DEA-C01 Data Operations and Support Practice Test 26 — 15 Questions

Question 1

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?

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

Accepted Answer

Include the Hive JAR files in the 'Python library path' or use a Glue version with Hive support.. The 'ClassNotFoundException' for a Hive class indicates the Hive JARs are not on the classpath at runtime. AWS Glue's Python shell jobs run in an environment that does not include Hive libraries by default, so the engineer must either add the Hive JARs to the Python library path or use a Glue version/configuration that bundles Hive support. This is a classpath/dependency issue, not a network or IAM problem.

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 2

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.. Registering the S3 location in Lake Formation and disabling IAM access control for that registered location enforces Lake Formation permissions for all access methods, including direct S3 access via the console. This ensures that Lake Formation acts as the single source of truth for fine-grained access control.

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 3

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 Amazon EMR running Apache Flink supports exactly-once state consistency via its checkpointing mechanism (distributed snapshots aligned with barriers), so a streaming pipeline built on Flink can guarantee exactly-once processing even with duplicate or replayed records. Option C is correct because Kinesis Data Streams assigns each record a unique sequence number within a shard, and consumers can use those sequence numbers (or the KCL's checkpointing) to detect and skip already-processed records, enabling deduplication and effectively-once consumption. Option E is correct because Kinesis Data Analytics for Apache Flink inherits Flink's exactly-once checkpointing and, when combined with idempotent sinks (e.g., writing to DynamoDB with conditional writes or to S3 with deterministic keys), prevents duplicate records from producing duplicate downstream effects. Option B is not appropriate because Kinesis Data Firehose delivers at-least-once and its automatic retries can actually introduce duplicates rather than deduplicate them. Option D is not appropriate because DynamoDB Streams provides change data capture with at-least-once delivery and no built-in deduplication or exactly-once guarantee for a streaming pipeline.

Answer

Amazon Kinesis Data Firehose with automatic retries.

Answer

Amazon DynamoDB Streams for change data capture.

Question 4

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.. In Athena, partition pruning is the single most effective way to reduce data scanned because Athena only reads the S3 prefixes that match the partition filter. Adding a WHERE clause on partition columns (A) lets the query engine skip entire partitions, dramatically lowering both cost and runtime. This is especially impactful with Parquet, which already supports columnar projection and predicate pushdown.

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 5

A data engineer sees the CloudWatch log entry in the exhibit for a Lambda function that processes data from an Amazon SQS queue. What is the MOST likely cause of the timeout?

Accepted Answer

The Lambda function's timeout is too short for the processing required.. The CloudWatch log entry shows a Lambda invocation timing out, which directly indicates the function's configured timeout is shorter than the time needed to process the SQS message. Lambda timeouts are a hard limit (max 15 minutes), and when exceeded, the invocation is terminated and the message returns to the queue. Increasing the timeout (or optimizing the code) resolves the issue.

Answer

The Lambda function's reserved concurrency is set too low.

Answer

The Lambda function is running out of memory.

Answer

The SQS queue's visibility timeout is set too low.

Question 6

A data engineer creates an Amazon DynamoDB table using the CloudFormation snippet in the exhibit. The application writes 200 items per second to the table. The engineer notices that many write requests are being throttled. What is the MOST likely reason?

Accepted Answer

The provisioned write capacity is too low for the application's write rate.. The DynamoDB table is provisioned with a fixed write capacity, and the application writes 200 items per second. If the provisioned write capacity units (WCUs) are less than required, throttling occurs. Each write of an item up to 1 KB consumes 1 WCU, so 200 writes per second require at least 200 WCUs. If the table's provisioned write capacity is below this, requests will be throttled.

Answer

The table does not have a sort key, causing hot partitions.

Answer

The attribute type for OrderID should be numeric for better performance.

Answer

The table name 'Orders' conflicts with an existing table.

Question 7

A data pipeline using AWS Glue jobs is failing with 'Insufficient capacity' errors for Spark executors. Which action should the data engineer take to resolve this?

Accepted Answer

Increase the number of workers (DPUs) in the Glue job configuration.. The 'Insufficient capacity' error for Spark executors indicates that the Glue job is running out of resources (DPUs). Increasing the number of workers (DPUs) provides more compute capacity, allowing the job to allocate sufficient executors. Option A (reducing workers) would worsen the issue. Option B (increasing timeout) does not add resources. Option C (disabling Spark UI) does not affect capacity. Therefore, increasing the number of workers (DPUs) is the correct resolution.

Answer

Reduce the number of workers in the Glue job configuration.

Answer

Increase the job timeout value.

Answer

Disable Spark UI logging.

Question 8

A data engineer is setting up an AWS Glue job to process data from an Amazon S3 bucket. The job fails with an 'Access Denied' error. Which TWO IAM permissions are MOST likely missing from the Glue job's IAM role?

Accepted Answer

s3:PutObject. The Glue job needs to read the source data from the S3 bucket, which requires the s3:GetObject permission on the bucket's objects, so option E is correct. The job also needs to write its output (or intermediate results) back to S3, which requires the s3:PutObject permission, making option A correct. Option B (kms:Decrypt) is not necessarily missing unless the S3 objects are encrypted with a customer-managed KMS key and the role lacks decrypt rights, but the scenario does not state that. Option C (dynamodb:GetItem) is unrelated because the job processes data from S3, not DynamoDB. Option D (glue:StartJobRun) is a permission for triggering jobs, not for the job's execution role to access data, so it is not the cause of the Access Denied error.

Answer

kms:Decrypt

Answer

dynamodb:GetItem

Answer

glue:StartJobRun

Question 9

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. The migration is successful, but the ongoing replication task is experiencing high latency. Which configuration change is most likely to reduce latency?

Accepted Answer

Increase the size of the DMS replication instance.. Ongoing replication latency in AWS DMS is most commonly caused by insufficient compute, memory, or I/O capacity on the replication instance, especially when CDC processing, transformations, or high transaction volumes are involved. Increasing the replication instance size provides more CPU, memory, and network throughput to process change streams faster. This is the standard first remediation for sustained CDC latency before tuning task-level parameters.

Answer

Decrease the task's batch size and batch apply timeout.

Answer

Change the target endpoint to Amazon S3.

Answer

Enable Change Data Capture (CDC) from binary logs.

Question 10

Refer to the exhibit. A data engineer is reviewing the configuration of an Amazon Redshift cluster. The engineer wants to ensure that the cluster can be restored to a point in time up to 35 days in the past. Based on the exhibit, what change is needed?

Accepted Answer

Increase the automated snapshot retention period to 35 days.. Amazon Redshift automated snapshots have a default retention period of 1 day, but can be configured up to 35 days. To restore to a point in time up to 35 days, the retention period must be increased to 35 days.

Answer

Change the cluster subnet group to a custom one.

Answer

Enable encryption on the cluster.

Answer

Increase the number of nodes to 6.

Question 11

A company uses Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function is failing with 'ProvisionedThroughputExceededException' when writing to a DynamoDB table. Which action should the data engineer take to resolve this without losing data?

Accepted Answer

Configure a dead-letter queue (DLQ) on the Lambda function and increase the DynamoDB write capacity.. The Lambda function is throttled by DynamoDB because the write capacity is insufficient for the incoming Kinesis stream rate. Increasing the DynamoDB write capacity resolves the throttling, and configuring a DLQ on the Lambda function ensures that any records that fail after retries are captured for later processing, preventing data loss. This combination directly addresses the root cause while providing a safety net.

Answer

Reduce the number of Kinesis shards to lower the ingestion rate.

Answer

Increase the DynamoDB table's read capacity.

Answer

Disable retries on the Lambda function to avoid throttling.

Question 12

A data pipeline uses AWS DMS to replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The pipeline has been running successfully for months, but recently the DMS task status shows 'failed' with the error: 'The source database is running out of archive log space.' Which action should the engineer take to prevent this error?

Accepted Answer

Increase the amount of archive log space or reduce the log retention period on the source Oracle database.. The error 'source database is running out of archive log space' originates on the Oracle source, not on DMS. Oracle must retain archive logs until DMS (via LogMiner or Binary Reader) has consumed them; if DMS lags or the retention is too long, archive log space fills up. The fix is to increase archive log space or reduce the retention period so Oracle can purge logs after DMS reads them.

Answer

Configure multiple target S3 buckets to distribute the load.

Answer

Enable automatic log archiving on the DMS replication instance.

Answer

Increase the memory allocation for the DMS replication instance.

Question 13

A data engineer needs to ensure that sensitive data stored in Amazon S3 is encrypted at rest. Which TWO options meet this requirement? (Choose TWO.)

Accepted Answer

Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS). Options A (SSE-KMS) and B (SSE-S3) are correct because both are server-side encryption mechanisms that encrypt S3 objects at rest: SSE-KMS uses AWS KMS customer master keys (CMKs) to generate and manage data keys, while SSE-S3 uses AES-256 keys fully managed by Amazon S3. Both satisfy the requirement that sensitive data stored in S3 be encrypted at rest, and each is applied per-object when the object is written to the bucket. Option C is incorrect because a VPC only controls network-level access to S3 (via endpoints and policies) and does not encrypt data at rest. Option D is incorrect because MFA Delete only adds an authentication requirement for deleting objects or changing versioning state; it provides no encryption. Option E is incorrect because SSL/TLS encrypts data in transit, not at rest, and client-side encryption is a separate approach not represented by that option.

Answer

Using a VPC to restrict network access

Answer

Enabling MFA Delete on the S3 bucket

Answer

Client-Side Encryption with SSL/TLS

Question 14

A data engineer is troubleshooting a failed AWS Glue job that reads from an Amazon RDS for MySQL table. The error message indicates 'java.sql.SQLException: No suitable driver'. What is the most likely cause?

Accepted Answer

The MySQL JDBC driver JAR is not included in the Glue job's dependencies.. The error 'java.sql.SQLException: No suitable driver' indicates that the Java application (the Glue job) cannot find a suitable JDBC driver for the database URL. In AWS Glue, you must include the JDBC driver JAR as a dependency for the job. If the MySQL JDBC driver JAR is not included, the job cannot load the driver class, resulting in this error.

Answer

The Glue job is using the wrong JDBC driver class name.

Answer

The Glue job's VPC subnet does not have a route to the RDS instance.

Answer

The RDS instance is not publicly accessible.

Question 15

An AWS Glue job that performs data transformation on large Parquet files in Amazon S3 is taking a long time to complete. The job uses the default number of DPUs. Which change would most likely improve the job's performance?

Accepted Answer

Increase 'Max capacity' (number of DPUs) for the job.. AWS Glue allocates DPUs (Data Processing Units) to run the job, and increasing Max capacity adds more compute and memory resources that can parallelize the transformation across partitions. For large Parquet files with the default DPU count, the job is likely CPU- or memory-bound, so scaling DPUs is the most direct performance improvement. Parquet is already a columnar, compressed format, so the bottleneck is compute, not I/O format.

Answer

Use 'coalesce' to reduce the number of output files.

Answer

Reduce the number of partitions in the source data.

Answer

Change the input format from Parquet to CSV.