DEA-C01 Data Operations and Support Practice Test 14 — 15 Questions

Question 1

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.)

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

Accepted Answer

Use of S3 Select to retrieve only a subset of data.. Options B, D, and E are correct. B: S3 Select can retrieve only a subset of data, reducing the amount scanned and improving read performance. D: Larger average object sizes allow better throughput by leveraging parallel requests and reducing overhead. E: Storing data in compressed formats reduces the amount of data transferred, improving read performance. Option A is incorrect because S3 Transfer Acceleration is designed to improve upload speed over long distances, not read performance. Option C is incorrect because S3 Object Lock prevents object deletion or overwrite but does not affect read performance.

Answer

Use of S3 Transfer Acceleration.

Answer

Use of S3 Object Lock.

Question 2

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.. VACUUM and ANALYZE are the first actions to take because they reclaim disk space from deleted rows and update table statistics, which can significantly improve query performance and reduce disk usage. Option B is incorrect because enabling compression on all columns is not a first step; compression is typically set during table creation and may not address current disk usage issues. Option C is incorrect: redistributing tables by changing the distribution key requires recreating the table, which is an invasive operation and not the first troubleshooting step. Option D is incorrect: modifying the WLM queue affects concurrency management, not disk usage or query performance directly related to high disk usage.

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 3

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 B, C, and E are correct. B: Cross-Region snapshot copy allows copying snapshots to another AWS Region, protecting against regional disasters. C: Multi-node clusters with data replication within the cluster provide high availability by distributing data across nodes and automatically mirroring data. E: Automated snapshots to Amazon S3 enable point-in-time recovery for durability. Option A is incorrect because S3 Cross-Region Replication applies to S3, not directly to Redshift data. Option D is incorrect because Redshift does not support Multi-AZ deployments; it relies on automatic failover within the cluster using multiple nodes.

Answer

S3 Cross-Region Replication for Redshift data.

Answer

Multi-AZ deployment for automatic failover.

Question 4

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.

Question 5

A data engineer is troubleshooting an AWS Glue job that writes data to an S3 bucket. The IAM role attached to the Glue job has the policy shown in the exhibit. The job fails when writing to the 'secrets/' prefix but succeeds when writing to other prefixes. What is the reason for the failure?

Accepted Answer

The Deny statement explicitly denies PutObject to the secrets/ prefix.. The Deny statement explicitly denies s3:PutObject to the secrets/ prefix, which overrides any Allow statements. Option A is incorrect because the job can write to other prefixes, so it does have permission to the bucket. Option B is incorrect because the resource ARN includes the bucket and all objects (*), so it is sufficient. Option C is incorrect because in IAM, a Deny statement always overrides an Allow, regardless of order.

Answer

The job does not have permission to write to the bucket at all.

Answer

The resource ARN in the Allow statement does not include the bucket itself.

Answer

The Deny statement is not effective because it is placed after the Allow.

Question 6

A data pipeline uses Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The delivery stream is configured with a buffer size of 5 MB and a buffer interval of 60 seconds. The team notices that the S3 objects are much smaller than 5 MB. What is the most likely explanation?

Accepted Answer

The incoming data volume is low, so the 60-second buffer interval triggers delivery before the 5 MB buffer is filled.. If the incoming data rate is low, the buffer interval (60 seconds) expires before the buffer size (5 MB) is reached, causing small objects. Option B is incorrect because S3 event notifications do not split objects; they are triggered by events but do not affect object size. Option C is incorrect because S3 lifecycle policies transition objects to Glacier, which does not affect object size during delivery. Option D is incorrect because GZIP compression reduces the object size after batching, but the buffer interval can still trigger delivery before the buffer is full, so it is not the most likely explanation.

Answer

The S3 bucket has event notifications that split the objects.

Answer

The S3 bucket has a lifecycle policy that transitions objects to Glacier.

Answer

The delivery stream is using GZIP compression, which reduces the object size.

Question 7

A data engineer is investigating a failed AWS Glue job. The engineer runs the CLI command shown in the exhibit to retrieve the latest log stream. The output shows storedBytes: 0. What does this indicate?

Accepted Answer

The Glue job failed before writing any log events to CloudWatch.. StoredBytes: 0 means no log events were stored, likely because the job failed before writing any logs or logging was not enabled. Option A is wrong because logs are not expired; they were never written. Option B is wrong because the stream exists. Option D is wrong because the command retrieved the latest stream.

Answer

The log stream is from a different Glue job.

Answer

The log stream is empty because the job is still running.

Answer

The log stream has been expired and deleted.

Question 8

A data engineer runs an AWS Glue ETL job that reads from an S3 bucket containing JSON files. The job fails with an error indicating that some records are malformed. The engineer wants to skip the malformed records and continue processing. Which approach should the engineer take?

Accepted Answer

Configure the Glue DynamicFrame to use the `withErrorThreshold` option to skip corrupt records.. The `withErrorThreshold` option on an AWS Glue DynamicFrame allows the ETL job to skip a specified number of corrupt or malformed records without failing. This is the most direct way to handle malformed JSON records in Glue. Option A is incorrect because pre-processing all files is inefficient and may not be feasible for large datasets. Option B is incorrect because converting to Parquet does not address the issue of malformed JSON within the existing files. Option C is incorrect because AWS Glue Schema Registry validates schema compliance, not individual record malformation; it would reject entire datasets that don't match the schema, not skip malformed records.

Answer

Pre-process the JSON files to correct the malformed records before Glue reads them.

Answer

Convert the JSON files to Parquet format and use Glue to read Parquet.

Answer

Use AWS Glue Schema Registry to reject invalid records.

Question 9

A data engineer is configuring an S3 bucket for a data lake. The engineer runs the command shown in the exhibit. What does the output indicate about the bucket?

Accepted Answer

Versioning is enabled on the bucket.. 'Status: Enabled' indicates that versioning is enabled on the bucket. Option B is incorrect because when versioning is enabled, all versions of objects are retained, not just the latest, unless lifecycle rules are configured to remove older versions. Option C is incorrect because the status is 'Enabled', not 'Suspended'. Option D is incorrect because 'MFA Delete' is a separate setting; the output shows 'MFADelete: Disabled', meaning MFA Delete is not enabled.

Answer

The bucket retains only the latest version of each object.

Answer

Versioning is suspended on the bucket.

Answer

MFA Delete is enabled for the bucket.

Question 10

A data engineering team notices that an AWS Glue ETL job fails intermittently with a 'ThrottlingException' error. The job reads from an Amazon S3 bucket and writes to an Amazon Redshift table. What is the MOST likely cause of this error?

Accepted Answer

The Redshift cluster's write throughput is exceeding its provisioned capacity.. The 'ThrottlingException' error occurs when the rate of API requests exceeds the allowed limit. In this scenario, the Glue job writes to Amazon Redshift. Redshift has a provisioned write throughput capacity; if the Glue job attempts to write data faster than Redshift can handle, Redshift throttles the requests, resulting in a ThrottlingException. This is the most likely cause. Option A is incorrect because S3 throttling would manifest as a different error (e.g., 'SlowDown' or 'RequestTimeout'). Option C is incorrect because Glue job concurrency limits would cause a 'ConcurrentRunsExceededException' or similar, not ThrottlingException. Option D is incorrect because insufficient memory would typically lead to an 'OutOfMemoryError' or job failure, not a ThrottlingException.

Answer

The S3 bucket's request rate is exceeding the bucket's performance limits.

Answer

The Glue job is exceeding the maximum number of concurrent runs allowed.

Answer

The Glue job's allocated memory is insufficient for the data volume.

Question 11

A company uses Amazon EMR to run Spark jobs on a transient cluster. The jobs process data from S3 and write results back to S3. The team wants to reduce costs by optimizing the cluster. Which action should the team take?

Accepted Answer

Use Spot Instances for the task nodes.. Using Spot Instances for task nodes in a transient EMR cluster significantly reduces compute costs because Spot Instances are spare AWS EC2 capacity offered at up to 90% discount compared to On-Demand. Since transient clusters are terminated after job completion, the risk of Spot Instance interruptions is mitigated—the job can simply be retried on a new cluster if needed. This directly addresses the cost optimization goal without sacrificing job functionality.

Answer

Increase the number of core nodes and use larger instance types.

Answer

Enable EMRFS consistent view.

Answer

Terminate the cluster after each job and manually restart it for the next job.

Question 12

A company runs an Amazon DynamoDB table with on-demand capacity. A new reporting application performs frequent Scan operations on the table, causing occasional 'ProvisionedThroughputExceededException' errors. The operations team needs to resolve this with minimal cost. What should they do?

Accepted Answer

Create a global secondary index (GSI) on the attributes used in the reporting queries.. Creating a global secondary index (GSI) on the attributes used in the reporting queries allows the reporting application to use Query operations instead of expensive Scan operations. This reduces the read capacity consumption and avoids partition-level throttling, which is the cause of the 'ProvisionedThroughputExceededException' errors. On-demand tables have per-partition throughput limits, and frequent Scans can exceed those limits. Option A is incorrect because increasing the maximum read capacity via AWS Support is not applicable to on-demand tables; on-demand scaling is automatic but per-partition limits still apply. Option B is incorrect because switching to provisioned capacity would require careful capacity planning and would likely increase costs compared to using a GSI. Option C is incorrect because DAX can cache data and reduce read load, but it does not eliminate the inefficiency of Scan operations; the root cause is the Scan itself, which can be avoided by using a GSI to enable efficient queries.

Answer

Increase the table's maximum read capacity by requesting a limit increase from AWS Support.

Answer

Switch the table to provisioned capacity and increase the read capacity units.

Answer

Enable DynamoDB Accelerator (DAX) to cache the Scan results.

Question 13

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket. The job logs show the following error: 'java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found'. Which TWO actions will resolve this issue?

Accepted Answer

Include the hadoop-aws jar as an extra jar in the Glue job configuration.. The error 'ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem' indicates the S3A filesystem library is missing from the classpath. Option B resolves this by adding the hadoop-aws jar as an extra jar, which provides the S3AFileSystem class. Option D resolves it by using a Glue version (3.0 or later) that includes the S3A library by default. Option A (VPC endpoint) addresses networking, not classpath. Option C (IAM role) addresses permissions, not missing classes. Option E (EMRFS) is for Amazon EMR, not AWS Glue.

Answer

Enable VPC S3 endpoint for the Glue job.

Answer

Update the IAM role to allow access to S3.

Answer

Change the S3 access mode from S3A to EMRFS.

Question 14

A data engineer is troubleshooting a failed Amazon Kinesis Data Firehose delivery stream. The stream is configured to deliver data to an Amazon S3 bucket. The error log shows: 'The destination S3 bucket's bucket policy does not allow the firehose to put objects.' What is the MOST likely issue?

Accepted Answer

The S3 bucket policy does not include an Allow statement for the firehose to put objects.. The error states the bucket policy does not allow the firehose to put objects. The solution is to add an Allow statement in the bucket policy granting the firehose's IAM role permission to execute s3:PutObject. Option A is incorrect because the error is about bucket policy, not ACLs. Option B is incorrect because the error is already about permissions. Option D is incorrect because the issue is at the S3 bucket policy level, not IAM role trust policy.

Answer

The S3 bucket's ACL is configured to deny write access to the firehose.

Answer

The IAM role used by Firehose does not have the necessary permissions.

Answer

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

Question 15

A company uses Amazon RDS for MySQL as a source for AWS DMS. The replication tasks are failing due to large transactions on the source. The team wants to reduce the impact of large transactions on DMS. Which THREE actions should the team take?

Accepted Answer

Increase the size of the source RDS instance and enable binary logging with ROW format.. To reduce the impact of large transactions on AWS DMS when using Amazon RDS for MySQL as a source, the recommended actions are: increasing the source RDS instance size and enabling binary logging with ROW format (option B) to better handle large transactions and support CDC; enabling BatchApply (option C) to reduce the time to apply changes by batching them; and tuning the DMS task to use a larger memory limit while adjusting the transaction size (option E) to improve memory management. Option A is incorrect because increasing parallel threads on the source does not directly mitigate large transaction impact; DMS manages parallelism internally. Option D is incorrect because using 'full load only' would stop CDC, making it unsuitable for ongoing replication.

Answer

Increase the number of parallel threads on the source.

Answer

Use the 'full load only' migration type.