DEA-C01 Data Operations and Support Practice Test 24 — 15 Questions

Question 1

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?

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

Accepted Answer

Enable Apache Flink metrics in Amazon CloudWatch to monitor heap and checkpoint details. When a Flink application on Kinesis Data Analytics runs out of memory despite increased parallelism and TaskManager memory, the next best step is to enable Apache Flink metrics in CloudWatch to observe heap usage, garbage collection, and checkpoint behavior. These metrics reveal whether the issue is memory leaks, backpressure, or state growth. Without observability, further tuning is guesswork.

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 2

A company stores sensitive customer data in an S3 bucket. The data engineer needs to ensure that all data is encrypted at rest. Which S3 feature should be enabled?

Accepted Answer

Default encryption. Default encryption ensures that all new objects written to the bucket are encrypted at rest using SSE-S3, SSE-KMS, or SSE-C. Option A is incorrect because S3 Versioning tracks object versions but does not encrypt data. Option B is incorrect because S3 Block Public Access controls public access, not encryption. Option C is incorrect because the aws:SecureTransport condition enforces encryption in transit, not at rest.

Answer

S3 Versioning

Answer

S3 Block Public Access

Answer

Bucket policy requiring aws:SecureTransport

Question 3

A company is using Amazon Athena to query data in an S3 bucket. Queries are failing with the error 'HIVE_PATH_ALREADY_EXISTS'. The data is partitioned by year, month, day. What is the MOST likely cause?

Accepted Answer

A partition was manually added to the Glue Data Catalog that already exists. The error 'HIVE_PATH_ALREADY_EXISTS' occurs in Athena when attempting to add a partition (via ALTER TABLE ADD PARTITION or MSCK REPAIR TABLE) that already exists in the Glue Data Catalog. Option B (inconsistent data format) would cause schema mismatch errors like 'HIVE_PARTITION_SCHEMA_MISMATCH', not this error. Option C (empty S3 location) would not cause this error; queries might succeed but return no results. Option D (lack of s3:ListBucket permission) would cause permission errors like 'Access Denied'.

Answer

The data format in the partition is inconsistent with the table schema

Answer

The S3 location for the partition is empty

Answer

The IAM role used by Athena lacks s3:ListBucket permission on the bucket

Question 4

Refer to the exhibit. An IAM policy is attached to an IAM role used by an application. The application needs to read objects from 'my-bucket' that have the tag 'classification=public'. The application account is 123456789012. However, the application is getting 'Access Denied' errors. What is the most likely reason?

Accepted Answer

The object being accessed does not have the tag 'classification=public'.. The most likely reason for Access Denied is that the object being accessed does not have the tag 'classification=public'. The policy's Allow statement is conditioned on s3:ExistingObjectTag/classification equaling 'public', so if the tag is missing or has a different value, the Allow does not match and the request is denied. The Deny statement in the policy is a separate guardrail and is not the cause here.

Answer

The Deny statement uses StringNotEquals, which incorrectly denies the application account.

Answer

The policy does not grant s3:ListBucket permission, so the application cannot list objects.

Answer

The Deny statement blocks all access from accounts other than 123456789012, but the application is in that account.

Question 5

Which TWO actions should a data engineer take to optimize Amazon S3 query performance for Amazon Athena when dealing with large Parquet files? (Choose 2.)

Accepted Answer

Optimize file sizes to be around 64 MB to 256 MB. Option D is correct because Athena's performance degrades with very large Parquet files since a single file can only be read by one split; targeting roughly 64 MB to 256 MB per file allows Athena's split-based parallel reads to distribute work across more nodes. Option E is correct because partitioning data by frequently filtered columns (e.g., date, region) enables partition pruning, so Athena scans only the relevant S3 prefixes instead of the entire dataset, drastically reducing bytes scanned and query time. Option A is wrong because a single large unpartitioned file forces full-table scans and prevents parallelism. Option B is wrong because Parquet already uses columnar compression internally, and GZIP is not splittable, which can actually hurt parallelism for large files. Option C is wrong because splitting into many tiny files creates excessive per-file overhead (metadata, open/close costs) and degrades Athena performance rather than improving it.

Answer

Store data in a single large file without partitioning

Answer

Use GZIP compression on the Parquet files

Answer

Split large files into many small files

Question 6

A data engineer is monitoring an Amazon Kinesis Data Analytics application that processes real-time clickstream data. The application uses a Flink application with multiple operators. The engineer notices that the 'millisBehindLatest' metric is increasing steadily. Which action is MOST likely to reduce the lag?

Accepted Answer

Increase the parallelism of the Flink application.. Increasing the parallelism of the Flink application allows more parallel subtasks to process the incoming data, thereby increasing throughput and reducing the lag (millisBehindLatest). This is the most direct way to scale the application to handle higher load. Other options do not address the root cause of increasing lag.

Answer

Decrease the batch size in the Flink application.

Answer

Switch the source stream to use GZIP compression.

Answer

Increase the retention period of the Kinesis stream.

Question 7

A data engineer is tasked with designing a disaster recovery solution for a data lake stored in Amazon S3. The data lake contains sensitive customer data that must be replicated to a different AWS Region. The engineer needs to ensure that all objects, including those with encryption using SSE-KMS, are replicated. Which solution meets the requirements?

Accepted Answer

Enable S3 Cross-Region Replication (CRR) with the appropriate KMS key and IAM role.. S3 Cross-Region Replication (CRR) with the appropriate KMS key and IAM role is the correct solution because it automatically replicates objects, including those encrypted with SSE-KMS, to a destination bucket in another region. To replicate SSE-KMS encrypted objects, you must specify a KMS key in the destination region and grant the necessary IAM permissions to the replication role. This meets the requirement for disaster recovery of sensitive data.

Answer

Use S3 Batch Operations to copy objects to the destination bucket.

Answer

Use S3 Transfer Acceleration to copy objects across regions.

Answer

Use the AWS CLI s3 sync command scheduled in a cron job.

Question 8

A company runs a data warehouse on Amazon Redshift. The data engineer notices that some queries are running slowly. Upon reviewing the system tables, the engineer finds that the 'svv_table_info' shows high 'unsorted' percentage for several large tables. What is the MOST effective action to improve query performance?

Accepted Answer

Run the VACUUM command on the tables.. A high 'unsorted' percentage in svv_table_info means many rows were added after the last sort and are not in sort-key order, forcing Redshift to scan more blocks and slowing range-restricted queries. VACUUM re-sorts the table and reclaims space, restoring the benefits of the sort key. This is the direct, most effective fix for the reported symptom.

Answer

Run the ANALYZE command on the tables.

Answer

Change the distribution style of the tables to ALL.

Answer

Increase the number of nodes in the Redshift cluster.

Question 9

A data engineer is designing a data pipeline that ingests data from an on-premises database into Amazon S3 using AWS Database Migration Service (DMS). The data must be encrypted at rest in S3 using SSE-S3. The engineer also needs to track changes to the source database in real time. Which DMS configuration should the engineer use?

Accepted Answer

Use DMS with ongoing replication (change data capture) enabled.. DMS with ongoing replication (change data capture) enables real-time tracking of changes from the source database. Option A is incorrect because using a snapshot only captures data at a point in time, not real-time changes. Option C is incorrect because a full load task only loads existing data without capturing ongoing changes. Option D is incorrect because streaming to Amazon Kinesis is unnecessary; DMS CDC can directly replicate changes to S3. Encryption at rest in S3 with SSE-S3 is automatically supported by DMS when writing to S3.

Answer

Use DMS with a snapshot of the source database.

Answer

Use DMS with a full load task only.

Answer

Use DMS with a full load task and then stream to Amazon Kinesis.

Question 10

A data engineer is monitoring an Amazon Redshift cluster and notices that the 'WLM query wait time' metric is consistently high during peak hours. The cluster uses automatic WLM. The engineer wants to reduce query wait times without changing the cluster size. Which action is MOST effective?

Accepted Answer

Enable concurrency scaling.. Enabling concurrency scaling (Option A) is the most effective action because it automatically adds transient cluster capacity during peak loads, allowing more queries to run concurrently without increasing wait times. This is specifically designed to reduce WLM query wait time. Option B (manual WLM) requires tuning and does not add capacity. Option C (increasing max queries per queue) could increase concurrency but may lead to resource contention and longer wait times if the cluster is already saturated. Option D (short query acceleration) prioritizes short queries, which does not address overall wait times for all queries. Therefore, A is correct.

Answer

Change WLM to manual mode and increase the number of queues.

Answer

Increase the maximum number of queries per queue.

Answer

Enable short query acceleration (SQA).

Question 11

A data pipeline uses AWS Glue ETL jobs to process data from Amazon RDS for MySQL to Amazon S3. Recently, the jobs have been failing with the error 'Communications link failure' during the connection phase. The RDS instance is in a private subnet, and the Glue job uses a VPC endpoint for S3. What is the most likely cause?

Accepted Answer

The Glue job does not have a network path to the RDS instance because it is not attached to the same VPC subnet.. The most likely cause is that the AWS Glue job does not have a network path to the RDS instance because it is not attached to the same VPC subnet. AWS Glue jobs run in a serverless environment that, by default, is outside the customer's VPC. To connect to a private RDS instance, the Glue job must be configured with a VPC connection that specifies the VPC, subnet, and security group, allowing it to reach the RDS instance. Without this, the JDBC connection fails with 'Communications link failure' during the connection phase.

Answer

The RDS database has reached the maximum number of connections.

Answer

The Glue job does not have IAM permissions to decrypt the RDS database using AWS KMS.

Answer

The JDBC driver used by Glue is incompatible with the MySQL version.

Question 12

A company runs a data pipeline that uses AWS Glue to process data from an Amazon DynamoDB table and write results to Amazon S3. The Glue job runs on a schedule every hour. Recently, the job started failing intermittently with 'ProvisionedThroughputExceededException' errors from DynamoDB. What is the BEST solution?

Accepted Answer

Implement exponential backoff and retries in the Glue job for DynamoDB operations.. ProvisionedThroughputExceededException is a throttling error that occurs when request rate exceeds the table's provisioned read/write capacity, and AWS's documented best practice is to implement exponential backoff and retry logic so the client retries with increasing delays instead of hammering the table. This is the correct fix for intermittent throttling because it lets the Glue job ride out short bursts of capacity contention without failing. It also works whether the table is in provisioned or on-demand mode, and it is the least disruptive, most cost-effective change.

Answer

Use DynamoDB Accelerator (DAX) to reduce read latency.

Answer

Change the Glue job schedule to run every 2 hours.

Answer

Increase the read capacity units of the DynamoDB table.

Question 13

A team uses Amazon Redshift for analytics. They notice that some queries are slow and the system shows high disk usage. The team wants to improve query performance without adding more nodes. Which action should they take first?

Accepted Answer

Run the VACUUM and ANALYZE commands on the tables.. VACUUM and ANALYZE are the first actions to take because they reclaim disk space from deleted rows and update table statistics, which can significantly improve query performance and reduce disk usage. Option B is incorrect because enabling compression on all columns is not a first step; compression is typically set during table creation and may not address current disk usage issues. Option C is incorrect: redistributing tables by changing the distribution key requires recreating the table, which is an invasive operation and not the first troubleshooting step. Option D is incorrect: modifying the WLM queue affects concurrency management, not disk usage or query performance directly related to high disk usage.

Answer

Enable compression on all columns.

Answer

Redistribute the tables by changing the distribution key to a column with high cardinality.

Answer

Modify the workload management (WLM) queue to increase concurrency.

Question 14

A data engineer is setting up a new Amazon Redshift cluster for a data warehouse. The engineer wants to ensure data durability and high availability. Which THREE features should the engineer consider? (Choose three.)

Accepted Answer

Cross-Region snapshot copy.. Option B (Cross-Region snapshot copy) is correct because Redshift can automatically copy cluster snapshots to an S3 bucket in another AWS Region, providing disaster recovery and durability if an entire Region becomes unavailable. Option C (Multi-node cluster with data replication) is correct because a multi-node Redshift cluster distributes data across compute nodes and maintains replicas of each block on other nodes, so the cluster can continue serving queries if a node or disk fails. Option E (Automated snapshots to Amazon S3) is correct because Redshift automatically takes snapshots of the cluster and stores them in Amazon S3, enabling point-in-time recovery and restoring the cluster if data is lost or corrupted. Option A is not correct because S3 Cross-Region Replication is an S3 feature for replicating S3 objects and is not a Redshift durability mechanism. Option D is not correct because Redshift does not offer a Multi-AZ deployment mode with automatic failover; high availability within a Region is achieved through multi-node replication and automated snapshots.

Answer

S3 Cross-Region Replication for Redshift data.

Answer

Multi-AZ deployment for automatic failover.

Question 15

A data engineer is troubleshooting an AWS Glue job that writes data to an S3 bucket. The IAM role attached to the Glue job has the policy shown in the exhibit. The job fails when writing to the 'secrets/' prefix but succeeds when writing to other prefixes. What is the reason for the failure?

Accepted Answer

The Deny statement explicitly denies PutObject to the secrets/ prefix.. The IAM policy attached to the Glue job contains an explicit Deny statement for the 'secrets/' prefix, which overrides any Allow. In AWS IAM, an explicit Deny always takes precedence over Allow. Therefore, the job fails when writing to 'secrets/' but succeeds for other prefixes where no Deny exists.

Answer

The job does not have permission to write to the bucket at all.

Answer

The resource ARN in the Allow statement does not include the bucket itself.

Answer

The Deny statement is not effective because it is placed after the Allow.