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

Question 1

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.. Option B is correct because the error indicates a missing column. This typically happens when the source data schema changes (e.g., column renamed or dropped) but the Data Catalog schema is not updated. Option A is incorrect because an incompatible IAM role would cause a different error (e.g., AccessDenied). Option C is incorrect because a corrupted file would cause a read error, not a schema resolution error. Option D is incorrect because the 'cannot resolve' error is about column names, not data types.

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 2

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.. Option B is correct because AWS DMS CDC requires supplemental logging on the source Oracle database to capture changes. The error indicates that DMS cannot enable it automatically, so the engineer must enable it manually. Option A is incorrect because increasing the task log interval does not affect supplemental logging. Option C is incorrect because changing the target engine version is unrelated. Option D is incorrect because the error is not about source connectivity.

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 3

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.. Option C is correct because DynamoDB's point-in-time recovery (PITR) provides continuous backups that allow restoration to any point within the last 35 days (default 24h to 35d) with no manual scheduling. Option A is incorrect because on-demand backups are manual and not continuous. Option B is incorrect because exporting to S3 is a separate process, not a backup feature. Option D is incorrect because DynamoDB Streams is for change data capture, not backups.

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 4

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.. Option A is correct because creating a read replica offloads read queries from the primary instance, reducing read latency. Option B is incorrect because increasing instance size may help but is more disruptive and costly. Option C is incorrect because enabling Multi-AZ is for high availability, not read performance. Option D is incorrect because deleting unused indexes can improve write performance but may not significantly reduce read latency.

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 5

A data engineer is monitoring an Amazon Redshift cluster and notices that some queries are experiencing high disk usage and slow performance. The engineer wants to identify the queries that are causing the most disk spills to temporary files. Which system table should the engineer query to get this information?

Accepted Answer

SVL_QUERY_SUMMARY. Option D is correct because the SVL_QUERY_SUMMARY system view provides information about disk spills for each query step, including the number of bytes spilled to disk. Option A is incorrect because STL_SCAN is for table scans, not spills. Option B is incorrect because STV_TBL_PERM shows permanent table storage, not temporary spills. Option C is incorrect because SYS_QUERY_DETAIL provides general query details but not spill information.

Answer

SYS_QUERY_DETAIL

Answer

STL_SCAN

Answer

STV_TBL_PERM

Question 6

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.. Option C is correct because increasing the buffer size (e.g., to 128 MB) causes Firehose to deliver fewer, larger files. Option A is incorrect because reducing the buffer interval would create more small files. Option B is incorrect because changing the compression algorithm does not affect file size directly; it reduces storage size but not the number of files. Option D is incorrect because using a Lambda transformation does not inherently change buffering behavior.

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 7

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. Option A is correct because EMR managed scaling uses YARNMemoryAvailablePercentage and YARNContainersPending as the default metrics for scaling. Option B is incorrect because CPUUtilization is not a default metric for EMR managed scaling. Option C is incorrect because HDFSUtilization is for HDFS, not YARN. Option D is incorrect because IOPS is not a metric for EMR managed scaling.

Answer

CPUUtilization

Answer

DiskIOPS

Answer

HDFSUtilization

Question 8

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 enabling Instance Fleets allows EMR to diversify instance types for Spot, reducing the chance of interruption. Option D is correct because using a task instance group with Spot instances for non-critical tasks ensures that only those tasks are interrupted. Option B is incorrect because using only On-Demand increases cost. Option C is incorrect because reducing core nodes may cause data loss if Spot nodes storing HDFS are reclaimed. Option E is incorrect because enabling termination protection is for accidental termination, not Spot interruptions.

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.

Question 9

A data engineer is troubleshooting a failed AWS Glue job that reads from an Apache Hive metastore in an Amazon EMR cluster. The error message indicates 'ClassNotFoundException: org.apache.hadoop.hive.ql.metadata.HiveException'. The Glue job uses a custom Python shell script. What is the most likely cause of this error?

Accepted Answer

Include the Hive JAR files in the 'Python library path' or use a Glue version with Hive support.. Option C is correct because the ClassNotFoundException for Hive classes indicates that the required Hive JARs are not available in the Glue job's classpath. The Glue job needs to include Hive jars either via a library path or by using a Glue version that supports Hive connectivity. Option A is incorrect because the Python script itself does not need to be modified to include imports; the JARs must be provided. Option B is incorrect because network connectivity would not cause a class not found error. Option D is incorrect because the error is not about IAM permissions.

Answer

Check the network connectivity between Glue and the EMR cluster.

Answer

Modify the Python script to import the Hive libraries manually.

Answer

Update the IAM role to allow 'hive:Describe*' actions.

Question 10

A company uses Amazon S3 to store sensitive data. The security team requires that all data in transit between on-premises applications and S3 be encrypted. The data engineer must implement a solution that meets this requirement without changing the applications. Which TWO solutions should the engineer consider? (Choose two.)

Accepted Answer

Use an S3 VPC Endpoint and enforce the use of HTTPS through bucket policies.. Option A is correct because using an S3 VPC Endpoint with a gateway endpoint ensures traffic stays within AWS and can enforce encryption via policies. Option C is correct because mounting S3 as a file system using AWS Storage Gateway with HTTPS ensures encryption in transit. Option B is incorrect because default encryption is for data at rest, not in transit. Option D is incorrect because S3 Transfer Acceleration does not enforce encryption; it uses HTTPS but is optional. Option E is incorrect because SSE-S3 is for data at rest.

Answer

Enable S3 Transfer Acceleration on the bucket.

Answer

Enable default encryption on the S3 bucket.

Answer

Use server-side encryption with S3 managed keys (SSE-S3).

Question 11

A company stores sensitive data in Amazon S3 and uses AWS Lake Formation to manage fine-grained access control. A data engineer notices that users are able to access data in S3 directly via the AWS Management Console, bypassing Lake Formation permissions. What should the engineer do to enforce Lake Formation access controls for all access methods?

Accepted Answer

Register the S3 location in Lake Formation and disable IAM access control for the registered location.. Option A is correct because to enforce Lake Formation permissions for all access methods, you must register the S3 location in Lake Formation and disable IAM access control for that location. Option B is incorrect because S3 Block Public Access does not affect IAM/Lake Formation permissions. Option C is incorrect because S3 bucket policies would allow direct access. Option D is incorrect because disabling CloudTrail does not enforce Lake Formation.

Answer

Add a bucket policy that denies all access except from Lake Formation.

Answer

Disable AWS CloudTrail logging for S3 access.

Answer

Enable S3 Block Public Access on the bucket.

Question 12

A data engineer is designing a data pipeline that processes streaming data. The pipeline must be able to handle duplicate records and ensure exactly-once processing semantics. Which THREE AWS services or features should the engineer consider? (Choose three.)

Accepted Answer

Amazon EMR with Apache Flink for exactly-once semantics.. Option A is correct because Kinesis Data Streams provides sequence numbers for records, enabling deduplication. Option C is correct because Kinesis Data Analytics uses the concept of 'idempotent' output to achieve exactly-once processing. Option D is correct because Apache Flink on Amazon EMR provides exactly-once processing through checkpointing. Option B is incorrect because Kinesis Firehose delivers at-least-once, not exactly-once. Option E is incorrect because DynamoDB Streams is at-least-once.

Answer

Amazon Kinesis Data Firehose with automatic retries.

Answer

Amazon DynamoDB Streams for change data capture.

Question 13

A data engineer is running an Amazon Athena query that scans a large amount of data in Amazon S3, resulting in high costs. The data is stored in Parquet format in a partitioned table. Which strategy would be MOST effective in reducing the amount of data scanned?

Accepted Answer

Ensure the query includes a WHERE clause that filters on partition columns.. Option D is correct because using a WHERE clause on partition columns allows Athena to use partition pruning, scanning only the relevant partitions. Option A is incorrect because converting from Parquet to CSV would increase data scanned. Option B is incorrect because increasing the number of partitions without querying on them does not reduce scan. Option C is incorrect because compressing with GZIP reduces storage size but Athena still decompresses and scans the full data if no partition pruning is used.

Answer

Convert the Parquet files to CSV format and apply GZIP compression.

Answer

Use S3 Intelligent-Tiering storage class to reduce storage costs.

Answer

Increase the number of partitions by adding more partition columns.

Question 14

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.. Option C is correct because the Glue job's IAM role may not have permission to call the s3:PutObject action, but the error could also be due to the bucket policy denying access. However, the policy shown allows s3:PutObject on the bucket. The most common issue is that the Glue job's role does not have the necessary trust policy or the bucket policy blocks the request. But based on the exhibit, the policy appears correct. The error could be due to missing permissions on the Glue job's execution role. Option A is incorrect because the policy includes s3:PutObject. Option B is incorrect because the policy includes the bucket ARN. Option D is incorrect because the error is access denied, not a bucket policy issue. Actually, the correct answer is that the role may also need s3:PutObject on the bucket itself (not just objects) for certain operations like multipart uploads. But the most likely reason is that the bucket policy denies the request. Given the exhibit, the IAM policy is correct, so the issue is likely the bucket policy. Option C is the best answer.

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 15

A company uses Amazon DynamoDB as the primary data store for a real-time application. The data engineer observes that some read requests are returning stale data, even though the application uses strongly consistent reads. The table has auto-scaling enabled with a maximum read capacity of 10,000 RCUs. The observed read traffic averages 8,000 RCUs but occasionally spikes to 12,000 RCUs. What is the most likely cause of the stale reads?

Accepted Answer

Read capacity auto-scaling cannot keep up with sudden traffic spikes, causing throttling and fallback to eventually consistent reads.. Option C is correct because strongly consistent reads can return stale data if the application is throttled due to insufficient read capacity. During spikes above the maximum auto-scaling limit (10,000 RCUs), requests may be throttled, and the SDK may retry with eventually consistent reads, returning stale data. Option A is incorrect because global tables with eventually consistent reads would not affect a single table. Option B is incorrect because DynamoDB Accelerator (DAX) provides eventual consistency by default, but strongly consistent reads would bypass DAX. Option D is incorrect because write sharding does not cause stale reads on the same table.

Answer

The application uses write sharding, causing read-after-write inconsistencies.

Answer

The application is using DynamoDB Accelerator (DAX) which caches data and may return stale values.

Answer

The table is part of a DynamoDB global table, and the application reads from a replica in a different region.