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

 

Question 341:

A data engineer needs to create a serverless data warehouse that can automatically scale compute capacity based on workload demand. Which AWS service should be used?

1. Amazon RDS
2. Amazon Redshift Serverless
3. Amazon DynamoDB
4. Amazon ElastiCache

Correct Answer: 2

Explanation:

Amazon Redshift Serverless provides a serverless data warehouse environment where AWS manages the underlying infrastructure and automatically adjusts compute resources according to workload requirements. It is suitable for organizations that want to run analytical SQL queries without manually provisioning and managing Redshift clusters. Amazon RDS is designed for relational databases, DynamoDB is a NoSQL database, and ElastiCache provides in-memory caching. Redshift Serverless can query and analyze large datasets while reducing the operational effort associated with capacity management. Therefore, Amazon Redshift Serverless is the best choice when a data engineer requires a serverless analytical data warehouse.

Question 342:

A company wants to maintain a centralized metadata repository for datasets used by multiple AWS analytics services. Which service should the data engineer use?

1. AWS Glue Data Catalog
2. Amazon CloudWatch
3. AWS Secrets Manager
4. Amazon Kinesis Data Streams

Correct Answer: 1

Explanation:

AWS Glue Data Catalog provides a centralized metadata repository for data assets. It stores information such as table definitions, schemas, data locations, and partitions. AWS analytics services such as Amazon Athena and AWS Glue can use this metadata to understand and process datasets stored in locations such as Amazon S3. CloudWatch is primarily used for monitoring, Secrets Manager securely stores credentials, and Kinesis Data Streams handles real-time streaming data. A centralized catalog makes it easier for multiple teams and services to discover and work with shared datasets. Therefore, AWS Glue Data Catalog is the appropriate solution for centralized data metadata management.

Question 343:

A data engineer needs to run a SQL query that calculates the average sales amount for each product category. Which SQL combination should be used?

1. ORDER BY with COUNT()
2. WHERE with DISTINCT
3. GROUP BY with AVG()
4. UNION with DELETE

Correct Answer: 3

Explanation:

The SQL GROUP BY clause groups records according to one or more columns, while the AVG() aggregate function calculates the average value for each group. For example, a query can group sales records by product category and calculate the average sales amount for every category. ORDER BY only controls the ordering of query results, while COUNT() calculates the number of records. WHERE filters rows before aggregation, and DISTINCT removes duplicate values. UNION combines result sets, while DELETE modifies data. Therefore, GROUP BY combined with AVG() is the correct SQL approach for calculating average sales amounts by product category.

Question 344:

A company wants to automatically move older Amazon S3 objects to a lower-cost storage class based on their age. Which feature should be configured?

1. S3 Versioning
2. S3 Lifecycle rules
3. S3 Object Lock
4. S3 Access Points

Correct Answer: 2

Explanation:

Amazon S3 Lifecycle rules allow organizations to automatically transition objects between storage classes or expire objects based on conditions such as object age. For example, frequently accessed data can initially remain in S3 Standard and later transition to a lower-cost storage class when it becomes less frequently accessed. Lifecycle rules help automate storage management and reduce long-term costs without requiring manual object movement. Versioning preserves multiple object versions, Object Lock protects objects from deletion or modification, and Access Points simplify access management. Therefore, S3 Lifecycle rules are the correct solution for automatically transitioning older objects to lower-cost storage.

Question 345:

A data pipeline receives data from an external application and must validate each record before loading it into the data lake. Invalid records should not enter the curated dataset. What should the pipeline implement?

1. Data quality validation
2. Disable schema checks
3. Delete all source records
4. Remove all NULL values without rules

Correct Answer: 1

Explanation:

Data quality validation allows a pipeline to verify incoming records against predefined business and technical rules before they are written to a trusted or curated dataset. Validation can include checking required fields, data types, ranges, uniqueness, formats, and referential relationships. Records that fail validation can be redirected to a quarantine or error location for investigation while valid records continue through the pipeline. Disabling schema checks can allow corrupted data into downstream systems, while deleting source records risks data loss. Therefore, implementing data quality validation is the appropriate approach for preventing invalid records from entering the curated data layer.

Question 346:

A company wants to use Amazon Athena to query data stored in Amazon S3, but the data contains many small files. What is a recommended optimization?

1. Create more small files
2. Compact small files into larger files
3. Disable compression
4. Convert Parquet to uncompressed CSV

Correct Answer: 2

Explanation:

Large numbers of small files can increase overhead during analytical queries because the query engine must open and process many individual objects. Compacting small files into appropriately sized larger files can improve query performance and reduce unnecessary processing overhead. Columnar formats such as Parquet combined with compression are generally preferred for analytical workloads. Creating more small files increases the problem, while disabling compression or converting efficient columnar data into uncompressed CSV can increase storage and scanning requirements. Therefore, file compaction is a useful optimization when an Athena workload contains excessive numbers of small S3 objects.

Question 347:

A data engineer needs to capture database changes and process INSERT, UPDATE, and DELETE operations continuously. Which AWS DMS capability should be configured?

1. Full load only
2. Change data capture (CDC)
3. S3 Lifecycle
4. Glue Crawler

Correct Answer: 2

Explanation:

AWS Database Migration Service supports Change Data Capture (CDC), which continuously captures changes made to a supported source database after the initial load. CDC can capture operations such as INSERT, UPDATE, and DELETE and replicate those changes to a target system. This is useful for building near-real-time replication pipelines and minimizing downtime during database migrations. A full-load-only task does not continuously capture subsequent changes. S3 Lifecycle manages object storage, while Glue Crawlers discover metadata. Therefore, CDC is the appropriate AWS DMS capability when ongoing database changes need to be replicated and processed.

Question 348:

A data engineer wants to execute a recurring data-processing workflow every night at a specified time. Which AWS service can provide a scheduled trigger?

1. Amazon Macie
2. EventBridge Scheduler
3. Amazon S3 Glacier
4. Amazon ElastiCache

Correct Answer: 2

Explanation:

Amazon EventBridge Scheduler can invoke supported AWS targets according to a defined schedule. A data engineer can configure a recurring schedule, such as every night at a particular time, to start a workflow, invoke a Lambda function, or trigger another supported target. This eliminates the need for a continuously running scheduler application. Macie is focused on sensitive-data discovery, S3 Glacier provides archival storage, and ElastiCache provides in-memory caching. Therefore, EventBridge Scheduler is the appropriate service for initiating recurring data-processing workflows at predetermined times.

Question 349:

A company stores confidential data in Amazon S3 and wants to ensure that objects are encrypted at rest using AWS-managed encryption keys without managing customer keys. Which option should be selected?

1. SSE-S3
2. SSE-KMS with a customer-managed key
3. Client-side encryption only
4. TLS

Correct Answer: 1

Explanation:

Amazon S3 Server-Side Encryption with Amazon S3 managed keys, commonly referred to as SSE-S3, provides encryption at rest without requiring the customer to manage the encryption keys directly. Amazon S3 handles the encryption and key management processes. SSE-KMS can provide additional control through AWS KMS, particularly when customer-managed keys are used, but it introduces additional key-management considerations. TLS protects data during transmission rather than at rest. Therefore, SSE-S3 is appropriate when the organization requires server-side encryption at rest while allowing AWS to manage the encryption keys.

Question 350:

A data engineer wants to identify inefficient Athena queries and analyze query execution information for troubleshooting. Which capability is most useful?

1. Athena query history and execution details
2. S3 Versioning
3. Kinesis partition keys
4. DynamoDB Streams

Correct Answer: 1

Explanation:

Amazon Athena provides query history and execution information that can help data engineers investigate query behavior and performance. By reviewing query execution details, a data engineer can identify queries that scan excessive amounts of data, take longer than expected, or need optimization through partitioning, columnar formats, or better predicates. S3 Versioning protects previous object versions, Kinesis partition keys control stream record distribution, and DynamoDB Streams capture item-level changes. Therefore, Athena query history and execution details are the appropriate tools for investigating and optimizing Athena query performance.

Question 351:

A company wants to process records from DynamoDB whenever items in a table are inserted or modified. Which feature should be enabled?

1. DynamoDB Streams
2. S3 Inventory
3. Athena partitions
4. Redshift Spectrum

Correct Answer: 1

Explanation:

DynamoDB Streams captures information about item-level changes in a DynamoDB table. When an item is inserted, updated, or deleted, DynamoDB Streams can record the corresponding change information. Applications and AWS services can consume these stream records to build event-driven workflows, synchronization pipelines, analytics processes, or downstream processing systems. S3 Inventory provides object metadata reports, Athena partitions organize analytical data, and Redshift Spectrum allows querying external data sources. Therefore, DynamoDB Streams is the appropriate feature when a data pipeline needs to react to changes occurring in a DynamoDB table.

Question 352:

A data engineer wants to query historical data stored in Amazon S3 directly from Amazon Redshift without first loading all of the data into local Redshift tables. Which capability should be considered?

1. Redshift Spectrum
2. ElastiCache
3. DynamoDB Streams
4. AWS Secrets Manager

Correct Answer: 1

Explanation:

Amazon Redshift Spectrum allows Redshift users to query data stored externally, including data in Amazon S3, using SQL. This can be useful when organizations have large data lake datasets that do not need to be fully loaded into Redshift-managed storage. External tables can provide access to S3 data while allowing analytical queries to combine external data with data stored in Redshift. ElastiCache is an in-memory caching service, DynamoDB Streams captures database changes, and Secrets Manager manages sensitive credentials. Therefore, Redshift Spectrum is the appropriate capability for querying S3-based data directly from Redshift.

Question 353:

A data pipeline must execute one processing branch for successful records and a different branch when processing fails. Which Step Functions capability supports this behavior?

1. Catch
2. S3 Versioning
3. Glue Crawler
4. Kinesis Shard Iterator

Correct Answer: 1

Explanation:

AWS Step Functions supports Catch handlers that allow workflows to respond to errors and route execution to a designated state. A workflow can use Catch to handle a failed task by moving to an error-processing branch, logging the issue, notifying an operator, or storing failed data for later investigation. This provides explicit error-handling logic within the state machine. S3 Versioning is unrelated to workflow execution, Glue Crawlers discover data metadata, and Kinesis shard iterators are used for reading records from Kinesis streams. Therefore, the Catch capability is the appropriate Step Functions feature for routing failed executions.

Question 354:

A company needs to store data in Amazon S3 so that users can query only the partitions relevant to their business unit. Which design can improve query efficiency?

1. Store all data in one unpartitioned file
2. Partition the dataset using an appropriate business attribute
3. Convert all files to XML
4. Disable compression

Correct Answer: 2

Explanation:

Partitioning divides a dataset into separate logical subsets based on one or more attributes. For example, a dataset could be partitioned by business unit, date, or region. When Athena queries include predicates on the partition columns, the service can avoid scanning irrelevant partitions. This reduces the amount of data processed and can improve query performance and cost efficiency. Keeping everything in one unpartitioned dataset prevents partition pruning, while XML and disabling compression generally do not improve analytical efficiency. Therefore, partitioning the dataset using an appropriate business attribute is a useful design for efficient querying.

Question 355:

A data engineer needs to encrypt an Amazon S3 dataset and also audit usage of the encryption key through AWS KMS. Which solution is most appropriate?

1. SSE-S3
2. SSE-KMS
3. S3 Transfer Acceleration
4. S3 Lifecycle

Correct Answer: 2

Explanation:

S3 SSE-KMS uses AWS Key Management Service to provide server-side encryption for objects stored in Amazon S3. AWS KMS provides centralized key management and can integrate with AWS logging and auditing mechanisms, allowing organizations to monitor key usage and control permissions. This is useful when security requirements include both encryption at rest and greater visibility into key-related operations. SSE-S3 uses S3-managed keys and provides less direct customer control over key management. Transfer Acceleration concerns network transfer performance, while Lifecycle controls storage transitions and expiration. Therefore, SSE-KMS is the appropriate solution for encryption with KMS-based key management and auditing.

Question 356:

A company wants to run Apache Spark jobs without manually installing Spark, Hadoop, and other big-data components. Which AWS service should be used?

1. Amazon EMR
2. Amazon S3
3. Amazon Athena
4. Amazon EventBridge

Correct Answer: 1

Explanation:

Amazon EMR is a managed big-data platform that supports frameworks such as Apache Spark and Hadoop. It simplifies the process of creating and running distributed processing environments because AWS manages much of the underlying infrastructure and provides supported big-data software configurations. Data engineers can use EMR to process large datasets stored in Amazon S3 or other sources. S3 provides object storage, Athena is a serverless SQL query service, and EventBridge handles events and scheduling. Therefore, Amazon EMR is the most appropriate service when a data engineer needs managed infrastructure for Apache Spark workloads.

Question 357:

A data engineer wants to monitor AWS API calls made against resources in an AWS account for security and compliance investigations. Which service should be used?

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

Correct Answer: 2

Explanation:

AWS CloudTrail records API activity and provides an audit trail of actions performed through AWS accounts and services. It can help organizations determine which identity performed an action, when the action occurred, and what resource was involved. This information is valuable for security investigations, compliance auditing, and operational troubleshooting. CloudWatch focuses primarily on metrics, logs, and monitoring, while Glue is used for data integration and Athena is used for querying data. Therefore, AWS CloudTrail is the appropriate service for monitoring and investigating AWS API activity across an account.

Question 358:

A company wants to prevent an AWS Glue job from repeatedly processing the same S3 files during scheduled runs. Which feature should be enabled?

1. Glue job bookmarks
2. S3 Transfer Acceleration
3. Redshift distribution keys
4. Athena workgroups

Correct Answer: 1

Explanation:

AWS Glue job bookmarks maintain information about previously processed data and can help ETL jobs process new or changed input incrementally. This is useful when scheduled jobs repeatedly process data from sources such as Amazon S3. Instead of treating every execution as a completely new workload, the job can use bookmark state to identify data that has already been processed. S3 Transfer Acceleration improves data transfer speed, Redshift distribution keys control data placement, and Athena workgroups manage query environments. Therefore, Glue job bookmarks are the correct feature for avoiding unnecessary reprocessing of previously handled input data.

Question 359:

A data engineer wants to create an event-driven architecture where an S3 object creation event is routed only when the object key matches a specific prefix. Which service provides this filtering capability?

1. Amazon EventBridge
2. Amazon RDS
3. Amazon Macie
4. Amazon ElastiCache

Correct Answer: 1

Explanation:

Amazon EventBridge supports event patterns that can filter incoming events based on event attributes. For Amazon S3 object events, rules can be designed to route only events matching specific characteristics, such as a bucket or object key pattern. This allows data pipelines to respond only to relevant events instead of triggering for every object created in a bucket. RDS provides relational databases, Macie focuses on sensitive-data discovery, and ElastiCache provides in-memory caching. Therefore, EventBridge is the appropriate service for routing and filtering S3 events based on specific event attributes.

Question 360:

A company wants to improve Amazon Athena performance and reduce query costs for a dataset containing millions of records. The data is frequently queried by date. Which design is most appropriate?

1. Store all records in one large CSV file
2. Partition the data by date and use Parquet
3. Convert Parquet data to XML
4. Disable compression and partitioning

Correct Answer: 2

Explanation:

Partitioning data by a frequently queried attribute such as date allows Athena to scan only the relevant partitions when queries include date predicates. Storing the data in Parquet further improves efficiency because Parquet is a columnar format that supports column pruning and efficient compression. Together, partitioning and Parquet can significantly reduce the amount of data Athena needs to scan, improving performance and lowering query costs. A large unpartitioned CSV file can require much more data to be scanned, while XML and disabled compression are generally inefficient for analytical workloads. Therefore, partitioning by date and using Parquet is the best design.