DEA-C01 Practice Test 47 — 15 Questions

Question 1

A data engineer has this IAM policy attached to their user. They are trying to create an Amazon EMR cluster with a custom service role 'EMR_CustomRole'. What will happen?

Accepted Answer

The cluster creation will fail with an 'Access Denied' error for iam:PassRole.. The IAM policy allows iam:PassRole only for the specific role 'EMR_DefaultRole'. When creating an EMR cluster with a custom service role 'EMR_CustomRole', the user needs to pass that role, but the policy does not grant iam:PassRole for 'EMR_CustomRole'. Therefore, the cluster creation fails with an 'Access Denied' error for iam:PassRole. Other options are incorrect: A incorrectly attributes failure to elasticmapreduce:* being too broad; B incorrectly assumes success because elasticmapreduce:* is allowed, ignoring the PassRole requirement; D incorrectly states PassRole is not required.

Answer

The cluster creation will fail because elasticmapreduce:* is too broad.

Answer

The cluster creation will succeed because elasticmapreduce:* is allowed.

Answer

The cluster creation will succeed because PassRole is not required for EMR.

Question 2

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an S3 bucket. The application is consistently running out of memory and failing. The operator has already increased the Parallelism and TaskManager memory. What is the next BEST step to troubleshoot?

Accepted Answer

Enable Apache Flink metrics in Amazon CloudWatch to monitor heap and checkpoint details. Enabling Apache Flink metrics in Amazon CloudWatch provides visibility into heap usage, checkpoint sizes, and backpressure, which can help diagnose the root cause of memory failures. After increasing parallelism and TaskManager memory without success, the next best step is to monitor these metrics to identify the specific bottleneck. Option A changes processing semantics from exactly-once to at-least-once, which can reduce overhead but does not help diagnose the memory issue and may alter data delivery guarantees. Option B reduces the number of shards in the source stream, which decreases throughput and might not address the underlying memory consumption. Option D increases the buffer timeout for the S3 sink, which could accumulate more data in memory before writing, potentially worsening the memory problem.

Answer

Change the processing mode from exactly-once to at-least-once

Answer

Reduce the number of shards in the source stream

Answer

Increase the buffer timeout for the S3 sink

Question 3

A company uses Amazon Athena to query data in an S3 bucket. A data engineer notices that a query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Error opening Hive split s3://bucket/path/file.parquet (Path does not exist)'. However, the file exists in S3. What is the most likely cause?

Accepted Answer

Another process deleted the file after Athena listed the files but before reading.. The error 'Path does not exist' occurs when Athena has already listed the files in the table's location and then tries to read a specific file that was deleted after the listing. This is a race condition caused by concurrent operations. Option A is incorrect because an incomplete multipart upload would not cause a path-not-found error; the file would simply not be visible or be incomplete. Option B is incorrect because outdated Glue metadata would cause schema mismatches or table not found errors, not a missing file path. Option C is incorrect because a bucket policy denying access would result in an Access Denied error, not 'Path does not exist'.

Answer

The file was uploaded using S3 multipart upload and is incomplete.

Answer

The table's metadata in the Glue Data Catalog is outdated.

Answer

The S3 bucket has a bucket policy that denies access to the Athena principal.

Question 4

A company runs an Amazon EMR cluster processing data from S3. The data engineer notices that the cluster's task nodes are underutilized while core nodes are fully utilized. Which TWO steps should the engineer take to improve resource utilization?

Accepted Answer

Increase the number of core nodes.. The core nodes are fully utilized, indicating a need for more processing capacity. Increasing the number of core nodes (Option B) adds more resources for HDFS and processing. Adding task nodes with Spot Instances (Option C) provides additional compute capacity without increasing HDFS storage, offloading work from core nodes. Option A (consolidating tasks) might reduce overhead but does not add capacity; Option D (reducing core nodes) would worsen utilization; Option E (moving HDFS from EBS to instance store) is not related to utilization and risks data loss.

Answer

Consolidate multiple small tasks into larger tasks.

Answer

Reduce the number of core nodes and increase the number of task nodes.

Answer

Move HDFS data from EBS to instance store volumes.

Question 5

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 error 'cannot resolve column_name' indicates that the Spark SQL query is trying to reference a column that does not exist in the schema provided by the AWS Glue Data Catalog. This typically happens when the source data schema has changed (e.g., column renamed or dropped) but the Data Catalog schema is not updated accordingly. Option B is incorrect because a corrupted file would cause a read or parse error, not a schema resolution error. Option C is incorrect because an IAM permissions issue would result in an AccessDenied error. Option D is incorrect because a data type mismatch would cause a type casting error, not a 'cannot resolve' error which is about column names.

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 6

A company uses Amazon DynamoDB as the primary data store for a high-traffic application. Recently, read latency has increased significantly. The DynamoDB table has on-demand capacity mode. Which action is MOST effective to reduce read latency?

Accepted Answer

Add a DynamoDB Accelerator (DAX) cluster in front of the table. DynamoDB Accelerator (DAX) is an in-memory cache that significantly reduces read latency for frequent reads, especially for high-traffic applications. Option B is incorrect because switching to provisioned capacity does not inherently reduce latency; it only manages throughput. Option C is incorrect because on-demand capacity mode does not use read capacity units or auto scaling settings for read capacity adjustments. Option D is incorrect because Global Tables replicate data across regions for disaster recovery and global access, but do not reduce read latency for reads in the same region.

Answer

Switch the table to provisioned capacity mode with higher read capacity

Answer

Increase the read capacity units in the table's auto scaling settings

Answer

Enable DynamoDB Global Tables to distribute reads across regions

Question 7

A data engineer is setting up a data pipeline to ingest streaming data from an IoT fleet. The data must be processed in near real-time and stored in Amazon S3 for analytics. Which THREE AWS services should the engineer consider using?

Accepted Answer

Amazon Kinesis Data Firehose. The correct services are Amazon Kinesis Data Streams (option E) for real-time data ingestion, AWS Lambda (option C) for near real-time processing, and Amazon Kinesis Data Firehose (option B) for delivering data to S3. Options A and D are incorrect because Amazon EMR is a big data processing framework not optimized for real-time streaming ingestion, and AWS Glue is primarily a batch ETL service.

Answer

Amazon EMR

Answer

AWS Glue

Question 8

Refer to the exhibit. A company has an S3 bucket 'my-data-lake' with the lifecycle policy shown. Objects under the 'logs/' prefix are being moved to GLACIER after 30 days and expire after 365 days. A data engineer notices that objects older than 365 days are still present in the bucket and are not being deleted. What is the most likely cause?

Accepted Answer

The expiration days count from the transition date, not the object creation date. When a lifecycle rule includes both transition to GLACIER and expiration, the expiration days count from the transition date, not from the object creation date. In this scenario, objects transition to GLACIER at 30 days, so the expiration at 365 days actually triggers after 395 days from creation (30 + 365). Therefore, objects older than 365 days from creation but younger than 395 days are not yet expired, explaining why they remain.

Answer

Lifecycle expiration does not apply to objects in GLACIER storage class

Answer

The rule status is disabled

Answer

The prefix filter does not match the objects

Question 9

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' indicates that the Oracle database's archive log area is full. AWS DMS uses Change Data Capture (CDC) which reads from archive logs to replicate changes. Increasing archive log space or reducing log retention prevents this error. Option A is incorrect because distributing load across multiple S3 buckets does not address source log space. Option C is incorrect because DMS does not manage archiving; it only reads logs. Option D is incorrect because memory on the replication instance does not affect source archive log space.

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 10

A data engineer is monitoring an Amazon Redshift cluster and notices that queries are taking longer than expected. The engineer checks the system tables and sees that many queries are waiting for 'WLM' resources. What is the most likely cause and recommended fix?

Accepted Answer

The WLM queue concurrency is set too low; increase the concurrency level.. A wait for WLM (Workload Management) resources indicates that queries are being throttled due to insufficient concurrency slots. Increasing the WLM queue concurrency allows more queries to run simultaneously. Option A is incorrect because sort keys affect query scan efficiency, not WLM queue waits. Option B is incorrect because distribution style affects data redistribution, not concurrency throttling. Option D is incorrect because low disk space would cause different errors, not specifically WLM queue waits.

Answer

The table sort keys are poorly designed; recreate tables with better sort keys.

Answer

The distribution style is set to ALL; change to KEY distribution.

Answer

The cluster is running low on disk space; resize the cluster.

Question 11

A company is migrating its on-premises MySQL database to Amazon RDS for MySQL. They want to minimize downtime and ensure data consistency. Which AWS service should be used for the migration?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it is specifically designed for migrating databases to AWS with minimal downtime. DMS supports homogeneous migrations like MySQL to Amazon RDS for MySQL, and it uses ongoing replication (change data capture) to keep the source and target databases in sync during the migration, ensuring data consistency and allowing a cutover with only seconds of downtime.

Answer

AWS S3 Transfer Acceleration

Answer

AWS Glue

Answer

AWS Snowball Edge

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.. Creating a read replica offloads read queries from the primary instance, reducing read latency with minimal application changes. The application simply needs to direct read-only queries to the replica endpoint. Option A is incorrect because deleting unused indexes may help write performance but does not directly address high read latency during peak hours. Option B is incorrect because enabling Multi-AZ is for high availability and failover, not for improving read performance. Option C is incorrect because increasing the DB instance class can improve performance but is more disruptive and costly compared to adding a read replica, and may require downtime for resizing.

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 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 'ProvisionedThroughputExceededException' occurs when the Lambda function exceeds the DynamoDB table's write capacity. To resolve this without data loss, the data engineer should both increase the DynamoDB write capacity to accommodate the throughput and configure a dead-letter queue (DLQ) on the Lambda function. The DLQ captures records that fail after all retries, preventing data loss. Option A (reducing shards) would lower the ingestion rate but may cause data loss and does not address the root cause. Option B (increasing read capacity) is irrelevant because the issue is with writes. Option D (disabling retries) would cause data loss because failed records would not be retried.

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 14

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 generic and does not specify the root cause. The first troubleshooting step should be to review the job's CloudWatch Logs, which provide detailed error messages, stack traces, and other diagnostic information. Option A is incorrect because recreating the table with a different distribution style is a premature action without understanding the cause. Option B is incorrect because testing with a small dataset may not reproduce the issue and is not the first step; logs can help determine if the issue is data-related. Option C is incorrect because updating the Redshift JDBC driver version is not the first step; log analysis should precede any changes.

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 15

A company uses Amazon Kinesis Data Firehose to deliver streaming log data to an Amazon S3 bucket. The delivery stream uses dynamic partitioning with a custom prefix. Recently, the delivery stream has been failing with the error 'InvalidArgumentException: The number of partitions exceeds the limit'. What is the likely cause?

Accepted Answer

The incoming data contains more distinct partition key values than the allowed limit.. The error 'InvalidArgumentException: The number of partitions exceeds the limit' occurs when Kinesis Data Firehose dynamic partitioning receives more distinct partition key values than the allowed limit (default 500 distinct values per stream). Option A correctly identifies this cause. Option B is wrong because S3 bucket policy restrictions would cause AccessDenied errors, not partition limit errors. Option C is wrong because buffer size and interval affect file size and delivery frequency, not partition count. Option D is wrong because throughput limits cause ProvisionedThroughputExceededException, not partition limit errors.

Answer

The S3 bucket has a bucket policy that restricts the number of prefixes.

Answer

The buffer size and interval are set too low, causing many small files.

Answer

The data volume exceeds the maximum throughput of the delivery stream.