DEA-C01 Data Operations and Support Practice Test 18 — 15 Questions

Question 1

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?

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

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 Option B is incorrect because outdated Glue metadata would cause schema mismatches or table not found errors, not a missing file path.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The table's metadata in the Glue Data Catalog is outdated.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 2

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket and writes to an Amazon Redshift table. The job logs show a permission error. Which IAM policy change would resolve the issue?

Accepted Answer

Add redshift:DataAPI access to the Glue job's IAM role. The Glue job's IAM role needs the redshift:DataAPI permission to write to the Redshift table via the Data API. Option A is irrelevant because enabling encryption on the S3 bucket does not resolve permission errors related to Redshift. Option B is incorrect because s3:GetObject is needed for reading from S3, but the error is about writing to Redshift, not reading. Option D is incorrect because attaching an IAM role to the Redshift cluster grants permissions to the cluster itself, not to the Glue job's role; the Glue job's role must have the redshift:DataAPI permission directly.

Answer

Enable encryption on the S3 bucket using AWS KMS

Answer

Add s3:GetObject permission to the Glue job's IAM role

Answer

Attach an IAM role with redshift:GetClusterCredentials to the Redshift cluster

Question 3

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. The consumer application is falling behind and the iterator age is increasing. Which action would most effectively improve throughput?

Accepted Answer

Increase the number of shards in the stream. Increasing the number of shards in a Kinesis data stream increases the level of parallelism for both ingestion and consumption, allowing the consumer to process more data concurrently and catch up. Option A is incorrect because Kinesis Data Firehose is a delivery stream, not a replacement for real-time consumption, and cannot solve a consumer lag issue. Option B is incorrect because decreasing batch size reduces the amount of data processed per poll, which can actually slow down throughput and increase iterator age. Option C is incorrect because enhanced fan-out is useful when multiple consumers need dedicated throughput, but it does not increase the total throughput of the stream; it simply provides each consumer with its own 2 MB/sec per shard read throughput, which may not help a single consumer that is already falling behind.

Answer

Switch from Kinesis Data Streams to Kinesis Data Firehose

Answer

Decrease the batch size in the consumer

Answer

Enable enhanced fan-out for the consumer

Question 4

A data engineer notices that an Amazon Athena query on a partitioned table in S3 scans more data than expected. The table is partitioned by year, month, day. The query includes a WHERE clause on a non-partition column but also filters on day='2023-01-01'. What is the most likely cause of the excessive data scan?

Accepted Answer

The partition column data type in the table definition does not match the actual partition folder names. The most likely cause is that the partition column data type in the table definition does not match the actual partition folder names. Athena uses the folder names to determine which partitions to scan (partition pruning). If the data type mismatch causes Athena to be unable to correctly interpret the folder names, partition pruning fails, and Athena scans all partitions, leading to excessive data scan. Option A is incorrect because JSON format does not prevent partition pruning; it may affect compression but not pruning. Option B is incorrect because the WHERE clause filters on a non-partition column, but it also filters on the partition column 'day', so partition pruning should work if the column data type matches. Option D is incorrect because sorting within partitions does not affect scan size; it affects query performance but not the amount of data scanned.

Answer

The data is stored in JSON format instead of Parquet

Answer

The table is not partitioned by the column used in the WHERE clause

Answer

The data is not sorted within partitions

Question 5

A data engineer is troubleshooting a slow Amazon Redshift query. The query plan shows a large number of 'DS_DIST_ALL_INNER' and 'DS_BCAST_INNER' operations. Which TWO actions would likely improve query performance?

Accepted Answer

Change the distribution style of large tables to KEY on the join column.. Using DISTSTYLE KEY on the join column for large tables colocates data with the same key on the same slice, reducing the need for data redistribution operations like DS_DIST_ALL_INNER and DS_BCAST_INNER. Option C is correct because increasing the number of slices by resizing the cluster distributes data across more compute nodes, allowing more parallelism and reducing the relative impact of data redistribution. Option A is incorrect because setting DISTSTYLE to ALL on both tables would broadcast each table to all nodes, increasing data movement and likely worsening performance. Option D is incorrect because SORTKEYs optimize range-restricted scans and sorting, not join data movement. Option E is incorrect because dropping and recreating tables with the same DDL does not change distribution or sort strategies, so it does not address the root cause of excessive redistribution.

Answer

Set DISTSTYLE to ALL for both tables.

Answer

Define SORTKEYs on the join columns.

Answer

Drop and recreate the tables with the same DDL.

Question 6

A data engineer needs to set up a data pipeline that ingests CSV files from an S3 bucket, transforms them using AWS Glue, and loads the results into Amazon Redshift. The pipeline must handle schema evolution and data quality checks. Which combination of services is most appropriate?

Accepted Answer

Use AWS Glue ETL jobs with Glue DataBrew for data quality and write to Redshift. AWS Glue ETL jobs can handle schema evolution through the use of Glue DynamicFrames, and Glue DataBrew provides built-in data quality checks (profiling, validation) that integrate seamlessly. Option A is incorrect because Lambda has timeout and memory limits, making it unsuitable for large-scale transformations. Option B is incorrect because Athena cannot write directly to Redshift; CTAS only writes to S3. Option C is incorrect because Kinesis Data Firehose is designed for streaming data, not batch CSV ingestion from S3.

Answer

Use S3 Events to trigger an AWS Lambda function that writes directly to Redshift

Answer

Use Amazon Athena to query data in S3 and insert results into Redshift via CTAS

Answer

Use Amazon Kinesis Data Firehose to transform and load data into Redshift

Question 7

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 8

A company is running a critical Amazon RDS for MySQL database. They need to implement a backup strategy that allows point-in-time recovery (PITR) with a recovery time objective (RTO) of 15 minutes and a recovery point objective (RPO) of 5 minutes. Which solution meets these requirements?

Accepted Answer

Enable automated backups with 1-day retention and enable Multi-AZ deployment. Automated backups enable point-in-time recovery using transaction logs that record changes every 5 minutes, achieving an RPO of 5 minutes. Multi-AZ deployment provides automatic failover to a standby instance, achieving an RTO of typically 1-2 minutes, well within the 15-minute RTO. Option B is incorrect because cross-Region automated backups replicate data to another region, but the promotion of a replica in another region can take longer than 15 minutes, and replication lag may cause RPO to exceed 5 minutes. Option C is incorrect because manual snapshots are taken at specific points in time and do not include transaction logs; even if taken every 5 minutes, restoring from a snapshot would not provide point-in-time recovery to any point within those 5 minutes, and the restore time is typically longer than 15 minutes. Option D is incorrect because a Read Replica is an asynchronous copy; promoting a read replica to a primary instance can take several minutes, and the RPO may be higher due to replication lag, thus not meeting the 5-minute RPO or 15-minute RTO.

Answer

Use cross-Region automated backups and promote the replica

Answer

Take manual snapshots every 5 minutes and restore from the latest snapshot

Answer

Enable a Read Replica and promote it during a disaster

Question 9

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 10

A data engineer is monitoring an Amazon EMR cluster and notices that one core node is running out of disk space. The cluster is running a Spark job that processes large Parquet files. What should the engineer do to prevent the issue?

Accepted Answer

Enable Snappy compression for intermediate data. Enabling Snappy compression for intermediate data reduces the volume of data written to disk during Spark shuffle operations, directly addressing the disk space issue on the core node. Snappy provides a good balance between compression ratio and speed, minimizing I/O overhead while conserving storage. This is a standard tuning practice in Amazon EMR for Spark jobs that process large Parquet files.

Answer

Terminate the core node and replace it with a larger instance type

Answer

Use Spark's in-memory processing to avoid writing intermediate data to disk

Answer

Increase the number of core nodes

Question 11

A company uses AWS DMS to migrate an on-premises Oracle database to Amazon Aurora PostgreSQL. The migration is ongoing with continuous replication. The data engineer notices that the target Aurora database has a higher lag than expected. Which action would most likely reduce the lag?

Accepted Answer

Increase the number of parallel tasks in the DMS task settings. Increasing the number of parallel tasks in a DMS task improves throughput, allowing data to be loaded faster to the target and reducing replication lag. Option A is incorrect because the S3 bucket size does not affect DMS replication performance. Option C is incorrect: while Batch Optimized Apply can reduce apply overhead on certain targets like PostgreSQL, increasing parallel tasks is a more direct and effective way to address lag. Option D is incorrect because disabling validation reduces data integrity checks and may provide only minor lag reduction, but it is not the recommended primary action.

Answer

Increase the size of the S3 bucket used for staging

Answer

Enable Batch Optimized Apply on the DMS task

Answer

Disable validation of data on the target

Question 12

A data engineer is designing a data lake on Amazon S3. The data includes sensitive personally identifiable information (PII). Which combination of services would provide the most comprehensive data protection?

Accepted Answer

Enable S3 Block Public Access and use Amazon Macie to discover and classify PII. Combining S3 Block Public Access prevents unintended public exposure, and Amazon Macie automatically discovers, classifies, and protects sensitive PII. Option A is incorrect because S3 Transfer Acceleration speeds up uploads but does not protect data; versioning helps with accidental deletion but not confidentiality or access control. Option B is incorrect because SSE-KMS only provides encryption at rest, not comprehensive protection against misconfigurations or authorized access to PII. Option C is incorrect because CloudWatch Logs monitor and audit access but do not prevent exposure or classify data; MFA Delete protects against accidental deletion but not against data leaks.

Answer

Use S3 Transfer Acceleration and enable versioning

Answer

Enable S3 server-side encryption with AWS KMS

Answer

Use Amazon CloudWatch Logs to monitor access and enable MFA Delete

Question 13

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 14

A data engineer is troubleshooting a slow-running Amazon Redshift query. The query involves a large fact table with a distribution style of EVEN and a sort key on date. The table has 10 slices. The engineer notices that the query is performing a broadcast join with a small dimension table. Which change would most improve performance?

Accepted Answer

Change the fact table to DISTSTYLE KEY on the join column. Changing the fact table’s distribution style to KEY on the join column co-locates rows from both tables on the same nodes, eliminating the need for broadcasting and reducing network traffic. This fully optimizes the join for the large fact table. Option A is incorrect: removing the sort key can degrade range queries, and a compound sort key on the join column does not address data distribution. Option B: while setting the dimension table to DISTSTYLE ALL would avoid broadcasting by replicating the table to all nodes, it does not improve the fact table’s own data distribution, which remains EVEN and can cause skew or suboptimal joins in other queries. Option C: adding slices increases parallelism but still requires broadcasting, so it does not address the root cause of the performance issue.

Answer

Remove the sort key and use a compound sort key on the join column

Answer

Change the dimension table to DISTSTYLE ALL

Answer

Increase the number of slices by resizing the cluster

Question 15

A data engineer needs to monitor the number of records processed by an AWS Glue ETL job. Which CloudWatch metric should the engineer use?

Accepted Answer

glue.driver.aggregate.numRecords. Glue emits a 'glue.driver.aggregate.numRecords' metric for the number of records processed. Option A is wrong because 'glue.driver.aggregate.elapsedTime' is for time. Option C is wrong because 'glue.driver.aggregate.bytesRead' is for bytes. Option D is wrong because 'glue.driver.aggregate.recordsRead' is not a standard metric.

Answer

glue.driver.aggregate.elapsedTime

Answer

glue.driver.aggregate.bytesRead

Answer

glue.driver.aggregate.recordsRead