DEA-C01 Data Operations and Support Practice Test 28 — 15 Questions

Question 1

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?

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

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 2

Refer to the exhibit. An IAM policy is attached to a user who needs to read objects from the 'example-bucket' S3 bucket. The user reports being unable to read any object under the 'confidential/' prefix. What is the reason for this access issue?

Accepted Answer

The explicit deny statement overrides the allow statement. An explicit deny statement overrides any allow statement, regardless of the order in which they appear. In this policy, there is an allow for GetObject on all objects in example-bucket, but there is an explicit deny for GetObject on the 'confidential/' prefix. Since explicit deny takes precedence, the user cannot read objects under that prefix. Option A is incorrect because the order of evaluation does not matter; explicit deny always wins. Option B is incorrect because the deny statement does not need an explicit allow; the deny itself is effective. Option D is incorrect because the resource ARN in the deny statement is correctly specified as 'arn:aws:s3:::example-bucket/confidential/*'.

Answer

The allow statement is evaluated before the deny statement

Answer

The deny statement is missing an explicit allow for the confidential prefix

Answer

The resource ARN in the deny statement is incorrect

Question 3

A data engineer is troubleshooting an AWS Glue ETL job that suddenly started failing with 'An error occurred while calling o103.pyWriteDynamicFrame. Unknown error'. The job writes data to an Amazon Redshift table. Which step should the engineer take FIRST?

Accepted Answer

Review the job's CloudWatch Logs for detailed error messages.. The error message 'An error occurred while calling o103.pyWriteDynamicFrame. Unknown error' is a generic wrapper thrown by the Glue DynamicFrame writer when the underlying cause is not surfaced. The first and most reliable step is to inspect the job's CloudWatch Logs, where the full stack trace, Redshift error code, and driver-level messages are recorded. This aligns with standard AWS troubleshooting guidance: always check logs before making changes. Reviewing logs is non-destructive, fast, and directly reveals the root cause (e.g., permission issue, connection timeout, schema mismatch).

Answer

Recreate the Redshift table with a different distribution style.

Answer

Test the job with a small sample dataset to isolate the issue.

Answer

Update the Redshift JDBC driver version in the Glue job.

Question 4

A data engineer needs to monitor the number of records processed by an AWS Glue ETL job and send an alert if the count drops below a threshold. Which AWS service should be used to create this custom metric?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the correct service for creating custom metrics because it allows you to publish your own data points, such as the number of records processed by an AWS Glue ETL job. You can use the CloudWatch PutMetricData API or the AWS Glue job script to emit a custom metric, then set an alarm on that metric to trigger an alert when the count drops below a threshold.

Answer

Amazon S3

Answer

AWS Config

Answer

AWS CloudTrail

Question 5

Refer to the exhibit. A data engineer runs two queries on an Athena table partitioned by 'ds'. Both queries scan the same amount of data. What does this indicate?

Accepted Answer

The partition column is not being used as a filter. If two queries that filter on different partition values scan the same amount of data, it means the partition column is not being used as a filter in the WHERE clause — Athena is scanning all partitions instead of pruning to the relevant ones. Partition pruning only occurs when the query includes a predicate on the partition column (e.g., WHERE ds = '2024-01-01'), so equal scan sizes across different filters indicate the predicate is missing or not applied.

Answer

The table does not have any partitions defined

Answer

The table is not partitioned

Answer

Partition pruning is working correctly

Question 6

A company's Amazon Redshift cluster is running slowly. The data engineer suspects that table design is the cause. Which TWO design practices can improve query performance? (Choose TWO.)

Accepted Answer

Define appropriate sort keys on frequently filtered columns.. Option A is correct because defining an appropriate sort key on columns that are frequently used in WHERE filters (especially range and equality predicates) lets Redshift use zone maps to skip scanning irrelevant blocks, dramatically reducing I/O and improving query performance. Option C is correct because choosing an appropriate distribution key (for example, DISTRIBUTING BY the join column) collocates matching rows on the same slice, enabling collocated joins that avoid expensive data redistribution (broadcast or shuffle) across nodes during query execution. Option B is not a table design practice and, while DISTINCT and GROUP BY can differ in execution, it does not address the suspected table-design cause. Option D is incorrect because the number of slices per node is determined by the node type and cannot be changed by resizing; resizing changes node count or type, not slices per node. Option E is incorrect because CHAR and VARCHAR have essentially equivalent performance in Redshift, and CHAR is not a cause of the reported slowness.

Answer

Use GROUP BY instead of DISTINCT in queries.

Answer

Increase the number of slices per node by resizing the cluster.

Answer

Use VARCHAR instead of CHAR for fixed-length strings.

Question 7

A data engineer is troubleshooting an AWS Glue ETL job that fails intermittently with the error 'Rate exceeded.' The job reads from an Amazon RDS for MySQL source and writes to Amazon S3. What is the MOST likely cause of this error?

Accepted Answer

The number of Glue job workers or parallel queries is exceeding the maximum connections or IOPS of the RDS instance.. The 'Rate exceeded' error in AWS Glue typically occurs when the job opens too many concurrent connections to the source database. With RDS for MySQL, each Glue worker or parallel read task establishes a connection, and exceeding the instance's max_connections or IOPS limits triggers throttling. This is the most likely cause when reading from RDS and writing to S3.

Answer

The Glue job is using Amazon Kinesis Data Streams as a source, which has a shard throughput limit.

Answer

The Amazon S3 bucket has a bucket policy that limits the number of objects written per second.

Answer

The IAM role attached to the Glue job does not have sufficient permissions to read from RDS.

Question 8

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 9

A company runs an Amazon EMR cluster with Spark jobs that process data from Amazon S3. The data engineer receives an alert that one of the Spark jobs failed with an OutOfMemoryError. The job processes large files and uses the default Spark configurations. Which configuration change is MOST likely to resolve the issue?

Accepted Answer

Increase the spark.executor.memory configuration.. An OutOfMemoryError in Spark typically occurs when an executor's JVM heap is exhausted during processing of large partitions or files. Increasing spark.executor.memory directly expands the heap available to each executor, allowing it to hold more data in memory and resolve the OOM condition.

Answer

Increase the number of executors.

Answer

Disable dynamic resource allocation.

Answer

Decrease the number of cores per executor.

Question 10

A data engineer needs to move data from an Amazon S3 bucket to an Amazon Redshift cluster on a daily schedule. The data is in CSV format and the target table already exists. Which AWS service should the engineer use to automate this task?

Accepted Answer

AWS Glue. AWS Glue is a fully managed extract, transform, and load (ETL) service that can schedule and run jobs to move data from S3 to Redshift. It provides built-in connectors for both S3 and Redshift, and you can define a crawler to infer the schema and a job to load the data into the existing Redshift table. Glue handles the scheduling, retries, and scaling, making it the ideal choice for this daily automated task.

Answer

Amazon Athena

Answer

Amazon EMR

Answer

Amazon Kinesis Data Analytics

Question 11

A company uses Amazon S3 to store sensitive data. The data engineer needs to ensure that all data in transit between the S3 bucket and clients is encrypted. Which configuration should the engineer implement?

Accepted Answer

Create an S3 bucket policy that denies requests where SecureTransport is false.. An S3 bucket policy that denies requests where SecureTransport is false enforces HTTPS for all access, encrypting data in transit. Option A is wrong because CloudFront with SSL can enforce HTTPS but is not a direct S3 configuration and may incur additional costs. Option B is wrong because SSE-S3 only encrypts data at rest, not in transit. Option D is wrong because SSE-C also encrypts data at rest with a customer-provided key, not in transit.

Answer

Use Amazon CloudFront to serve the content and enable SSL.

Answer

Enable default encryption on the S3 bucket using SSE-S3.

Answer

Use SSE-C to encrypt the data with a customer-provided key.

Question 12

A company runs a data pipeline that uses AWS Lambda to process files uploaded to an S3 bucket. Recently, some files have been processed multiple times. The Lambda function is triggered by S3 event notifications. What is the MOST likely cause of duplicate processing?

Accepted Answer

S3 event notifications are delivered at least once.. S3 event notifications are delivered using an at-least-once delivery model, meaning the same event can be delivered more than once, which causes Lambda to be invoked multiple times for the same object. This is the most likely cause of duplicate processing when the function itself is not idempotent. The root cause is the delivery guarantee, not the function's error handling.

Answer

The Lambda function has a high error rate and retries.

Answer

The Lambda function is not idempotent.

Answer

The Lambda function has a reserved concurrency setting.

Question 13

A data engineer needs to set up a data catalog for a new data lake in AWS Glue. The data resides in S3 in Parquet format. The engineer wants to ensure that the schema is automatically detected and updated when new columns are added to the data. Which configuration should the engineer use?

Accepted Answer

Configure the crawler's 'Schema updates' option to 'Update the table schema'.. The Glue crawler's 'Schema updates' option controls what happens when the crawler detects schema changes in the data. Setting it to 'Update the table schema' allows the crawler to add new columns and modify the existing table definition in the Data Catalog when new columns appear in the Parquet files. This is the correct configuration for automatic schema evolution.

Answer

Add a partition index to the Glue Data Catalog table.

Answer

Set the crawler's 'Database' output to a new database.

Answer

Enable partition indexing on the table.

Question 14

A data engineer has set up an AWS Lambda function that processes files uploaded to an S3 bucket. The function is triggered by S3 event notifications. However, the function is not being invoked when a file is uploaded. The engineer checks the Lambda function's CloudWatch Logs and finds no execution logs. What should the engineer check FIRST?

Accepted Answer

Verify that the S3 bucket has an event notification configured for the Lambda function.. The symptom is that the Lambda function is not invoked at all and there are no CloudWatch execution logs — meaning the function never ran. The first thing to verify is whether the S3 bucket actually has an event notification configured to trigger the Lambda function, since without that configuration no invocation occurs. This is the most direct cause of 'no invocation, no logs.'

Answer

Check the Lambda function's code for errors.

Answer

Verify that the Lambda function's IAM role has permissions to read from S3.

Answer

Check if the Lambda function is attached to a VPC.

Question 15

A data engineer is designing a data pipeline that ingests streaming data from an IoT device fleet. The data must be processed in near real-time and stored in Amazon S3 for long-term analytics. Which TWO AWS services should the engineer use together to achieve this?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams (D) is correct because it provides a highly scalable, low-latency ingestion service for real-time streaming data from thousands of IoT devices, allowing custom consumers to process records in near real-time. Amazon Kinesis Data Firehose (C) is correct because it can consume that stream (or receive data directly) and reliably deliver it in near real-time to Amazon S3 for long-term analytics, handling batching, compression, and format conversion. Together they form the canonical AWS pattern for real-time IoT ingestion plus durable S3 storage. Amazon Athena (A) is only a query service over S3 and does not ingest or process streaming data. AWS Glue (B) is a serverless ETL/catalog service, not a real-time streaming ingestion or delivery mechanism. Amazon SQS (E) is a message queue for decoupling applications, not designed for high-throughput real-time streaming ingestion into S3.

Answer

Amazon Athena

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)