View Full Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Dumps.

 

Question 321:

A data engineer needs to load a large amount of data from Amazon S3 into an Amazon Redshift table. Which command is specifically designed for this operation?

1. INSERT
2. COPY
3. UPDATE
4. MERGE

Correct Answer: 2

Explanation:

The Amazon Redshift COPY command is designed to efficiently load large amounts of data from Amazon S3 and other supported sources into Redshift tables. COPY can load data in parallel across the cluster, making it significantly more efficient for bulk ingestion than individual INSERT statements. The command can work with formats such as CSV, JSON, and Parquet, depending on the configuration and source. INSERT is useful for individual or smaller sets of rows, while UPDATE and MERGE are primarily used for modifying existing data. Therefore, COPY is the preferred command when loading large datasets from Amazon S3 into Amazon Redshift.

Question 322:

A company wants to prevent unauthorized public access to all Amazon S3 buckets in an AWS account. Which feature should the data engineer use?

1. S3 Block Public Access
2. S3 Inventory
3. S3 Transfer Acceleration
4. S3 Select

Correct Answer: 1

Explanation:

Amazon S3 Block Public Access provides controls that help prevent buckets and objects from becoming publicly accessible. The settings can be applied at the account level, providing a centralized safeguard against accidental public exposure. This is particularly useful for organizations that store sensitive data in S3 and want to establish a strong baseline security control. S3 Inventory generates reports about objects, Transfer Acceleration improves data transfer performance, and S3 Select allows applications to retrieve subsets of object data. Therefore, S3 Block Public Access is the appropriate feature for preventing unintended public access across an AWS account.

Question 323:

A data engineer needs to maintain a history of changes made to objects in an Amazon S3 bucket so that previous versions can be recovered. Which feature should be enabled?

1. S3 Lifecycle
2. S3 Versioning
3. S3 Select
4. S3 Inventory

Correct Answer: 2

Explanation:

Amazon S3 Versioning preserves multiple versions of an object in the same bucket. When an object is overwritten or deleted, previous versions can remain available, allowing users to recover data from an earlier state. This is useful for protecting against accidental deletion, overwrites, or application errors. S3 Lifecycle manages object transitions and expiration, S3 Select retrieves subsets of object data, and S3 Inventory provides reports about stored objects. Therefore, S3 Versioning is the correct feature when a data engineer needs to preserve and recover previous versions of S3 objects.

Question 324:

A company needs to run SQL queries against data stored in Amazon S3 while ensuring that query users are separated into different environments with independent query settings and cost controls. Which Athena feature should be used?

1. Athena workgroups
2. S3 Access Points
3. Glue crawlers
4. Kinesis shards

Correct Answer: 1

Explanation:

Amazon Athena workgroups allow organizations to separate users, queries, and workloads into logical groups. Workgroups can provide independent query settings, access controls, and result locations, and can also help organizations manage and monitor query usage and costs. This is useful when multiple teams share an Athena environment but need different configurations or controls. S3 Access Points focus on managing access to S3 data, Glue crawlers discover metadata, and Kinesis shards support streaming data ingestion. Therefore, Athena workgroups are the appropriate feature for separating analytical query environments and managing query-related settings.

Question 325:

A data engineer needs to detect duplicate records in an ETL pipeline based on a unique transaction_id field. Which approach is most appropriate?

1. Sort records alphabetically
2. Remove all records with NULL values
3. Use transaction_id to identify and deduplicate records
4. Convert all records to JSON

Correct Answer: 3

Explanation:

When transaction_id uniquely identifies a business transaction, it can be used as the key for identifying duplicate records. During ETL processing, the pipeline can group or compare records using transaction_id and retain the appropriate record according to business rules, such as the newest event timestamp. Sorting records or converting them to another format does not inherently remove duplicates. Removing NULL values is also unrelated to duplicate detection and could unnecessarily discard valid records. Therefore, using transaction_id as the deduplication key is the appropriate approach for ensuring that duplicate transactions do not enter the target dataset.

Question 326:

A company wants to automatically start an AWS Glue ETL job when a new object is created in a specific Amazon S3 bucket. Which service can provide the event-driven trigger?

1. Amazon EventBridge
2. Amazon Redshift
3. Amazon ElastiCache
4. AWS Secrets Manager

Correct Answer: 1

Explanation:

Amazon EventBridge can receive Amazon S3 events and route them to supported targets, including AWS Glue workflows or other AWS services. An event pattern can be configured to respond when objects are created in a specific bucket, allowing the data pipeline to start automatically when new data arrives. This creates an event-driven architecture without requiring continuous polling of the bucket. Redshift provides data warehousing, ElastiCache provides caching, and Secrets Manager manages sensitive credentials. Therefore, EventBridge is the appropriate service for detecting the S3 object-created event and initiating the next stage of the data pipeline.

Question 327:

A data engineer wants to reduce the storage size and improve the analytical query performance of a large dataset in Amazon S3. Which combination is most appropriate?

1. CSV and no compression
2. JSON and larger files
3. Parquet with compression
4. XML with encryption disabled

Correct Answer: 3

Explanation:

Parquet is a columnar storage format designed for analytical workloads, while compression reduces the physical size of stored data. Together, Parquet and compression can reduce storage requirements and the amount of data that analytical engines such as Athena need to read. Parquet also supports column pruning, meaning queries that require only selected columns can avoid reading unrelated columns. CSV, JSON, and XML are generally less efficient for large analytical workloads. Encryption settings do not determine whether a file format is suitable for analytical processing. Therefore, storing data as compressed Parquet is an effective strategy for both storage efficiency and query performance.

Question 328:

A streaming application receives records through Amazon Kinesis Data Streams. The company needs records belonging to the same customer to remain ordered. Which configuration should be used?

1. Use customer_id as the partition key
2. Use a random partition key for every record
3. Store every record in a separate stream
4. Disable record sequencing

Correct Answer: 1

Explanation:

Amazon Kinesis Data Streams uses partition keys to determine which shard receives a record. Records with the same partition key are routed to the same shard, and records within a shard maintain ordering. Therefore, using customer_id as the partition key ensures that records for the same customer are directed to the same shard and can be processed in order. Randomizing the partition key could distribute records for the same customer across different shards, making ordering more difficult. Creating separate streams for every customer is inefficient and unnecessary. Thus, customer_id is an appropriate partition key when per-customer ordering is required.

Question 329:

A company wants to encrypt data in Amazon S3 using a customer-managed AWS KMS key and maintain control over key permissions. Which encryption option should be used?

1. SSE-S3
2. Client-side compression
3. SSE-KMS
4. TLS only

Correct Answer: 3

Explanation:

Amazon S3 SSE-KMS provides server-side encryption using AWS Key Management Service keys. When a customer-managed KMS key is selected, the organization can control key policies, permissions, rotation settings, and auditing through KMS. SSE-S3 uses Amazon S3-managed encryption keys and does not provide the same level of direct customer control over the encryption key. Compression is unrelated to encryption, while TLS protects data while it is being transmitted rather than providing encryption at rest. Therefore, SSE-KMS with a customer-managed KMS key is the appropriate solution when the organization requires greater control over encryption keys.

Question 330:

A data engineer needs to discover the schema of JSON files stored in Amazon S3 and create corresponding metadata tables automatically. Which AWS Glue component should be used?

1. AWS Glue Crawler
2. AWS Glue DataBrew
3. AWS CloudTrail
4. Amazon Macie

Correct Answer: 1

Explanation:

AWS Glue Crawlers can scan data sources such as Amazon S3, infer the structure and schema of supported datasets, and create or update tables in the AWS Glue Data Catalog. This is particularly useful when a data engineer has many files and does not want to manually define metadata for each dataset. Glue DataBrew is focused on visual data preparation, CloudTrail records AWS API activity, and Macie discovers sensitive information. Therefore, a Glue Crawler is the appropriate component for automatically discovering the schema of JSON files and cataloging the resulting metadata.

Question 331:

A company wants to process streaming data and deliver it to Amazon S3 without managing servers or writing custom consumers. Which service is most appropriate?

1. Amazon Data Firehose
2. Amazon RDS
3. Amazon Redshift Serverless
4. AWS Glue Data Catalog

Correct Answer: 1

Explanation:

Amazon Data Firehose is a managed service designed to capture streaming data and deliver it to destinations such as Amazon S3, Amazon Redshift, and Amazon OpenSearch Service. It automatically handles much of the infrastructure required for buffering and delivering streaming records, reducing the need for custom consumer applications. RDS is a relational database service, Redshift Serverless provides a data warehouse environment, and the Glue Data Catalog stores metadata. Therefore, Data Firehose is the most appropriate choice when streaming data needs to be delivered to S3 without managing streaming servers or custom delivery infrastructure.

Question 332:

A data engineer wants an AWS Glue job to transform incoming data and write the output in a format optimized for analytical queries. Which format should be selected?

1. Plain text
2. XML
3. Parquet
4. Uncompressed CSV

Correct Answer: 3

Explanation:

Apache Parquet is a columnar format that is highly suitable for analytical workloads. AWS Glue jobs can transform source data and write it to Parquet in Amazon S3, allowing services such as Athena to take advantage of column pruning and efficient compression. This can reduce both query execution time and the amount of data scanned. Plain text, XML, and uncompressed CSV are generally less efficient for large-scale analytical processing. Therefore, Parquet is the preferred output format when the goal is to create an optimized analytical dataset after an AWS Glue transformation job.

Question 333:

A company wants to automatically retry a failed AWS service operation when the failure is likely to be temporary. Which approach is most appropriate?

1. Exponential backoff with retries
2. Disable all error handling
3. Delete the failed data
4. Permanently stop the pipeline

Correct Answer: 1

Explanation:

Transient failures can occur because of temporary throttling, network issues, or short-lived service problems. Exponential backoff with retries allows an application or workflow to retry an operation after progressively longer delays. This reduces the chance of repeatedly sending requests during a temporary problem and helps avoid unnecessary load on the affected service. Disabling error handling or permanently stopping the pipeline does not provide resilience, while deleting failed data can cause data loss. Therefore, exponential backoff combined with a controlled retry strategy is an effective approach for handling temporary service failures.

Question 334:

A data engineer wants to query only the required columns from a Parquet dataset in Amazon Athena. Which optimization helps reduce the amount of data read?

1. Column pruning
2. Row duplication
3. Data expansion
4. Disabling compression

Correct Answer: 1

Explanation:

Column pruning allows an analytical query engine to read only the columns required by the query instead of scanning every column in the dataset. This optimization works particularly well with columnar formats such as Apache Parquet. For example, if a table contains 50 columns but a query needs only three, Athena can avoid reading the other columns when the dataset is stored appropriately. This reduces the amount of data scanned and can improve query performance and cost efficiency. Therefore, column pruning is the correct optimization for reducing unnecessary reads from a Parquet dataset.

Question 335:

A company needs to continuously replicate changes from an on-premises Oracle database to AWS while minimizing application downtime during migration. Which AWS service should be used?

1. Amazon Athena
2. AWS Database Migration Service
3. Amazon Macie
4. AWS CloudFormation

Correct Answer: 2

Explanation:

AWS Database Migration Service supports ongoing replication through change data capture, making it suitable for migration scenarios where the source database must remain operational while data is replicated to AWS. DMS can perform an initial full load and then continuously replicate subsequent changes. This approach allows organizations to reduce downtime and perform a controlled cutover when the target environment is ready. Athena is used for SQL analytics, Macie discovers sensitive data, and CloudFormation manages infrastructure. Therefore, AWS DMS is the appropriate service for continuously replicating database changes during a migration.

Question 336:

A data engineer wants to ensure that an ETL application does not store database passwords directly in source code. Which approach is recommended?

1. Store passwords in a public S3 bucket
2. Hard-code passwords in Lambda environment variables
3. Store credentials in AWS Secrets Manager
4. Put passwords in CloudWatch logs

Correct Answer: 3

Explanation:

AWS Secrets Manager provides secure storage for database credentials and other sensitive secrets. Applications can retrieve secrets at runtime rather than embedding passwords directly in source code. Secrets Manager also supports features such as rotation and access control through IAM. Storing passwords in a public S3 bucket is insecure, while hard-coding them in application configuration increases the risk of accidental exposure. CloudWatch logs should never be used as a secret store because sensitive values could become accessible to users with log permissions. Therefore, AWS Secrets Manager is the recommended solution for securely managing ETL database credentials.

Question 337:

A company wants to query data in Amazon Athena while preventing users from accidentally running queries that scan extremely large datasets. Which feature can help control query costs?

1. Athena workgroup data usage controls
2. S3 Versioning
3. Kinesis enhanced fan-out
4. Glue job bookmarks

Correct Answer: 1

Explanation:

Amazon Athena workgroups can be configured with query-related controls, including data usage limits that help organizations manage and monitor query costs. This is useful when multiple users share an Athena environment and some queries could potentially scan very large datasets. Setting appropriate workgroup controls can help prevent unexpected spending and establish governance around analytical workloads. S3 Versioning protects object versions, Kinesis enhanced fan-out provides dedicated consumer throughput, and Glue job bookmarks track previously processed data. Therefore, Athena workgroup data usage controls are the appropriate feature for helping manage excessive query scanning and associated costs.

Question 338:

A data engineer needs to store semi-structured documents and retrieve them using a flexible key-value access pattern with very low latency. Which AWS service is most appropriate?

1. Amazon DynamoDB
2. Amazon Redshift
3. Amazon Athena
4. Amazon S3 Glacier Flexible Retrieval

Correct Answer: 1

Explanation:

Amazon DynamoDB is a fully managed NoSQL database designed for high-performance applications requiring low-latency access. It supports key-value and document data models, making it suitable for semi-structured records whose attributes can vary between items. DynamoDB is commonly used when applications need predictable performance and access patterns based on partition keys and optional sort keys. Redshift is optimized for analytical data warehousing, Athena provides serverless SQL analytics, and S3 Glacier Flexible Retrieval is designed for archival storage. Therefore, DynamoDB is the most appropriate service for flexible document storage combined with low-latency key-based access.

Question 339:

A data engineer wants to identify which AWS user or role deleted an Amazon S3 object during an investigation. Which service should be consulted?

1. AWS Glue
2. Amazon CloudWatch
3. AWS CloudTrail
4. Amazon Athena only

Correct Answer: 3

Explanation:

AWS CloudTrail records AWS API activity and can provide information about actions performed against resources. When appropriate S3 data events are enabled, CloudTrail can record object-level operations such as DeleteObject. Investigation teams can use these records to determine details such as the identity that made the API request, the time of the operation, and other request information. Glue is used for data integration, CloudWatch focuses on monitoring, and Athena is a query service rather than the primary audit source. Therefore, CloudTrail should be consulted when investigating which identity performed an S3 object deletion.

Question 340:

A company wants to build a workflow where two independent data-processing tasks can execute at the same time before a final aggregation task runs. Which AWS Step Functions feature should be used?

1. Sequential states only
2. Parallel state
3. S3 Lifecycle rule
4. Glue crawler

Correct Answer: 2

Explanation:

AWS Step Functions provides a Parallel state that allows multiple branches of workflow execution to run concurrently. This is useful when independent data-processing tasks do not depend on one another and can execute at the same time. After the parallel branches complete, the workflow can continue to a subsequent state, such as a final aggregation or validation step. Running independent tasks concurrently can reduce overall workflow duration compared with executing every operation sequentially. S3 Lifecycle rules manage object storage transitions, and Glue Crawlers discover metadata. Therefore, the Step Functions Parallel state is the correct feature for concurrent workflow branches.