapshot copy to another region.. Automated cross-region snapshot copy in Amazon Redshift provides scheduled snapshots copied to another region, enabling recovery within minutes. With a snapshot interval of 1 hour, RPO ≤1 hour is met. Restoring from a cross-region snapshot typically completes within 1-2 hours, meeting the 2-hour RTO. This approach is built-in with minimal manual intervention, offering the least operational overhead compared to setting up DMS replication (complex, costly) or daily UNLOAD exports (violates RPO). Local snapshots (Option D) do not protect against region failure and thus fail the DR requirement.

Answer

Set up a secondary Redshift cluster in another region and use AWS DMS for continuous replication.

Answer

Export the data to S3 daily using UNLOAD and copy to another region.

Answer

Enable automated snapshots with a retention period of 1 day.

Question 15

A data pipeline uses AWS Glue to process data from Amazon S3 and write results to Amazon Redshift. The pipeline fails intermittently with the error 'S3ServiceException: Access Denied'. The IAM role used by Glue has permissions to read from the S3 bucket. What is the most likely cause of this error?

Accepted Answer

The S3 bucket policy denies access to the Glue job's IAM role. S3 bucket policies can explicitly deny access even if the IAM role allows it. Option A is wrong because S3 bucket region does not cause access denied errors. Option B is wrong because S3 Server Access Logging does not affect access permissions. Option D is wrong because S3 Transfer Acceleration is not related to access denied errors.

Answer

The S3 bucket is in a different AWS Region than the Glue job

Answer

S3 Server Access Logging is enabled and blocking requests

Answer

The S3 bucket has S3 Transfer Acceleration enabled

Question 16

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error 'java.lang.OutOfMemoryError: Java heap space'. The job processes a large number of small files in Amazon S3. Which action would MOST effectively resolve the issue?

Accepted Answer

Enable S3 groupFiles option in the Glue job. Enabling the S3 groupFiles option in AWS Glue groups small files into fewer, larger splits, which reduces the number of Spark partitions and memory overhead, directly addressing the 'Java heap space' error. Option B is wrong because changing the worker type to G.1X (which has more memory per worker) does not solve the root cause of too many small files; it only provides more memory per worker but still processes many small files. Option C is wrong because increasing the number of workers increases parallelism but does not reduce the memory overhead per worker caused by numerous small files. Option D is wrong because using a G.2X worker type with more memory may temporarily alleviate the issue but is less effective than grouping files, which reduces the number of splits and thus memory consumption at the source.

Answer

Change the worker type to G.1X

Answer

Increase the number of workers in the Glue job

Answer

Use a G.2X worker type with more memory

Question 17

A company uses Amazon EMR to run Spark jobs on data stored in S3. After upgrading the EMR cluster to a new release, one of the Spark jobs fails with 'OutOfMemoryError' in the executor. Which configuration change is MOST likely to resolve this issue?

Accepted Answer

Increase spark.executor.memory to allocate more memory per executor.. Increasing spark.executor.memory directly allocates more memory per executor, addressing the OutOfMemoryError in the executor. Option A (increasing the number of core nodes) adds more cluster capacity but does not increase the memory available to individual executors, so it may not resolve the OOM if the existing executors are already memory-constrained. Option B (decreasing spark.sql.shuffle.partitions) reduces the number of shuffle partitions, which can increase the size of each partition and potentially cause more memory pressure, not less. Option C (increasing spark.driver.memory) only helps the driver process, not the executor, so it does not fix executor OOM errors.

Answer

Increase the number of core nodes in the EMR cluster.

Answer

Decrease spark.sql.shuffle.partitions to reduce overhead.

Answer

Increase spark.driver.memory in the Spark configuration.

Question 18

A company uses Amazon Redshift for its data warehouse. During a routine audit, the data engineer discovers that some queries are returning stale data even though the underlying source data has been updated. The engineer confirms that the COPY command completes successfully and that no errors are reported. Which action should the engineer take to ensure queries reflect the latest data?

Accepted Answer

Refresh the materialized views that the queries are using.. Materialized views in Amazon Redshift are not automatically refreshed when the underlying base tables are updated via COPY. To ensure queries return the latest data, the materialized views must be manually refreshed using the REFRESH MATERIALIZED VIEW command. Option A (VACUUM) reclaims disk space and re-sorts rows but does not update materialized views. Option B (RESET ALL) clears the query result cache, but stale materialized views persist even if the cache is cleared. Option C (ANALYZE) updates table statistics for query planning, not the actual data content. Therefore, refreshing the materialized views is the necessary action.

Answer

Run the VACUUM command on the source tables.

Answer

Clear the Redshift result cache by running RESET ALL.

Answer

Run the ANALYZE command on the source tables.

Question 19

A data engineer configured an AWS Glue job that reads from an S3 bucket and writes to an Amazon Redshift table. The job runs successfully, but the data in Redshift is missing some records that exist in S3. The engineer suspects the issue may be related to the job's bookmarks. What should the engineer do to ensure all records are processed?

Accepted Answer

Disable job bookmarks in the Glue job configuration.. Disabling job bookmarks forces the Glue job to reprocess all data from the S3 source, ensuring no records are missed. Option A is incorrect because increasing the number of workers does not address bookmark-related issues. Option B is incorrect because resetting the job bookmark using the AWS Glue API is not a standard operation; bookmarks are managed via the job configuration. Option C is incorrect because updating the IAM role does not affect bookmark behavior.

Answer

Update the IAM role to grant additional S3 read permissions.

Answer

Reset the job bookmark using the AWS Glue API.

Answer

Increase the number of workers in the Glue job.

Question 20

A data engineer notices that an AWS Glue ETL job is failing intermittently with the error 'Connection refused'. The job reads from Amazon RDS for MySQL and writes to Amazon S3. What is the MOST likely cause?

Accepted Answer

The security group for the RDS instance is not allowing inbound traffic from the Glue job's subnet.. The 'Connection refused' error indicates a network connectivity issue between the AWS Glue ETL job and Amazon RDS for MySQL. The most likely cause is that the security group for the RDS instance is not configured to allow inbound traffic on the MySQL port (3306) from the subnet or security group used by the Glue job. Option A (max connections) would result in a 'too many connections' error, not 'connection refused'. Option C (DPU limits) would cause resource-related errors, not a connection refusal. Option D (IAM permissions) would lead to an access denied error when writing to S3, not a connection error. Therefore, option B is correct.

Answer

The RDS instance has reached its maximum number of connections.

Answer

The Glue job is using too many DPUs and hitting resource limits.

Answer

The IAM role associated with the Glue job lacks permissions to write to the S3 bucket.