DEA-C01 Data Operations and Support Practice Test 8 — 15 Questions

Question 1

A data pipeline uses AWS Glue to process data from Amazon S3. The job fails with an 'OutOfMemoryError' during the transformation phase. Which action should the data engineer take to resolve this issue?

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

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the Glue job.. The OutOfMemoryError occurs because the Glue job does not have enough memory allocated. Increasing the number of DPUs (Data Processing Units) increases both memory and processing capacity, directly resolving the issue. Option A (S3 server-side encryption) affects data security, not memory. Option B (increasing data partitions) may help parallelism but does not directly increase memory per executor. Option C (changing to Parquet) can reduce data volume but does not guarantee sufficient memory for transformation.

Answer

Enable S3 server-side encryption.

Answer

Increase the number of partitions in the input data.

Answer

Change the data format from CSV to Parquet.

Question 2

A data engineer is monitoring an Amazon EMR cluster and notices that the cluster is running out of disk space on the core nodes. Which action can be taken to resolve this issue?

Accepted Answer

Increase the EBS volume size attached to core nodes. Increasing the EBS volume size attached to core nodes directly adds storage capacity, resolving the disk space issue. Option A is wrong because reducing HDFS data retention may free space but does not increase available disk space; it could result in data loss. Option B is wrong because changing to a compute-optimized instance type affects CPU and memory, not storage. Option D is wrong because Spot Instances are a pricing model and do not add disk space.

Answer

Reduce the retention period of data stored on HDFS

Answer

Change the core node instance type to a compute-optimized type

Answer

Use Spot Instances for core nodes

Question 3

A company runs an Amazon Redshift cluster for analytics. During peak hours, query performance degrades significantly. The data engineer notices that disk space usage is above 80% on many nodes. Which of the following is the MOST effective long-term solution to improve query performance?

Accepted Answer

Resize the cluster to include additional nodes.. Resizing the cluster to include additional nodes increases both storage and compute capacity, directly addressing the high disk usage and improving query performance. Increasing WLM queue slots (Option A) only manages concurrency but does not add capacity. Compression encoding (Option C) reduces storage but may not alleviate immediate performance degradation, and is not a long-term solution for capacity. Running VACUUM (Option D) reclaims space from deleted rows but does not add new capacity.

Answer

Increase the workload management (WLM) queue slots.

Answer

Apply compression encoding to all columns.

Answer

Run the VACUUM command to reclaim space.

Question 4

A company has an S3 data lake with millions of objects. A data engineer needs to provide a daily report of objects that are not accessed for 90 days. The engineer must minimize cost and impact on performance. Which approach should be used?

Accepted Answer

Use S3 Storage Lens to generate a dashboard of object age and last access. S3 Storage Lens provides cost-effective analytics including last access date and object age, enabling identification of objects not accessed for 90 days without scanning all objects or incurring high costs. Option A is less suitable because S3 Inventory creates daily lists but requires additional Athena queries, adding complexity and cost. Option B is incorrect because S3 Select is designed for querying object content, not metadata; applying it to millions of objects would be highly inefficient and costly. Option C is incorrect because analyzing S3 server access logs would involve storing and querying large volumes of log data, which is costly and impacts performance, and it logs all requests rather than providing a direct last access timestamp.

Answer

Enable S3 Inventory and query with Athena

Answer

Use S3 Select on each object to check last access metadata

Answer

Analyze S3 server access logs to find objects not accessed

Question 5

A data engineer needs to monitor Amazon DynamoDB table metrics to detect throttled requests. Which CloudWatch metric should the engineer set an alarm on?

Accepted Answer

ThrottledRequests. `ThrottledRequests` is the specific Amazon CloudWatch metric that tracks the number of requests to a DynamoDB table that are throttled due to exceeding the provisioned throughput capacity. This metric directly reflects throttling events, making it the appropriate choice for setting an alarm to detect throttled requests.

Answer

ReadThrottleEvents

Answer

SuccessfulRequestLatency

Answer

ConsumedWriteCapacityUnits

Question 6

A data engineer is troubleshooting a failed AWS Glue Crawler. The crawler logs show 'Insufficient permissions to access S3 bucket'. What should the engineer do to resolve this?

Accepted Answer

Update the IAM role used by the crawler to include S3 read permissions. The AWS Glue Crawler uses an IAM role to access data sources. The error 'Insufficient permissions to access S3 bucket' indicates that the IAM role attached to the crawler lacks the necessary S3 read permissions (e.g., s3:GetObject, s3:ListBucket). Updating the IAM role's policy to include these permissions resolves the issue, as the crawler operates under that role, not under a specific IAM user.

Answer

Grant the crawler's IAM user access to the bucket

Answer

Attach a VPC endpoint to the S3 bucket

Answer

Enable S3 default encryption on the bucket

Question 7

A company uses AWS Kinesis Data Streams to ingest real-time data. The data engineer notices that the stream's 'WriteProvisionedThroughputExceeded' error occurs frequently during peaks. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards in the stream.. Increasing the number of shards increases the write capacity, directly addressing the throughput exceeded error. Option B is wrong because the error is not from the producer side. Option C is wrong because the error is about write throughput, not read. Option D is wrong because the error is not about data format.

Answer

Modify the producer to use a different partition key.

Answer

Compress the data before sending to the stream.

Answer

Enable enhanced fan-out for consumers.

Question 8

A company is using Kinesis Data Firehose to deliver data to an S3 bucket. The delivery stream is failing with 'S3 bucket access denied' errors. The bucket policy allows the Firehose service principal. What could be the issue?

Accepted Answer

The IAM role assigned to Firehose lacks s3:PutObject permission. Even though the S3 bucket policy allows the Firehose service principal, Kinesis Data Firehose uses an IAM role to write data. This role must have the s3:PutObject permission. Without it, Firehose will receive an access denied error. Option A is incorrect because VPC differences affect network connectivity, not IAM permissions. Option B is incorrect because SSE-KMS requires KMS permissions, but the error here is specifically about S3 access. Option C is incorrect because bucket name validation occurs during stream creation, not during data delivery.

Answer

The S3 bucket is in a different VPC

Answer

The S3 bucket uses SSE-KMS and Firehose does not have KMS permissions

Answer

The S3 bucket name contains invalid characters

Question 9

A company stores sensitive data in Amazon S3 and needs to ensure that data is encrypted at rest. Which AWS service can be used to manage the encryption keys?

Accepted Answer

AWS Key Management Service (KMS). AWS Key Management Service (KMS) is the managed service for creating and controlling encryption keys used to encrypt data at rest in Amazon S3. Option A is correct. Option B (Secrets Manager) is for managing secrets like database passwords, not encryption keys. Option C (IAM) manages access permissions, not encryption keys. Option D (Certificate Manager) handles SSL/TLS certificates, not encryption keys for data at rest.

Answer

AWS Secrets Manager

Answer

AWS Identity and Access Management (IAM)

Answer

AWS Certificate Manager (ACM)

Question 10

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. Concatenating small files into larger files reduces the overhead of file listing and task scheduling, improving query performance. Option D is correct because partitioning the data by a frequently filtered column allows Athena to use partition pruning, scanning only relevant partitions and reducing the amount of data read. Option A is incorrect because increasing the number of small files increases overhead and worsens performance. Option B is incorrect because disabling S3 server-side encryption does not affect query performance. Option E is incorrect because converting from CSV to JSON does not improve query performance; columnar formats like Parquet or ORC would be beneficial.

Answer

Increase the number of files to increase parallelism

Answer

Disable S3 server-side encryption

Answer

Convert files from CSV to JSON

Question 11

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. The recommended approaches for improving Redshift query performance include applying column compression encoding (Option B) to reduce I/O, defining sort keys on columns used in WHERE clauses (Option C) to enable range-restricted scans, and choosing an appropriate distribution key for large tables (Option E) to minimize data movement between nodes. Enabling concurrency scaling (Option A) primarily helps with handling multiple concurrent queries but does not directly improve the performance of an individual query. Adding more nodes (Option D) can increase overall capacity but is often not the most cost-effective or immediate solution for performance issues.

Answer

Enable concurrency scaling

Answer

Add more nodes to the cluster

Question 12

A data engineer is designing a data lake on Amazon S3. The data is accessed frequently for the first 30 days, then rarely after that. Which lifecycle policy is MOST cost-effective?

Accepted Answer

Transition to S3 Standard-Infrequent Access (Standard-IA) after 30 days.. Transitioning to S3 Standard-Infrequent Access (Standard-IA) after 30 days is the most cost-effective because it reduces storage costs for data that is rarely accessed while maintaining low latency and high durability. Option B (One Zone-IA) offers lower durability and is not recommended for a data lake. Option C (Glacier Deep Archive) has high retrieval times, which may not be suitable even for rare access. Option D (keeping in Standard for 90 days then deleting) is more expensive for the first 90 days and deletes data that might still be needed.

Answer

Transition to S3 One Zone-IA after 30 days.

Answer

Transition to S3 Glacier Deep Archive after 30 days.

Answer

Keep in S3 Standard for 90 days, then delete.

Question 13

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 it is the native monitoring service for AWS Glue, capturing job metrics, logs, and state changes. You can configure CloudWatch alarms to trigger on job failures, which then invoke Amazon SNS (C) to send notifications via email, SMS, or other endpoints. Together, they provide a complete monitoring and alerting solution without additional orchestration.

Answer

AWS Step Functions

Answer

Amazon Kinesis Data Streams

Answer

Amazon SQS

Question 14

A company runs an Amazon EMR cluster with Spark jobs. One job fails with 'Container killed by YARN for exceeding memory limits'. The data engineer has already increased the executor memory. What is the NEXT best step to resolve the issue?

Accepted Answer

Increase the YARN container memory allocation (yarn.nodemanager.resource.memory-mb).. Increasing the yarn.nodemanager.resource.memory-mb allows YARN to allocate larger containers, preventing the kill. Option A: Setting spark.executor.memoryOverhead increases off-heap memory but may still exceed YARN limits if they are not also raised. Option C: Reducing partitions decreases parallelism and may reduce memory per executor, but does not address the root cause of insufficient container memory. Option D: Increasing driver memory is not relevant because the issue is with executors.

Answer

Set spark.executor.memoryOverhead to a higher value.

Answer

Decrease the number of Spark partitions.

Answer

Increase the driver memory.

Question 15

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