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

 

Question 101

A data engineer needs to process data from Amazon S3 using SQL and wants to store the query results back in Amazon S3. Which service is most appropriate?

1. Amazon RDS
2. Amazon Athena
3. Amazon DynamoDB
4. Amazon ElastiCache

Correct Answer: 2

Explanation:

Amazon Athena is a serverless interactive query service that allows data engineers to analyze datasets stored in Amazon S3 using standard SQL. Athena can also write query results to Amazon S3, making it useful for creating derived datasets, reports, and intermediate analytical results. It does not require users to provision or manage database infrastructure. Amazon RDS is a managed relational database service, DynamoDB is a NoSQL database, and ElastiCache is designed for in-memory caching. Therefore, Amazon Athena is the most appropriate choice when SQL queries need to process S3 data and the resulting output needs to be stored in S3.

Question 102

A company wants to automatically discover new tables and schemas in data stored in Amazon S3. Which AWS Glue component should be used?

1. AWS Glue Crawler
2. AWS Glue Trigger
3. AWS Glue Connection
4. AWS Glue Workflow

Correct Answer: 1

Explanation:

AWS Glue Crawlers automatically scan supported data stores such as Amazon S3 and discover information about datasets, including schemas, data types, partitions, and table structures. The discovered metadata can then be stored in the AWS Glue Data Catalog and used by analytics services such as Amazon Athena. Glue Triggers are used to start jobs or workflows based on schedules or events, while Connections provide configuration information for connecting to certain data sources. Glue Workflows coordinate multiple processing activities. Therefore, an AWS Glue Crawler is the appropriate component when the primary requirement is automatically discovering schemas and creating or updating metadata in the Data Catalog.

Question 103

A data engineer needs to create a workflow where an AWS Glue job runs only after another Glue job completes successfully. Which AWS Glue feature can be used?

1. AWS Glue Data Catalog
2. AWS Glue Trigger
3. AWS Glue Crawler
4. AWS Glue Schema Registry

Correct Answer: 2

Explanation:

AWS Glue Triggers can start Glue jobs and crawlers based on different conditions, including the completion state of previous jobs. A conditional trigger can be configured so that a downstream job starts only when an upstream job completes successfully. This helps data engineers build controlled ETL workflows with dependencies between processing steps. The Glue Data Catalog stores metadata, Crawlers discover schemas, and Schema Registry manages schemas for streaming applications. Therefore, an AWS Glue Trigger is the appropriate feature when one Glue job must start based on the successful completion of another job.

Question 104

A company receives streaming data from multiple applications and wants to deliver that data to Amazon S3 without writing custom data delivery infrastructure. Which service should be used?

1. Amazon Redshift
2. Amazon Data Firehose
3. Amazon RDS
4. Amazon EBS

Correct Answer: 2

Explanation:

Amazon Data Firehose is a fully 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 many infrastructure tasks, including buffering, scaling, and delivery management. This makes it useful when organizations need to continuously deliver streaming data without building and maintaining their own delivery systems. Redshift is a data warehouse, RDS provides relational databases, and EBS provides block storage. Therefore, Amazon Data Firehose is the best choice when streaming data needs to be delivered continuously to Amazon S3 with minimal infrastructure management.

Question 105

A data engineer wants to make a data processing pipeline idempotent so that processing the same input multiple times does not create duplicate results. Which approach is most appropriate?

1. Use a unique record identifier and deduplication logic
2. Disable all error handling
3. Store multiple copies of every record
4. Remove validation checks

Correct Answer: 1

Explanation:

An idempotent data pipeline produces the same intended result even if the same input is processed more than once. A common approach is to assign or use a unique record identifier and apply deduplication or upsert logic when writing the results. This is particularly useful in distributed systems where retries can cause the same event to be processed multiple times. Storing multiple copies increases duplication, while disabling error handling and removing validation reduce pipeline reliability and data quality. Therefore, using unique identifiers together with appropriate deduplication logic is an effective way to make data processing idempotent and prevent duplicate results.

Question 106

A company wants to query data stored in Amazon S3 using Athena and reduce query costs. Which action is most effective?

1. Use uncompressed CSV files
2. Scan the entire dataset for every query
3. Use partitioning and compressed columnar formats
4. Duplicate the dataset into multiple S3 buckets

Correct Answer: 3

Explanation:

Amazon Athena pricing is based primarily on the amount of data scanned by queries, so reducing scanned data can lower costs. Partitioning allows Athena to skip irrelevant portions of a dataset when appropriate filters are applied. Columnar formats such as Parquet allow Athena to read only the columns required by a query, while compression reduces the physical amount of data that must be scanned. Uncompressed CSV files can result in larger scan volumes, scanning the entire dataset is inefficient, and duplicating datasets increases storage without necessarily improving query efficiency. Therefore, combining partitioning with compressed columnar formats is an effective strategy for reducing Athena query costs.

Question 107

A data engineer needs to monitor AWS Glue job execution time, failures, and other operational metrics. Which AWS service should be used?

1. AWS CloudTrail
2. Amazon CloudWatch
3. AWS KMS
4. Amazon Macie

Correct Answer: 2

Explanation:

Amazon CloudWatch provides monitoring and observability capabilities for AWS resources and applications. It can collect metrics, logs, and events and can be used to create dashboards and alarms. Data engineers can use CloudWatch to monitor AWS Glue jobs, investigate failures, track execution behavior, and configure alerts when specific conditions occur. CloudTrail primarily records API activity, KMS manages encryption keys, and Macie focuses on discovering sensitive data in Amazon S3. Therefore, CloudWatch is the most appropriate service for monitoring Glue job execution, failures, and operational metrics and for helping data engineers identify issues in data processing pipelines.

Question 108

A company wants to restrict an IAM role so that it can read objects from only one specific Amazon S3 bucket. Which security principle should be applied?

1. Least privilege
2. Eventual consistency
3. Horizontal scaling
4. Data replication

Correct Answer: 1

Explanation:

The principle of least privilege means granting an identity only the permissions necessary to perform its required tasks. If an IAM role only needs to read objects from one S3 bucket, its policy should grant read permissions only to that bucket and the required objects rather than granting broad access to all S3 resources. This reduces the potential impact of compromised credentials or unintended actions. Eventual consistency is a data behavior concept, horizontal scaling concerns infrastructure capacity, and replication creates additional copies of data. Therefore, least privilege is the correct security principle for restricting an IAM role to only the S3 resources it actually needs.

Question 109

A company needs to create a centralized repository for storing schemas used by streaming applications. Which AWS service is most appropriate?

1. AWS Glue Schema Registry
2. Amazon CloudFront
3. Amazon EBS
4. Amazon Route 53

Correct Answer: 1

Explanation:

AWS Glue Schema Registry provides a centralized location for managing and storing schemas used by streaming applications and data processing systems. It helps organizations define, validate, and evolve schemas while maintaining consistency between producers and consumers. This can be particularly useful in streaming architectures where different applications exchange structured event data. CloudFront is a content delivery service, EBS provides block storage, and Route 53 provides DNS services. Therefore, AWS Glue Schema Registry is the most appropriate choice when a company needs centralized schema management for streaming applications and wants to maintain consistency as data structures evolve.

Question 110

A data engineer needs to join two large datasets stored in Amazon S3 before performing an analytical query. Which AWS service can provide serverless SQL-based processing without requiring a database cluster?

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

Correct Answer: 1

Explanation:

Amazon Athena provides serverless SQL querying against data stored in Amazon S3. A data engineer can define tables using metadata from the AWS Glue Data Catalog and perform SQL operations such as JOINs across datasets. Athena removes the need to provision and manage a dedicated database cluster for many ad hoc analytical workloads. ElastiCache is an in-memory caching service, Route 53 provides DNS, and SQS provides message queuing. Therefore, Amazon Athena is an appropriate choice when large datasets in S3 need to be joined and analyzed using SQL without managing dedicated database infrastructure.

Question 111

A company needs to protect data stored in Amazon S3 from accidental deletion while also allowing authorized users to access the data normally. Which feature can provide object immutability?

1. S3 Transfer Acceleration
2. S3 Object Lock
3. S3 Intelligent-Tiering
4. S3 Access Points

Correct Answer: 2

Explanation:

S3 Object Lock provides object immutability by preventing objects from being deleted or overwritten for a specified retention period or while a legal hold is active. It is commonly used for compliance and regulatory workloads where certain records must remain unchanged. Authorized users can still access the protected objects according to the applicable permissions, but deletion or modification is restricted according to the Object Lock configuration. Transfer Acceleration improves transfer speeds, Intelligent-Tiering optimizes storage costs, and Access Points provide access management. Therefore, S3 Object Lock is the appropriate feature for protecting objects from accidental deletion while maintaining controlled access.

Question 112

A data engineer wants to run a data processing workflow whenever a new object is uploaded to Amazon S3. Which architecture is most appropriate?

1. S3 event notification triggering an AWS Lambda function
2. Amazon Route 53 triggering Amazon RDS
3. Amazon CloudFront triggering Amazon EBS
4. Amazon Athena triggering Amazon SQS manually

Correct Answer: 1

Explanation:

Amazon S3 can generate event notifications when objects are created. These events can trigger AWS Lambda functions, allowing data processing to begin automatically whenever a new object arrives. The Lambda function can validate the file, initiate an ETL job, transform metadata, or invoke another workflow. This creates an event-driven architecture that avoids continuous polling or manual execution. Route 53, CloudFront, and EBS serve different purposes and are not designed for this event-driven S3 processing pattern. Therefore, using an S3 event notification to trigger a Lambda function is an appropriate architecture for automatically starting processing whenever new data is uploaded.

Question 113

A company wants to store historical data that is rarely accessed but must be retained for several years at the lowest possible storage cost. Which S3 storage class is most appropriate?

1. S3 Standard
2. S3 Intelligent-Tiering
3. S3 Glacier Deep Archive
4. S3 One Zone-IA

Correct Answer: 3

Explanation:

S3 Glacier Deep Archive is designed for long-term archival data that is rarely accessed and can tolerate longer retrieval times. It generally provides very low storage costs compared with storage classes intended for frequently accessed data. It is suitable for regulatory records, long-term backups, and historical datasets that must be retained for years but are unlikely to be retrieved frequently. S3 Standard is intended for frequent access, Intelligent-Tiering is useful when access patterns change, and One Zone-IA is designed for infrequently accessed data that can tolerate single-zone storage. Therefore, Glacier Deep Archive is the most appropriate choice for long-term, rarely accessed historical data.

Question 114

A data engineer needs to analyze a dataset containing billions of records and perform complex aggregations using a scalable data warehouse. Which AWS service should be used?

1. Amazon Redshift
2. Amazon SQS
3. Amazon Route 53
4. Amazon ElastiCache

Correct Answer: 1

Explanation:

Amazon Redshift is a managed cloud data warehouse designed for large-scale analytical workloads. It supports complex SQL queries, aggregations, reporting, and business intelligence workloads across very large datasets. Redshift uses a distributed architecture and columnar storage techniques to efficiently process analytical queries. SQS is a message queuing service, Route 53 provides DNS functionality, and ElastiCache is designed for low-latency in-memory caching. Therefore, Amazon Redshift is the most appropriate service when a data engineer needs a scalable analytical data warehouse capable of processing billions of records and executing complex aggregation queries.

Question 115

A company wants to identify sensitive data stored in S3 and understand which buckets contain potentially confidential information. Which AWS service should be used?

1. Amazon Macie
2. Amazon Athena
3. AWS Step Functions
4. Amazon Redshift

Correct Answer: 1

Explanation:

Amazon Macie is designed to help organizations discover and protect sensitive data stored in Amazon S3. It can identify potentially sensitive information using automated data discovery and pattern matching capabilities and can provide findings about the locations and types of sensitive data detected. Athena is a query service, Step Functions is a workflow orchestration service, and Redshift is a data warehouse. Therefore, Amazon Macie is the most appropriate AWS service when an organization needs to identify sensitive or potentially confidential information stored across S3 buckets. Macie can help security and data teams improve visibility and support privacy and compliance requirements.

Question 116

A data engineer wants to ensure that an ETL pipeline does not load records with invalid email addresses into a curated dataset. Which data engineering practice should be implemented?

1. Data validation
2. Data replication
3. Data archival
4. Data compression

Correct Answer: 1

Explanation:

Data validation ensures that records meet predefined quality rules before they are loaded into downstream systems. In this example, the pipeline can validate email addresses against an expected format and reject, quarantine, or flag records that fail the validation rule. This helps prevent poor-quality data from entering curated datasets and improves trust in analytical results. Data replication creates copies, archival moves data for long-term retention, and compression reduces storage requirements. None of these techniques directly verify whether an email address is valid. Therefore, data validation is the appropriate practice for enforcing data quality rules during an ETL pipeline.

Question 117

A company wants to decouple a data producer from a consumer so that messages can be processed even if the consumer is temporarily unavailable. Which service should be used?

1. Amazon SQS
2. Amazon CloudFront
3. Amazon Athena
4. Amazon QuickSight

Correct Answer: 1

Explanation:

Amazon Simple Queue Service (SQS) provides managed message queues that allow producers and consumers to operate independently. A producer can send messages to an SQS queue, and the messages can remain available until a consumer retrieves and processes them. This decouples application components and helps systems handle temporary differences in availability or processing speed. CloudFront is a content delivery service, Athena is a serverless SQL query service, and QuickSight provides business intelligence. Therefore, Amazon SQS is the appropriate service when a company needs to decouple a producer from a consumer and ensure that messages can wait until the consumer is ready to process them.

Question 118

A data engineer needs to create a data lake architecture where raw data is stored first and transformed data is generated later for analytics. Which storage service should be the foundation of the data lake?

1. Amazon RDS
2. Amazon S3
3. Amazon DynamoDB
4. Amazon ElastiCache

Correct Answer: 2

Explanation:

Amazon S3 is commonly used as the foundation of AWS data lake architectures because it provides highly scalable object storage for large amounts of structured, semi-structured, and unstructured data. Raw data can be stored in S3 before transformation, while processed and curated versions can also be maintained in separate locations or prefixes. S3 integrates with services such as AWS Glue, Athena, EMR, and Redshift for processing and analytics. RDS and DynamoDB are database services designed for specific application workloads, while ElastiCache provides in-memory caching. Therefore, Amazon S3 is the most appropriate foundation for a scalable AWS data lake architecture.

Question 119

A company wants to automatically run a workflow when a scheduled time arrives and then coordinate multiple data processing tasks. Which combination is appropriate?

1. Amazon EventBridge and AWS Step Functions
2. Amazon Route 53 and Amazon CloudFront
3. Amazon S3 and Amazon EBS only
4. Amazon SQS and Amazon ElastiCache

Correct Answer: 1

Explanation:

Amazon EventBridge can create scheduled events that automatically start workflows at specific times or intervals. AWS Step Functions can then orchestrate multiple processing tasks, including Lambda functions, Glue jobs, and other supported AWS services. Combining these services allows organizations to build automated scheduled data pipelines without manually starting individual jobs. Route 53 and CloudFront provide networking and content delivery capabilities, while S3 and EBS provide storage and SQS provides messaging. Therefore, EventBridge combined with Step Functions is an effective architecture for scheduling a data workflow and coordinating its multiple processing stages.

Question 120

A data engineer needs to ensure that sensitive information is encrypted while stored in an Amazon S3 bucket and wants centralized control over encryption keys. Which solution is most appropriate?

1. S3 Lifecycle rules
2. AWS KMS with S3 SSE-KMS encryption
3. Amazon CloudFront
4. Amazon Athena

Correct Answer: 2

Explanation:

Amazon S3 can use server-side encryption with AWS KMS keys, known as SSE-KMS, to protect objects while they are stored. AWS KMS provides centralized management of encryption keys and allows organizations to control key permissions and auditing. S3 can automatically encrypt objects when they are uploaded, while KMS manages the cryptographic keys used for the encryption process. Lifecycle rules manage object transitions and expiration, CloudFront distributes content, and Athena queries data. Therefore, using AWS KMS with S3 SSE-KMS encryption is the appropriate solution when sensitive information needs encryption at rest together with centralized encryption-key management.