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

 

Question 381:

A data engineer needs to create a temporary copy of data for testing without affecting the original Amazon S3 objects. Which S3 feature can help maintain multiple object versions?

1. S3 Versioning
2. S3 Transfer Acceleration
3. S3 Select
4. S3 Lifecycle

Correct Answer: 1

Explanation:

Amazon S3 Versioning keeps multiple versions of an object in the same bucket. When an object is overwritten or deleted, its previous version can remain available, allowing users to recover earlier data. This is useful for protecting datasets from accidental changes and supporting recovery scenarios. However, Versioning does not automatically create a separate testing copy, so a data engineer may still need to copy objects when an independent dataset is required. Transfer Acceleration improves transfer performance, S3 Select retrieves subsets of object data, and Lifecycle manages storage transitions. Therefore, S3 Versioning is the relevant feature for maintaining historical object versions.

Question 382:

A company wants to query compressed Parquet data in Amazon S3 using Amazon Athena. Which benefit does Parquet provide?

1. It requires every column to be read
2. It supports columnar storage and efficient compression
3. It only supports unstructured data
4. It prevents partitioning

Correct Answer: 2

Explanation:

Apache Parquet is a columnar storage format optimized for analytical workloads. It stores data by column, allowing Athena and other query engines to read only the columns required by a query. Parquet also supports efficient compression and encoding, which can reduce storage requirements and the amount of data scanned. This can improve query performance and lower Athena query costs. Parquet can also be combined with partitioning for additional optimization. It is not limited to unstructured data, and it does not prevent partitioning. Therefore, columnar storage combined with efficient compression is a major benefit of using Parquet for Athena workloads.

Question 383:

A data engineer needs to ensure that records arriving from multiple sources follow a common schema before they are stored in a curated data lake. What should the ETL process perform?

1. Schema standardization
2. Disable validation
3. Remove all records with optional fields
4. Convert everything to plain text

Correct Answer: 1

Explanation:

Schema standardization ensures that data from different sources follows a consistent structure before being loaded into a curated data layer. The ETL process can rename fields, convert data types, normalize formats, and map source-specific attributes to a common schema. This improves interoperability and makes downstream analytics more reliable. Disabling validation can allow inconsistent records into the curated dataset, while removing records simply because optional fields are missing can cause unnecessary data loss. Converting everything to plain text does not solve structural inconsistencies. Therefore, schema standardization is the appropriate approach for creating a consistent curated data model.

Question 384:

A company wants to automatically identify new tables and partitions when datasets are added to an Amazon S3 data lake. Which AWS Glue component should be used?

1. AWS Glue Crawler
2. AWS Glue Connection
3. AWS Glue Trigger only
4. AWS Glue Bookmark

Correct Answer: 1

Explanation:

AWS Glue Crawlers can scan data sources such as Amazon S3 and automatically discover schemas, tables, and partitions. The crawler stores the discovered metadata in the AWS Glue Data Catalog, making it available to services such as Athena and Glue ETL jobs. Glue Connections are used to configure connectivity to certain data sources, while job bookmarks help track previously processed data. Triggers control when Glue jobs or workflows run but do not perform schema discovery. Therefore, a Glue Crawler is the appropriate component for automatically identifying new tables and partitions in an S3-based data lake.

Question 385:

A data engineer needs to migrate an Amazon RDS MySQL database to Amazon Aurora MySQL while keeping downtime as low as possible. Which approach is appropriate?

1. Use AWS DMS with ongoing replication
2. Export only the database schema
3. Delete the source database first
4. Use Amazon Macie

Correct Answer: 1

Explanation:

AWS Database Migration Service can perform an initial full load followed by ongoing change data capture. This allows changes occurring in the source database to continue being replicated while the target environment is prepared. Once the target is synchronized, the application can be switched over with reduced downtime compared with a simple offline migration. Exporting only the schema does not migrate the actual data, and deleting the source database would cause data loss. Macie is unrelated to database migration. Therefore, using AWS DMS with ongoing replication is an appropriate approach for minimizing downtime during the migration.

Question 386:

A company requires encryption keys to be managed directly by the organization and wants to control which IAM principals can use those keys. Which solution is most appropriate for S3 data encryption?

1. SSE-S3
2. SSE-KMS using a customer-managed KMS key
3. S3 Lifecycle
4. S3 Transfer Acceleration

Correct Answer: 2

Explanation:

S3 SSE-KMS allows Amazon S3 objects to be encrypted using AWS Key Management Service. When a customer-managed KMS key is selected, the organization has greater control over key policies and permissions, including which IAM principals can use the key. KMS also provides auditing capabilities for key usage. SSE-S3 uses S3-managed keys and provides less direct control over the encryption key. Lifecycle rules manage storage transitions and expiration, while Transfer Acceleration improves data transfer performance. Therefore, SSE-KMS with a customer-managed KMS key is the appropriate solution when the organization requires direct control over encryption-key permissions.

Question 387:

A data engineer wants Amazon Athena to automatically determine partition values from a partitioned S3 dataset without maintaining a large partition list in the Glue Data Catalog. Which feature should be considered?

1. Athena partition projection
2. S3 Versioning
3. Kinesis enhanced fan-out
4. DynamoDB Streams

Correct Answer: 1

Explanation:

Athena partition projection allows partition information to be calculated from configured rules rather than requiring every partition to be explicitly stored in the AWS Glue Data Catalog. This can be useful for datasets with a large number of partitions, especially when partition values follow predictable patterns such as dates or numeric ranges. It can reduce the administrative overhead associated with maintaining partition metadata. S3 Versioning tracks object versions, Kinesis enhanced fan-out provides dedicated stream-consumer throughput, and DynamoDB Streams captures item changes. Therefore, Athena partition projection is the appropriate feature for reducing partition metadata management overhead.

Question 388:

A company needs to store analytical data in Amazon Redshift and frequently queries rows based on a timestamp column. Which Redshift feature can help organize table data to improve range-restricted query performance?

1. Sort key
2. IAM role
3. Security group
4. KMS key

Correct Answer: 1

Explanation:

Amazon Redshift sort keys determine the order in which rows are stored within table regions. Choosing a frequently filtered column, such as a timestamp, as an appropriate sort key can improve performance for queries that use range restrictions on that column. Redshift can use zone-map information to avoid scanning blocks that cannot contain relevant values. IAM roles control permissions, security groups control network access for certain resources, and KMS keys provide encryption capabilities. Therefore, a suitable sort key is the appropriate Redshift feature when queries frequently filter data using ranges on a timestamp column.

Question 389:

A data engineer wants to send a notification whenever an AWS Glue job fails. Which combination can provide event detection and notification delivery?

1. EventBridge and Amazon SNS
2. S3 and DynamoDB
3. Athena and Redshift
4. Macie and Kinesis

Correct Answer: 1

Explanation:

Amazon EventBridge can detect relevant AWS service events, including events associated with AWS Glue jobs, and route those events to targets such as Amazon SNS. SNS can then deliver notifications to subscribed endpoints, including email or other supported destinations. This creates an automated monitoring and notification workflow without requiring a continuously running application. S3 and DynamoDB are storage services, Athena and Redshift are analytical services, and Macie and Kinesis serve different data-security and streaming purposes. Therefore, EventBridge combined with SNS is an appropriate solution for notifying an operations team when a Glue job fails.

Question 390:

A company wants to automatically expire temporary Amazon S3 objects after 30 days to reduce unnecessary storage costs. Which configuration should be used?

1. S3 Lifecycle expiration rule
2. S3 Versioning only
3. S3 Object Lock
4. S3 Transfer Acceleration

Correct Answer: 1

Explanation:

Amazon S3 Lifecycle rules can automatically transition or expire objects based on conditions such as object age. A lifecycle expiration rule can be configured so that temporary objects are automatically removed after 30 days, helping reduce ongoing storage costs and operational effort. S3 Versioning preserves previous object versions, Object Lock protects objects against deletion or overwrite during retention periods, and Transfer Acceleration improves transfer speeds. Therefore, an S3 Lifecycle expiration rule is the most appropriate solution when temporary objects should automatically be deleted after a defined period.

Question 391:

A data engineer needs to perform an analytical query on data stored in Amazon S3 and wants to pay only for the amount of data scanned by the query. Which service is most appropriate?

1. Amazon Athena
2. Amazon RDS
3. Amazon ElastiCache
4. Amazon Route 53

Correct Answer: 1

Explanation:

Amazon Athena is a serverless interactive query service that allows users to analyze data directly in Amazon S3 using SQL. Athena does not require the data engineer to provision database servers or manage a data warehouse cluster. Query costs are generally based on the amount of data scanned, making efficient formats such as Parquet and techniques such as partitioning particularly valuable. RDS is a managed relational database service, ElastiCache is an in-memory caching service, and Route 53 provides DNS functionality. Therefore, Athena is the appropriate service for serverless SQL analysis of S3 data with scan-based pricing.

Question 392:

A company wants to continuously stream application events and allow several independent applications to consume the same events. Which AWS service is best suited for this requirement?

1. Amazon Kinesis Data Streams
2. Amazon S3 Glacier
3. Amazon RDS
4. AWS Secrets Manager

Correct Answer: 1

Explanation:

Amazon Kinesis Data Streams is designed for real-time data streaming and supports multiple consumer applications reading records from the same stream. Applications can independently process events for analytics, monitoring, machine learning, or operational workflows. Kinesis provides durable streaming capacity and supports features such as partition keys and enhanced fan-out for consumer workloads. S3 Glacier is designed for archival storage, RDS is a relational database service, and Secrets Manager stores sensitive credentials. Therefore, Kinesis Data Streams is the most appropriate service for continuously streaming application events to multiple independent consumers.

Question 393:

A data engineer wants to create a workflow that retries a failed AWS Glue job twice and then sends the execution to an error-handling state. Which Step Functions features should be used?

1. Retry and Catch
2. Parallel and Map only
3. Wait and Pass only
4. Choice and Succeed only

Correct Answer: 1

Explanation:

AWS Step Functions provides Retry and Catch mechanisms for workflow error handling. Retry can be configured to attempt a failed task again according to specified retry settings, such as the number of attempts and retry intervals. If the task continues to fail after the configured retries, Catch can route execution to an error-handling state. This provides a structured way to handle transient failures while ensuring persistent failures are handled separately. Parallel, Map, Wait, Pass, Choice, and Succeed states provide other workflow capabilities but do not by themselves provide the required retry-and-error-routing behavior.

Question 394:

A company wants to protect a dataset from accidental deletion while retaining the ability to recover previous object versions. Which combination is most appropriate?

1. S3 Versioning and appropriate access controls
2. S3 Transfer Acceleration only
3. S3 Select and Athena
4. Kinesis Data Streams and Firehose

Correct Answer: 1

Explanation:

S3 Versioning helps preserve previous versions of objects so that data can be recovered after accidental overwrites or deletions. Appropriate IAM and bucket policies can further restrict who is allowed to delete or modify objects. Combining versioning with strong access controls provides both recovery capability and protection against accidental changes. Transfer Acceleration improves network transfer speed, while S3 Select and Athena are query-related capabilities. Kinesis Data Streams and Firehose are designed for streaming data pipelines. Therefore, S3 Versioning combined with appropriate access controls is the best approach for protecting recoverable datasets from accidental deletion.

Question 395:

A data engineer wants to load only new records from a source system during each ETL run rather than processing the entire dataset every time. Which approach is most appropriate?

1. Incremental processing
2. Full reload on every run
3. Delete the source after every run
4. Disable job state tracking

Correct Answer: 1

Explanation:

Incremental processing loads only new or changed records rather than reprocessing the complete source dataset during every run. This can significantly reduce processing time, resource consumption, and cost. AWS Glue job bookmarks can support incremental processing for supported workloads by maintaining state about previously processed data. Other approaches, such as performing a full reload every time, can create unnecessary processing overhead. Deleting source data is risky and can cause permanent data loss, while disabling state tracking makes incremental processing more difficult. Therefore, incremental processing is the appropriate strategy for efficiently handling recurring ETL workloads.

Question 396:

A company needs to route events differently depending on attributes such as event type and source. Which AWS service provides event-pattern filtering and routing?

1. Amazon EventBridge
2. Amazon RDS
3. Amazon S3 Glacier
4. Amazon ElastiCache

Correct Answer: 1

Explanation:

Amazon EventBridge provides event buses, event rules, and event-pattern matching that allow events to be routed to different targets based on their attributes. For example, a rule can route order-created events to one target while sending payment-related events to another. This enables loosely coupled, event-driven architectures and reduces the need for applications to implement custom routing logic. RDS provides relational databases, S3 Glacier provides archival storage, and ElastiCache provides in-memory caching. Therefore, EventBridge is the appropriate AWS service for filtering and routing events based on source and event attributes.

Question 397:

A data engineer wants to reduce the amount of data transferred during an Athena query by selecting only three required columns from a Parquet table containing 40 columns. Which optimization is being used?

1. Column pruning
2. Data duplication
3. Full table scanning
4. Row expansion

Correct Answer: 1

Explanation:

Column pruning allows query engines to read only the columns required by a query. Because Parquet is a columnar file format, Athena can take advantage of this structure and avoid reading unrelated columns when possible. For example, a query that selects only three columns from a table containing 40 columns may scan substantially less data than a query that selects every column. This can improve performance and reduce query costs. Full table scanning would increase the amount of data processed, while duplication and row expansion provide no performance benefit. Therefore, the optimization described is column pruning.

Question 398:

A company wants to use IAM permissions to allow an AWS Glue job to read objects from one S3 bucket but not delete or modify them. Which permission set follows least privilege?

1. s3:* on all buckets
2. Read-only S3 permissions for the required bucket and objects
3. AdministratorAccess
4. Public access to the bucket

Correct Answer: 2

Explanation:

The principle of least privilege requires granting only the permissions necessary for a task. If an AWS Glue job only needs to read data from an S3 bucket, its IAM role should have the required read permissions, such as appropriate Get/List permissions, without granting write or delete access. Granting s3:* or AdministratorAccess provides excessive privileges and increases security risk. Making the bucket public is also inappropriate for protected data. Therefore, read-only permissions scoped to the required bucket and objects provide a more secure and least-privileged configuration for the Glue job.

Question 399:

A data engineer wants to identify the reason an AWS Glue ETL job failed, including errors recorded during execution. Which source should be examined first?

1. CloudWatch logs
2. S3 Lifecycle configuration
3. Route 53 records
4. S3 Transfer Acceleration settings

Correct Answer: 1

Explanation:

AWS Glue integrates with Amazon CloudWatch Logs, where job execution logs and error information can be examined. These logs can help data engineers identify issues such as transformation errors, schema problems, permission failures, connection errors, or unexpected input data. Reviewing CloudWatch logs is an important troubleshooting step before changing the pipeline configuration. S3 Lifecycle settings control object transitions, Route 53 manages DNS, and Transfer Acceleration concerns S3 transfer performance. Therefore, CloudWatch logs are the most appropriate first source to examine when troubleshooting a failed Glue ETL job.

Question 400:

A company wants to create a governed data lake where different departments can access only the datasets and columns required for their roles. Which AWS service should be central to the governance design?

1. Amazon CloudFront
2. AWS Lake Formation
3. Amazon ElastiCache
4. Amazon Route 53

Correct Answer: 2

Explanation:

AWS Lake Formation provides centralized governance and fine-grained access control for data lakes. It can be used to manage permissions on databases, tables, and columns, allowing organizations to restrict access according to departmental or business requirements. Lake Formation integrates with services such as AWS Glue and Amazon Athena to help establish controlled access to data lake resources. CloudFront is designed for content delivery, ElastiCache provides in-memory caching, and Route 53 provides DNS services. Therefore, AWS Lake Formation should be central to a governed data lake architecture where departments require controlled access to specific datasets and columns.