DEA-C01 Data Operations and Support Practice Test 22 — 15 Questions

Question 1

A data engineer is troubleshooting a slow-running Amazon Athena query on a large dataset stored in S3. The query scans many small files. Which TWO actions can improve query performance?

Accepted Answer

Concatenate small files into larger files. Option C is correct because Athena performance is heavily degraded by many small files, since each file incurs overhead for opening, listing, and reading metadata; concatenating small files into larger files (typically 128 MB or more) reduces this per-file overhead and lets Athena scan data more efficiently. Option D is correct because partitioning the data by a frequently filtered column allows Athena to use partition pruning, so it reads only the relevant S3 prefixes instead of scanning the entire dataset, dramatically reducing the amount of data scanned and query time. Option A is incorrect because adding more small files increases metadata and open/close overhead rather than improving performance, even though parallelism is a factor. Option B is incorrect because S3 server-side encryption is transparent to Athena and disabling it does not affect query performance. Option E is incorrect because converting CSV to JSON does not inherently reduce scanned data or file count and JSON is typically more verbose, so it would not improve performance.

Answer

Increase the number of files to increase parallelism

Answer

Disable S3 server-side encryption

Answer

Convert files from CSV to JSON

Question 2

A company is running a Redshift cluster and wants to improve query performance for a frequently used dashboard. Which THREE approaches are recommended?

Accepted Answer

Apply column compression encoding. Option B is correct because applying column compression encoding reduces the amount of data read from disk, which lowers I/O and speeds up scans for dashboard queries. Option C is correct because defining sort keys on columns used in WHERE clauses enables zone maps to skip irrelevant blocks, dramatically reducing the data scanned for filtered queries. Option E is correct because choosing an appropriate distribution key for large tables colocates related rows on the same node slice, minimizing data movement during joins and aggregations common in dashboards. Option A is not among the marked answers, and concurrency scaling mainly handles concurrent workload spikes rather than improving single-query performance. Option D is not marked because simply adding nodes increases capacity and cost but does not by itself optimize query execution without proper sort, distribution, and encoding design.

Answer

Enable concurrency scaling

Answer

Add more nodes to the cluster

Question 3

A data engineer is setting up a data pipeline using AWS Glue. The engineer wants to monitor job failures and receive notifications. Which TWO services can be used together for this purpose?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch (B) is correct because AWS Glue automatically publishes job metrics and logs to CloudWatch, and you can create CloudWatch alarms that trigger when a Glue job fails (for example, on the glue.driver.aggregate.numFailedTasks metric or a FAILED job run state). Amazon SNS (C) is correct because it is the notification service that CloudWatch alarms target via an SNS topic, delivering email, SMS, or HTTP notifications to subscribers when a Glue job failure alarm fires. Together, CloudWatch detects the failure and SNS delivers the alert, which is the standard AWS pattern for Glue job failure notifications. AWS Step Functions (A) can orchestrate Glue jobs but is not a monitoring/notification service, Amazon Kinesis Data Streams (D) is for real-time streaming ingestion, and Amazon SQS (E) is a message queue for decoupling applications, so none of these provide the alarm-and-notify capability required here.

Answer

AWS Step Functions

Answer

Amazon Kinesis Data Streams

Answer

Amazon SQS

Question 4

Refer to the exhibit. A data engineer runs the command on an object in S3. The engineer expected the object to have a tag 'type=raw' but sees no metadata. What is the likely cause?

Accepted Answer

Object tags are not returned by head-object; use get-object-tagging instead. The head-object command does not return object tags; you must use the get-object-tagging command to retrieve tags. Option B is incorrect because the head-object command succeeds regardless of region, and region does not affect tag visibility. Option C is incorrect because bucket policies can deny access but do not prevent tags from being returned by head-object; they would affect get-object-tagging instead. Option D is incorrect because lifecycle rules do not remove tags from objects; they may transition or expire objects but do not strip metadata.

Answer

The S3 bucket is in a different AWS Region

Answer

The bucket policy blocks reading tags

Answer

The object was created without tags because of lifecycle rules

Question 5

A data engineer needs to automate the backup of an Amazon RDS for PostgreSQL database. Which AWS service can be used to schedule and manage the backups?

Accepted Answer

AWS Backup. AWS Backup is a fully managed backup service that can automate backups of RDS databases. Option A (Amazon S3) is incorrect because S3 is an object storage service, not a backup scheduling service. Option B (AWS Lambda) is incorrect because while Lambda can be used for custom automation, it is not the primary managed service for backup scheduling. Option D (Amazon CloudWatch) is incorrect because CloudWatch is for monitoring and observability, not backup management.

Answer

Amazon S3

Answer

AWS Lambda

Answer

Amazon CloudWatch

Question 6

A data engineer is troubleshooting a slow-running Amazon Athena query. The query scans a large amount of data. Which TWO actions can improve query performance? (Choose TWO.)

Accepted Answer

Convert the data to Parquet or ORC format.. Option A is correct because converting data to columnar formats like Parquet or ORC lets Athena read only the columns referenced in the query and benefits from compression and predicate pushdown, drastically reducing the bytes scanned and thus improving performance and lowering cost. Option D is correct because partitioning the table on frequently filtered columns (e.g., date or region) enables partition pruning, so Athena skips scanning irrelevant partitions instead of reading the entire dataset. Option B is incorrect because encryption at rest protects stored data but does not reduce the amount of data scanned or speed up query execution. Option C is incorrect because increasing the query timeout only allows a slow query to run longer; it does not make the query faster. Option E is incorrect because using SELECT * retrieves all columns, which increases the data scanned and worsens performance, especially with columnar formats.

Answer

Enable encryption at rest.

Answer

Increase the Athena query timeout.

Answer

Use SELECT * to retrieve all columns.

Question 7

A company is experiencing high costs from Amazon Redshift. The data engineer wants to optimize costs. Which THREE actions should the engineer take? (Choose THREE.)

Accepted Answer

Right-size the cluster based on workload analysis.. Option B is correct because right-sizing the cluster based on actual workload analysis ensures you are not paying for compute and storage capacity that the workload does not consume, directly reducing Redshift costs. Option D is correct because purchasing Reserved Instances for steady-state (predictable, always-on) workloads provides a significant discount over on-demand pricing for the same node usage. Option E is correct because Concurrency Scaling adds transient capacity only when queries queue, and pairing it with a usage limit caps how much extra concurrency-scaling compute can be billed, preventing runaway costs. Option A is not correct because increasing automated snapshot frequency does not reduce cost and can actually increase storage charges for retained snapshots. Option C is not correct because adding more nodes increases both compute and storage cost, which is the opposite of cost optimization unless justified by a genuine performance need.

Answer

Increase the frequency of automated snapshots.

Answer

Increase the number of nodes to improve performance.

Question 8

A data engineer notices that an AWS Glue ETL job processing data from Amazon S3 to Amazon Redshift has been failing intermittently with the error 'S3ServiceException: SlowDown'. Which action is MOST likely to resolve this issue?

Accepted Answer

Implement exponential backoff and retry logic in the Glue job.. The 'S3ServiceException: SlowDown' error indicates that the AWS Glue job is making requests to Amazon S3 at a rate that exceeds the bucket's request rate limits. Implementing exponential backoff and retry logic (option C) is the most effective solution because it reduces the effective request rate by introducing delays between retries, allowing S3 to recover from throttling. Option A is incorrect because increasing partitions would likely increase the number of concurrent requests, exacerbating throttling. Option B is incorrect because switching to a larger worker type does not affect the rate of S3 requests. Option D is incorrect because S3 Transfer Acceleration improves network transfer speed but does not reduce request throttling.

Answer

Increase the number of partitions in the Glue job to parallelize reads.

Answer

Switch from a Standard to a G.2X large Glue worker type.

Answer

Enable S3 Transfer Acceleration on the source bucket.

Question 9

A data engineer needs to ensure that data in an Amazon S3 bucket is not publicly accessible. Which TWO measures should the engineer implement? (Choose TWO.)

Accepted Answer

Attach a bucket policy that denies access to 'Principal': '*' unless specific conditions are met.. Option A is correct because an S3 bucket policy with an explicit Deny for 'Principal': '*' (optionally scoped by conditions such as aws:SourceIp or aws:SourceVpce) overrides any Allow and prevents anonymous or public access to the bucket's objects. Option C is correct because S3 Block Public Access provides account-level and bucket-level controls (BlockPublicAcls, IgnorePublicAcls, BlockPublicPolicy, RestrictPublicBuckets) that reject public ACLs and public bucket policies, which is the primary AWS safeguard against accidental public exposure. Option B is incorrect because a lifecycle policy only transitions or expires objects and has no effect on access permissions. Option D is incorrect because S3 Versioning only retains multiple object versions for recovery and does not restrict who can read the data. Option E is incorrect because default encryption protects data at rest but does not prevent public read access to the bucket or its objects.

Answer

Create a lifecycle policy to delete objects after 30 days.

Answer

Enable S3 Versioning on the bucket.

Answer

Enable default encryption on the bucket.

Question 10

A data engineer notices that an AWS Glue ETL job is failing with an OutOfMemory error when processing a large dataset. The job uses a Standard worker type. Which action is MOST effective to resolve this issue without changing the job script?

Accepted Answer

Change to G.2X worker type. The most effective action is to switch to the G.2X worker type (Option C). AWS Glue worker types determine the vCPU, memory, and disk allocated per worker. Standard and G.1X both provide 4 vCPU and 16 GB memory per worker (1 DPU); G.1X does not increase memory over Standard, only disk. G.2X doubles resources to 8 vCPU and 32 GB memory (2 DPU), directly resolving the OutOfMemory error without changing the script. Option A increases the number of workers but not per-worker memory. Option B (G.1X) does not increase memory, so it is not effective. Option D is invalid because DPUs per worker is not a configurable parameter; it is defined by the worker type.

Answer

Increase the number of workers

Answer

Switch to G.1X worker type

Answer

Increase the number of DPUs per worker

Question 11

A company uses AWS DMS to migrate a 2 TB Oracle database to Amazon RDS for PostgreSQL. The migration completes successfully, but data validation shows some tables have missing rows. The task is configured for ongoing replication using change data capture (CDC). What is the MOST likely cause of the missing rows?

Accepted Answer

Source tables missing primary keys. AWS DMS requires a primary key or unique index on source tables to reliably identify and apply row changes during CDC. Without a primary key, DMS cannot uniquely match rows for updates and deletes, and it may also fail to capture all inserts during the initial load plus CDC handoff, resulting in missing rows.

Answer

Source database archive log retention period too short

Answer

Large objects (LOBs) not supported by the target

Answer

Insufficient storage on the DMS replication instance

Question 12

A team uses Amazon Kinesis Data Analytics to process streaming data. They notice that the application's output is delayed. Which AWS service can be used to monitor the application's performance and identify bottlenecks?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the native monitoring service for AWS resources and applications, and Kinesis Data Analytics (now Amazon Managed Service for Apache Flink) publishes metrics such as millisBehindLatest, KPU utilization, and input/output throughput directly to CloudWatch. These metrics let the team pinpoint whether the delay stems from input backlog, insufficient KPUs, or downstream sink throttling. CloudWatch alarms and dashboards then provide the operational visibility needed to remediate the bottleneck.

Answer

AWS CloudTrail

Answer

Amazon Athena

Answer

AWS X-Ray

Question 13

A data engineer is troubleshooting an AWS Glue job that reads from an Apache Kafka topic using a Glue connector. The job fails with 'TimeoutException'. The Kafka cluster is in a VPC. Which step should the engineer take FIRST?

Accepted Answer

Check the security group and network ACLs associated with the Glue job's VPC.. A TimeoutException from a Glue job reading Kafka in a VPC almost always indicates a network connectivity problem between the Glue job's ENIs and the Kafka brokers. The first step is to verify that the security groups and network ACLs allow traffic on the Kafka ports (typically 9092/9093) between the Glue job's subnet and the Kafka cluster's subnet. This is the most common root cause and the fastest to check.

Answer

Increase the Kafka consumer session timeout.

Answer

Update the Glue connector to the latest version.

Answer

Change the Glue job type from Spark to Python Shell.

Question 14

A company stores sensitive data in Amazon S3. To meet compliance requirements, they need to ensure that any data older than 1 year is automatically moved to a lower-cost storage class. Which S3 feature should they use?

Accepted Answer

S3 Lifecycle policies. S3 Lifecycle policies let you define rules that automatically transition objects to a lower-cost storage class (e.g., S3 Standard-IA, S3 Glacier Instant Retrieval, S3 Glacier Flexible Retrieval) after a specified age, such as 365 days. This is the native, declarative mechanism for age-based storage class transitions and satisfies the compliance requirement without custom code.

Answer

S3 Replication

Answer

S3 Glacier

Answer

S3 Intelligent-Tiering

Question 15

A data engineer is troubleshooting a failed AWS Glue job that writes results to Amazon S3. The error log shows 'AccessDenied' when trying to list the bucket. Which IAM policy statement should the engineer add to the Glue job's role?

Accepted Answer

s3:ListBucket. Listing a bucket requires the s3:ListBucket permission. Option B is wrong because s3:PutObject is for writing, not listing. Option C is wrong because s3:DeleteObject is not needed. Option D is wrong because s3:GetObject is for reading objects, not listing.

Answer

s3:PutObject

Answer

s3:DeleteObject

Answer

s3:GetObject