DEA-C01 Data Operations and Support Practice Test 25 — 15 Questions

Question 1

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?

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

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 2

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.. The output of the command (likely 'aws s3api get-bucket-versioning') shows a Status of 'Enabled', indicating that versioning is enabled on the bucket. This means multiple versions of objects are retained.

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 3

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 4

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: org.apache.hadoop.fs.s3a.S3AFileSystem' means the S3A filesystem implementation class is not on the Glue job's classpath, so the fix must supply that library. Option B is correct because adding the hadoop-aws jar (which contains org.apache.hadoop.fs.s3a.S3AFileSystem) as an extra jar via the Glue job's --extra-jars configuration puts the missing S3A class on the classpath. Option D is correct because newer AWS Glue versions (Glue 3.0 and later, built on Spark 3.x/Hadoop 3.x) bundle the S3A filesystem library, so running the job on such a version provides the class natively. Option A is wrong because a VPC S3 endpoint only fixes network routing to S3, not a missing Java class. Option C is wrong because IAM permissions govern authorization, and a ClassNotFoundException is a classpath problem, not an access-denied error. Option E is wrong because EMRFS is an EMR-specific filesystem and is not a valid S3 access mode to switch to within 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 5

A company uses Amazon Redshift for data warehousing. They notice that queries are running slowly, and the STL_LOAD_ERRORS table shows many 'Parse error' entries. The data is loaded from Amazon S3 using COPY commands. What is the MOST likely cause of the parse errors?

Accepted Answer

The source data files have a different schema or delimiter than what is specified in the COPY command.. COPY loads data by parsing each source record according to the FORMAT, DELIMITER, and column definitions supplied in the command. When the actual file layout (column count, order, or delimiter) does not match those specifications, Redshift cannot map fields to columns and writes a 'Parse error' row into STL_LOAD_ERRORS. This is the classic signature of a schema/delimiter mismatch, not a resource or permission problem.

Answer

The Redshift cluster does not have enough compute nodes to process the data.

Answer

The IAM role used by Redshift does not have permission to decrypt the S3 objects.

Answer

The source data files are compressed using an unsupported compression format.

Question 6

A data engineer is monitoring Amazon CloudWatch metrics for an Amazon Redshift cluster and notices high CPU utilization. The engineer wants to reduce CPU usage. Which TWO actions should the engineer take?

Accepted Answer

Enable concurrency scaling to offload read queries to additional clusters.. Option A is correct because concurrency scaling automatically adds transient cluster capacity to handle bursts of read (SELECT) queries, offloading that work from the main cluster and thereby reducing its CPU utilization. Option C is correct because choosing appropriate sort keys and compression encodings reduces the amount of data scanned and I/O performed, which directly lowers CPU work during query execution. Option B is not the best fit because adding nodes increases capacity but does not address inefficient queries or design, and it is a costlier, less targeted remedy for CPU pressure. Option D is not correct because VACUUM reclaims space and re-sorts rows after deletes/updates; it is maintenance rather than a primary CPU-reduction action and can itself consume significant resources. Option E is not correct because audit logging only records connection and user activity for monitoring/compliance and does nothing to reduce CPU usage.

Answer

Increase the number of nodes in the cluster.

Answer

Run the VACUUM command on all tables.

Answer

Enable audit logging to monitor queries.

Question 7

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 8

A data engineer notices that an AWS Glue ETL job that processes streaming data from Amazon Kinesis Data Streams is failing intermittently with a 'ResourceNotFoundException' error for the Kinesis stream. The job has been running successfully for weeks. Which action should the engineer take to resolve the issue?

Accepted Answer

Add the 'kinesis:DescribeStream' permission to the IAM role used by the Glue job.. A 'ResourceNotFoundException' from a Glue job reading Kinesis typically indicates the Glue execution role lacks permission to describe or access the stream, causing the service to report the stream as not found rather than as access-denied. Adding 'kinesis:DescribeStream' (and typically DescribeStreamSummary, GetShardIterator, GetRecords) to the IAM role resolves the issue. Since the job ran successfully for weeks, a recent IAM policy change or role modification is the likely trigger.

Answer

Increase the number of shards in the Kinesis data stream to handle higher throughput.

Answer

Rename the Kinesis data stream to match the stream name used in the Glue job exactly, including case.

Answer

Increase the timeout for the Glue job in the job configuration.

Question 9

A company uses AWS Glue to run ETL jobs on a schedule. Recently, a job failed with the error: 'AnalysisException: cannot resolve '`column_name`' given input columns: ...'. The job reads from an Amazon S3 source that has a schema defined in the AWS Glue Data Catalog. What is the MOST likely cause?

Accepted Answer

The schema of the source data has changed and is not reflected in the Data Catalog.. The AnalysisException 'cannot resolve column_name given input columns' means the Spark/Glue job's code references a column that does not exist in the DataFrame schema derived from the Data Catalog. The most likely cause is that the source data's schema has evolved (new/renamed/dropped columns) but the Glue Data Catalog table definition was not updated, so the job's schema and the actual data are out of sync.

Answer

The source data file is corrupted and cannot be parsed.

Answer

The IAM role associated with the Glue job does not have permissions to read the S3 bucket.

Answer

The data type of the column in the source does not match the Data Catalog definition.

Question 10

A company uses AWS DMS to replicate data from an on-premises Oracle database to Amazon RDS for MySQL. The full load completes successfully, but ongoing replication (CDC) is failing with a 'Failed to add supplemental logging' error. What should the data engineer do to resolve this issue?

Accepted Answer

Enable supplemental logging on the source Oracle database manually.. AWS DMS requires supplemental logging to be enabled on the source Oracle database to capture change data for CDC replication. The 'Failed to add supplemental logging' error occurs because DMS cannot automatically enable this at the database level when the user lacks the necessary privileges (e.g., ALTER DATABASE). The data engineer must manually enable supplemental logging on the source Oracle database, typically by executing ALTER DATABASE ADD SUPPLEMENTAL LOG DATA; and ensuring table-level supplemental logging is also configured.

Answer

Recreate the DMS endpoint for the source database with a new connection.

Answer

Modify the target MySQL database to use a different engine version.

Answer

Increase the task log interval in the DMS task settings.

Question 11

A data engineer needs to back up an Amazon DynamoDB table daily. The backup must be restorable to a specific point in time within the last 24 hours. Which solution meets these requirements with the LEAST operational overhead?

Accepted Answer

Enable point-in-time recovery (PITR) on the table.. DynamoDB point-in-time recovery (PITR) provides continuous backups of your table data with a restore granularity of one second, allowing you to restore to any point within the last 35 days. It requires no manual intervention, thus offering the least operational overhead. Enabling PITR meets the requirement to restore to a specific point in time within the last 24 hours.

Answer

Create an on-demand backup of the table every 24 hours.

Answer

Use DynamoDB Streams to replicate data to another table.

Answer

Export the table data to Amazon S3 every 6 hours using a Lambda function.

Question 12

A company runs a critical PostgreSQL database on Amazon RDS. The database experiences high read latency during peak hours. The data engineer needs to reduce read latency with minimal changes to the application. Which solution is MOST effective?

Accepted Answer

Create a read replica of the RDS instance and redirect read queries to it.. A read replica is a separate RDS instance that receives asynchronous replication from the primary. By redirecting read-only queries to the replica, you offload read traffic from the primary, directly reducing read latency during peak hours. This requires minimal application changes—typically just updating the connection string for read operations. Multi-AZ and larger instance classes do not provide a dedicated read-scaling endpoint, and deleting indexes would harm performance.

Answer

Delete unused indexes to improve query performance.

Answer

Enable Multi-AZ deployment for automatic failover.

Answer

Increase the DB instance class to a larger size with more memory.

Question 13

A data engineer is troubleshooting a data pipeline that uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The engineer notices that the S3 bucket contains many small files (less than 1 MB). This is causing performance issues in downstream processing. What is the BEST way to reduce the number of small files?

Accepted Answer

Increase the buffer size to at least 128 MB in the Firehose delivery stream configuration.. Increasing the Firehose buffer size to at least 128 MB allows more data to accumulate before delivery, resulting in fewer, larger files in S3. This directly addresses the small file problem by batching more records per delivery.

Answer

Use an AWS Lambda function to transform the data before delivery.

Answer

Change the compression format from GZIP to Snappy.

Answer

Decrease the buffer interval in the Firehose delivery stream configuration.

Question 14

A company is running an Amazon EMR cluster with Spark for data processing. The data engineer wants to automatically scale the core and task nodes based on the YARN memory and CPU utilization. Which scaling metric should the engineer use for the EMR managed scaling policy?

Accepted Answer

YARNMemoryAvailablePercentage. EMR managed scaling policies are designed around YARN metrics, and YARNMemoryAvailablePercentage is the primary metric used to determine when to add or remove core and task nodes. When available YARN memory drops below a threshold, EMR scales out; when it rises above a threshold, EMR scales in. This directly reflects the resource pressure that Spark executors place on the cluster.

Answer

CPUUtilization

Answer

DiskIOPS

Answer

HDFSUtilization

Question 15

A data engineer is using Amazon EMR to process large datasets. The cluster uses a mix of Spot Instances and On-Demand Instances. The engineer wants to reduce costs while ensuring the job can complete even if Spot Instances are reclaimed. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Enable Instance Fleets to use multiple instance types for Spot Instances.. Option A is correct because EMR Instance Fleets let you specify multiple instance types and subnets for Spot capacity, so if one Spot pool is reclaimed or unavailable, EMR can provision from another pool, improving the chance the job finishes despite Spot interruptions. Option E is correct because task nodes perform only data-processing work and hold no HDFS data, so placing them in a Spot-based task instance group lets you capture Spot savings while core nodes (which store HDFS data) remain protected and the job can still complete if task Spot capacity is reclaimed. Option B is wrong because using only On-Demand Instances eliminates Spot savings and does not address cost reduction. Option C is wrong because core nodes hold HDFS data; using Spot for core nodes risks data loss and job failure when Spot capacity is reclaimed. Option D is wrong because termination protection only prevents accidental cluster termination and does not help the job survive Spot reclamation or reduce costs.

Answer

Use only On-Demand Instances for all nodes.

Answer

Use Spot Instances for core nodes to reduce cost.

Answer

Enable termination protection for the cluster.