DEA-C01 Practice Test 35 — 15 Questions

Question 1

Which TWO AWS services can be used to transform data in transit during ingestion? (Choose 2.)

Accepted Answer

Amazon Kinesis Data Firehose with Lambda transformation. Amazon Kinesis Data Firehose can invoke an AWS Lambda function to transform streaming data in real time before delivering it to a destination, making it suitable for transforming data in transit during ingestion. AWS Glue ETL can be used for both batch and streaming transformations; specifically, AWS Glue supports streaming ETL jobs that can transform data in transit as it is ingested from sources like Amazon MSK or Kinesis Data Streams. Therefore, both services can transform data during ingestion, albeit with different use cases and latency characteristics.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Athena

Answer

AWS Data Pipeline

Question 2

A data engineer is configuring an S3 bucket for a data lake. The engineer runs the command shown in the exhibit. What does the output indicate about the bucket?

Accepted Answer

Versioning is enabled on the bucket.. The output of the command (likely 'aws s3api get-bucket-versioning') shows a Status of 'Enabled', indicating that versioning is enabled on the bucket. This means multiple versions of objects are retained.

Answer

The bucket retains only the latest version of each object.

Answer

Versioning is suspended on the bucket.

Answer

MFA Delete is enabled for the bucket.

Question 3

A company is using Amazon Kinesis Data Firehose to ingest data into Amazon S3. The data must be transformed from JSON to Parquet format before delivery. Which feature should be enabled on the Firehose delivery stream?

Accepted Answer

Format conversion (Parquet/ORC). Amazon Kinesis Data Firehose has a built-in format conversion feature that can automatically convert input data from JSON to Parquet or ORC format before delivery to Amazon S3. Option A (Amazon Kinesis Data Analytics) is for real-time stream processing, not format conversion within Firehose. Option B (Amazon S3 event notifications) triggers notifications on S3 events, not data transformation. Option D (AWS Lambda transformation) allows custom code for data transformation but is not specifically for converting JSON to Parquet; the built-in format conversion is the appropriate feature for this task.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3 event notifications

Answer

AWS Lambda transformation

Question 4

A company is using Amazon Redshift for data warehousing. The data engineer notices that the STL_ALERT_EVENT_LOG table shows many 'missing statistics' alerts. What is the best course of action to address this issue?

Accepted Answer

Run ANALYZE on the tables.. The STL_ALERT_EVENT_LOG table records alerts about query performance issues, including 'missing statistics' alerts. This indicates that the query optimizer lacks up-to-date table statistics, leading to suboptimal query plans. Running the ANALYZE command updates table statistics, enabling the optimizer to generate efficient execution plans. Therefore, option D is the correct course of action.

Answer

Increase the WLM concurrency slots.

Answer

Run VACUUM on the tables.

Answer

Enable compression on the tables.

Question 5

A data engineer needs to ingest data from an external partner's FTP server to Amazon S3. The data arrives once daily as a CSV file. Which AWS service should be used for this ingestion?

Accepted Answer

AWS Transfer Family. AWS Transfer Family provides fully managed support for file transfers over SFTP, FTPS, and FTP protocols, making it the correct choice for ingesting CSV files from an external partner's FTP server. It integrates directly with Amazon S3 as a destination, enabling automated, secure, and scheduled transfers without custom infrastructure.

Answer

AWS DataSync

Answer

Amazon Kinesis Data Firehose

Answer

Amazon AppFlow

Question 6

A company uses Amazon Redshift for its data warehouse. The data engineer notices that queries are slow on a large table that is frequently filtered on a column 'transaction_date'. Which optimization technique best improves query performance?

Accepted Answer

Set the sort key to 'transaction_date'.. Setting the sort key to 'transaction_date' organizes the table data physically by that column, which allows Redshift to use zone maps to skip blocks that don't match query filters. This dramatically reduces the amount of data scanned for range-restricted queries on 'transaction_date', improving query performance.

Answer

Apply compression encoding to 'transaction_date'.

Answer

Set the distribution key to 'transaction_date'.

Answer

Run VACUUM on the table.

Question 7

A company is migrating its on-premises data warehouse to Amazon Redshift. The data includes tables with up to 100 columns and 500 million rows. The migration involves a full load followed by incremental updates. The company needs to minimize downtime during the final cutover. Which THREE strategies should the data engineer use to facilitate the migration? (Choose THREE.)

Accepted Answer

Use the COPY command to load data from Amazon S3.. Option B is correct because the COPY command is the most efficient, parallelized way to bulk-load large datasets from Amazon S3 into Redshift, and it supports loading from multiple files in parallel to speed up the full load and minimize cutover downtime. Option C is correct because storing the source files in a columnar format such as Parquet lets COPY read only needed columns, compresses data heavily, and reduces I/O and load time compared with row-based CSV or JSON. Option D is correct because after a large load, running VACUUM re-sorts rows and reclaims space (restoring sort-key performance) and ANALYZE refreshes table statistics so the query planner produces efficient plans for the subsequent incremental workload. Option A is not appropriate because adding WLM queues does not by itself accelerate a single large load and can even reduce per-query resources; queue design is about workload isolation, not migration throughput. Option E is not appropriate because disabling distribution keys removes the ability to co-locate joins and causes data redistribution at query time, hurting performance; distribution keys should be chosen deliberately, not disabled.

Answer

Increase the number of WLM queues to allow more concurrent loads.

Answer

Disable distribution keys on the target tables to simplify loading.

Question 8

Refer to the exhibit. A data engineer runs this CLI command on an S3 bucket. The data is ingested from multiple sources. Which AWS service would be best to process these files in a single batch transformation?

Accepted Answer

AWS Glue. AWS Glue is designed for batch ETL processing, capable of handling multiple files of varying sizes in a single transformation job. Option A is wrong because Lambda has limitations on execution time (15 minutes) and memory (10 GB), making it unsuitable for large-scale batch transforms. Option B is wrong because Kinesis Data Analytics is for real-time stream processing, not batch. Option C is wrong because Athena is an interactive query service for ad-hoc analysis of data in S3, not for batch transformations.

Answer

AWS Lambda

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Athena

Question 9

A data engineer needs to store semi-structured JSON data that is accessed infrequently but requires immediate retrieval when needed. The data must be durable and cost-effective. Which Amazon S3 storage class should be used?

Accepted Answer

S3 Standard-IA. S3 Standard-IA is the correct choice because it offers the same durability and low-latency retrieval as S3 Standard but at a lower storage cost, making it ideal for infrequently accessed data that still needs immediate retrieval when requested. The scenario specifies 'infrequently accessed' and 'immediate retrieval,' which aligns with Standard-IA's design for data accessed less than once a month but with millisecond first-byte latency.

Answer

S3 Glacier

Answer

S3 Standard

Answer

S3 One Zone-IA

Question 10

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that is experiencing high error rates when writing to an S3 bucket. The error logs indicate 'AccessDenied' errors. The S3 bucket policy allows access from the Firehose service, but the errors persist. What is the most likely cause?

Accepted Answer

The IAM role assumed by Firehose does not have the s3:PutObject permission. The most likely cause is that the IAM role assumed by Kinesis Data Firehose lacks the `s3:PutObject` permission. Even if the S3 bucket policy allows access from the Firehose service, the IAM role must explicitly grant the necessary S3 write permissions for Firehose to deliver data. Without this permission, Firehose receives 'AccessDenied' errors when attempting to write objects to the bucket.

Answer

The S3 bucket has a lifecycle policy that is deleting objects too quickly

Answer

The S3 bucket has default encryption enabled

Answer

The S3 bucket uses an AWS KMS key for encryption and Firehose does not have kms:Decrypt permission

Question 11

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 12

A company is using Amazon S3 to store sensitive data. They need to ensure that all objects are encrypted at rest. Which combination of actions should be taken? (Choose TWO.)

Accepted Answer

Use a bucket policy to deny PutObject requests that do not include the x-amz-server-side-encryption header.. A bucket policy that denies PutObject requests lacking the `x-amz-server-side-encryption` header enforces encryption at the time of upload, ensuring that any object written without explicit encryption headers is rejected. Option E is correct because enabling default encryption on the S3 bucket automatically applies server-side encryption (SSE-S3 or SSE-KMS) to any object uploaded without specifying encryption headers, providing a fallback that covers all objects. Together, these actions ensure that every object stored in the bucket is encrypted at rest, either by explicit client request or by default bucket settings.

Answer

Enable S3 Versioning on the bucket.

Answer

Enable MFA Delete on the bucket.

Answer

Configure S3 Access Points with network policies.

Question 13

A data engineer is using AWS Glue to run an ETL job that reads data from Amazon DynamoDB and writes to Amazon Redshift. The job fails with a 'ThroughputExceededException' error. What is the most likely cause?

Accepted Answer

The DynamoDB table's read capacity is insufficient for the Glue job's read rate. A ThroughputExceededException from DynamoDB indicates that the read or write request rate exceeded the provisioned throughput (RCUs/WCUs) or the burst capacity of the table or index. When AWS Glue reads from DynamoDB, it consumes read capacity units; if the table's read capacity is too low for the parallel read rate of the Glue job, DynamoDB throttles the requests and the job fails.

Answer

The Glue job has a timeout setting that is too low

Answer

The Redshift cluster's concurrency scaling is insufficient

Answer

The S3 bucket where Glue writes temporary data does not have proper permissions

Question 14

A data engineer needs to ensure that an Amazon Redshift cluster encrypts all data at rest. Which setting must be enabled when creating the cluster?

Accepted Answer

Enable encryption. Amazon Redshift encryption at rest is enabled during cluster creation by selecting the encryption option. Option A is incorrect because enabling automated snapshots is for backup and recovery, not encryption. Option C is incorrect because SSL/TLS ensures encryption in transit, not at rest. Option D is incorrect because VPC is for network isolation, not encryption.

Answer

Enable automated snapshots

Answer

Enable SSL/TLS

Answer

Enable VPC

Question 15

A data engineer is designing a data lake on Amazon S3. The data consists of sensitive personally identifiable information (PII) that must be encrypted at rest. The company requires that encryption keys be rotated every 90 days and that access to the keys be logged. Which encryption solution meets these requirements?

Accepted Answer

Use server-side encryption with AWS KMS (SSE-KMS) and enable automatic key rotation.. SSE-KMS with automatic key rotation (Option D) meets the requirements because it encrypts data at rest in S3, allows key rotation every 90 days via AWS KMS automatic rotation, and logs all key usage in AWS CloudTrail for auditing. This provides the necessary encryption, rotation, and access logging without managing keys externally.

Answer

Use server-side encryption with customer-provided keys (SSE-C).

Answer

Use client-side encryption with a master key stored in AWS Secrets Manager.

Answer

Use server-side encryption with S3 managed keys (SSE-S3).