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

 

Question 21

A data engineer needs to ingest data from multiple sources, including databases and applications, and deliver it to Amazon S3 without managing servers. Which AWS service is most appropriate?

1. Amazon Data Firehose
2. Amazon RDS
3. AWS Lambda
4. Amazon QuickSight

Correct Answer: 1

Explanation:

Amazon Data Firehose is a fully managed service that can ingest streaming data and deliver it to destinations such as Amazon S3, Amazon Redshift, and Amazon OpenSearch Service. It automatically manages scaling, buffering, and delivery infrastructure, reducing operational overhead for data engineers. Amazon RDS is designed for relational databases, Lambda provides serverless compute for running code, and QuickSight is a business intelligence service. Therefore, Amazon Data Firehose is the most appropriate choice when continuously arriving data needs to be delivered to an analytics destination such as Amazon S3 without requiring the data engineer to manage servers.

Question 22

A company has a large dataset in Amazon S3 and wants to reduce the amount of data scanned by Amazon Athena queries. Which combination is most effective?

1. Store all data as uncompressed CSV files
2. Use partitioning and columnar formats such as Parquet
3. Store the data in Amazon EBS
4. Convert all data to JSON

Correct Answer: 2

Explanation:

Partitioning and columnar formats such as Apache Parquet can significantly improve Amazon Athena query efficiency. Partitioning allows Athena to eliminate unnecessary partitions when a query includes appropriate filter conditions. Parquet stores data by columns, allowing Athena to read only the columns required by a query instead of scanning the entire dataset. Compression can further reduce the amount of data that needs to be processed. CSV and JSON generally require more data to be scanned for analytical workloads. Amazon EBS is block storage designed primarily for compute instances. Therefore, combining partitioning with columnar formats such as Parquet is an effective strategy for reducing Athena scan volume and improving query performance.

Question 23

A data engineer needs to create a centralized repository containing metadata about tables, schemas, and data locations used by multiple analytics services. Which AWS service should be used?

1. Amazon DynamoDB
2. Amazon SQS
3. AWS Glue Data Catalog
4. Amazon ElastiCache

Correct Answer: 3

Explanation:

AWS Glue Data Catalog provides a centralized metadata repository for datasets used by AWS analytics services. It can store table definitions, schemas, partition information, and the locations of data in services such as Amazon S3. Multiple services, including Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum, can use this metadata to understand and query datasets. DynamoDB is a NoSQL database, SQS is a message queuing service, and ElastiCache provides in-memory caching. Therefore, AWS Glue Data Catalog is the best choice when a data engineer needs a centralized metadata repository that can be shared across multiple analytics and data processing services.

Question 24

A company needs to process records as they arrive and execute custom code for each event without managing servers. Which AWS service is most appropriate for the processing component?

1. Amazon RDS
2. Amazon Redshift
3. Amazon EBS
4. AWS Lambda

Correct Answer: 4

Explanation:

AWS Lambda is a serverless compute service that can execute code in response to events without requiring the organization to provision or manage servers. It can process individual events and integrate with services such as Amazon Kinesis, Amazon S3, Amazon EventBridge, and Amazon SQS. Lambda automatically scales based on incoming workloads within its configured limits. Amazon RDS and Redshift are database services, while Amazon EBS provides block storage for EC2 instances. Therefore, Lambda is an appropriate choice when a data engineer needs to execute custom processing logic automatically as records or events arrive.

Question 25

A data engineer needs to securely provide an application running on Amazon EC2 with access to an Amazon S3 bucket without storing long-term AWS access keys on the server. What should be used?

1. An IAM role attached to the EC2 instance
2. An IAM user with access keys stored in a file
3. The root account credentials
4. A hard-coded access key in the application

Correct Answer: 1

Explanation:

An IAM role attached to an Amazon EC2 instance allows applications running on that instance to obtain temporary security credentials for accessing AWS resources. This approach avoids storing long-term access keys in application code or configuration files. The role can be assigned a policy that grants only the required permissions to the S3 bucket, following the principle of least privilege. Storing IAM user credentials or hard-coding access keys creates additional security risks. Root credentials should never be used for application access. Therefore, attaching an appropriately permissioned IAM role to the EC2 instance is the recommended and secure approach.

Question 26

A company receives duplicate records in a streaming data pipeline and needs to remove duplicates before storing the data in Amazon S3. Which data processing concept addresses this requirement?

1. Partitioning
2. Deduplication
3. Encryption
4. Compression

Correct Answer: 2

Explanation:

Deduplication is the process of identifying and removing duplicate records from a dataset or data stream. It is particularly important in data pipelines where retries, repeated events, or source-system behavior can result in the same record being delivered more than once. A data engineer can use identifiers, timestamps, hashes, or other business keys to determine whether records are duplicates. Partitioning organizes data based on specific attributes, encryption protects information, and compression reduces storage or transfer size. Therefore, deduplication is the appropriate data processing concept when the objective is to ensure that duplicate records are removed before data is stored or analyzed.

Question 27

A data engineer wants to monitor the execution of AWS Glue ETL jobs and receive an alert when a job fails. Which AWS service can be used for monitoring and alarms?

1. Amazon Route 53
2. AWS KMS
3. Amazon CloudWatch
4. Amazon S3 Glacier

Correct Answer: 3

Explanation:

Amazon CloudWatch provides monitoring and observability capabilities for AWS resources and applications. It can collect metrics and logs and can be configured with alarms that respond to specific conditions. A data engineer can use CloudWatch to monitor AWS Glue jobs and create notifications or automated responses when failures or other conditions occur. Route 53 provides DNS functionality, AWS KMS manages encryption keys, and S3 Glacier provides archival storage. Therefore, CloudWatch is the appropriate service for monitoring Glue ETL workloads and creating alarms based on operational conditions such as job failures or abnormal behavior.

Question 28

A company needs to store frequently accessed analytical data in a data warehouse and run complex SQL queries across billions of rows. Which AWS service is designed for this workload?

1. Amazon DynamoDB
2. Amazon Redshift
3. Amazon SQS
4. Amazon ElastiCache

Correct Answer: 2

Explanation:

Amazon Redshift is a cloud data warehouse designed for large-scale analytical workloads. It supports SQL queries across large datasets and is optimized for complex aggregations, reporting, business intelligence, and analytical processing. Redshift uses a column-oriented architecture and other optimization techniques to support data warehouse workloads efficiently. DynamoDB is a NoSQL database primarily designed for high-scale operational applications, SQS is a message queuing service, and ElastiCache provides low-latency in-memory caching. Therefore, Amazon Redshift is the most appropriate service when a company needs to store analytical data and execute complex SQL queries over very large datasets.

Question 29

A data engineer wants to ensure that an AWS Glue ETL job can access data stored in a private Amazon S3 bucket. Which security mechanism should primarily control this access?

1. IAM permissions
2. Amazon CloudFront
3. Amazon Route 53
4. Amazon QuickSight

Correct Answer: 1

Explanation:

IAM permissions are a fundamental mechanism for controlling access to AWS resources, including Amazon S3 data accessed by AWS Glue jobs. A Glue job can assume an IAM role that contains permissions required to read from or write to specific S3 buckets and objects. The policy should follow the principle of least privilege and grant only the permissions necessary for the job. CloudFront is a content delivery service, Route 53 provides DNS services, and QuickSight is a business intelligence platform. Therefore, IAM permissions associated with the Glue job’s execution role are the appropriate primary mechanism for controlling access to private S3 data.

Question 30

A company wants to send messages between application components so that producers and consumers do not need to operate at exactly the same time. Which AWS service should be used?

1. Amazon Athena
2. Amazon Redshift
3. Amazon SQS
4. AWS Glue

Correct Answer: 3

Explanation:

Amazon Simple Queue Service, or Amazon SQS, is a managed message queuing service that enables application components to communicate asynchronously. Producers can place messages into a queue, and consumers can retrieve and process those messages later. This decouples application components and helps applications handle temporary differences in processing speed or availability. Amazon Athena is used for querying data, Amazon Redshift is a data warehouse, and AWS Glue provides data integration and ETL capabilities. Therefore, SQS is the appropriate service when an application architecture requires asynchronous communication between producers and consumers without requiring both components to operate simultaneously.

Question 31

A data engineer needs to perform a one-time SQL analysis of data stored in Amazon S3 without creating or managing a database cluster. Which service is the best choice?

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

Correct Answer: 1

Explanation:

Amazon Athena is a serverless query service that allows users to run SQL queries directly against data stored in Amazon S3. It does not require the creation or management of database servers or clusters, making it especially suitable for ad hoc and one-time analytical queries. Users can define the schema and execute queries using standard SQL. Amazon RDS requires a managed relational database instance, Neptune is designed for graph workloads, and DynamoDB is a NoSQL database. Therefore, Athena is the most appropriate choice when a data engineer needs to perform a one-time SQL analysis of S3 data without managing dedicated database infrastructure.

Question 32

A company needs to move large amounts of data from an on-premises environment to Amazon S3 using a managed transfer service. Which AWS service is appropriate?

1. Amazon Athena
2. AWS DataSync
3. Amazon QuickSight
4. Amazon ElastiCache

Correct Answer: 2

Explanation:

AWS DataSync is a managed data transfer service designed to move large amounts of data between on-premises storage systems and AWS storage services. It can automate and accelerate data transfers while providing monitoring and scheduling capabilities. DataSync supports transfers to destinations such as Amazon S3, Amazon EFS, and Amazon FSx. Athena is used for querying data, QuickSight provides business intelligence, and ElastiCache provides in-memory caching. Therefore, AWS DataSync is the most appropriate service when a company needs to transfer substantial amounts of data from an on-premises environment to Amazon S3 using a managed solution.

Question 33

A data engineer wants to automatically start an AWS Glue workflow when a new object is uploaded to an Amazon S3 bucket. Which AWS service can be used to detect the event and initiate the workflow?

1. Amazon S3 event notifications with an event-driven service
2. Amazon RDS
3. Amazon EBS
4. Amazon Neptune

Correct Answer: 1

Explanation:

Amazon S3 can generate event notifications when actions such as object creation occur. These events can be integrated with event-driven AWS services to initiate processing workflows, including AWS Glue workflows or other automated components. This architecture allows data processing to begin automatically when new data arrives rather than relying on manual execution or constant polling. Amazon RDS and Neptune are database services, while EBS provides block storage for EC2. Therefore, using S3 event notifications with an appropriate event-driven integration is a suitable approach for automatically triggering data processing when new objects are uploaded to an S3 bucket.

Question 34

A company wants to analyze data from multiple sources using a centralized analytical repository while supporting complex queries and reporting. Which architecture component is most appropriate?

1. Amazon SQS
2. Amazon Redshift
3. AWS IAM
4. Amazon Route 53

Correct Answer: 2

Explanation:

Amazon Redshift is designed to serve as a centralized analytical data warehouse where organizations can consolidate data from multiple sources for reporting and complex analysis. It supports SQL and provides capabilities designed for large-scale analytical workloads. Data can be loaded into Redshift through various ingestion and integration mechanisms, allowing organizations to create a consistent analytical environment. SQS is a messaging service, IAM controls access to AWS resources, and Route 53 provides DNS functionality. Therefore, Amazon Redshift is the most appropriate architecture component when the requirement is to maintain a centralized analytical repository that supports complex queries, reporting, and business intelligence workloads.

Question 35

A data engineer wants to protect sensitive information in a dataset by replacing personally identifiable values with non-sensitive substitute values while retaining the ability to map them when authorized. Which technique is most appropriate?

1. Partitioning
2. Tokenization
3. Compression
4. Replication

Correct Answer: 2

Explanation:

Tokenization replaces sensitive values with non-sensitive tokens that can be mapped back to the original values under controlled conditions. It is commonly used to protect personally identifiable information and other sensitive data while allowing systems to process a representation of the original value. Partitioning organizes datasets, compression reduces storage requirements, and replication creates additional copies of data. Tokenization can help reduce the exposure of sensitive values in analytical environments, although its implementation must include appropriate security controls for the mapping information. Therefore, tokenization is the most suitable technique when sensitive values need to be replaced while maintaining the possibility of authorized mapping.

Question 36

A company stores data in Amazon S3 and wants to automatically move older, infrequently accessed objects to lower-cost storage classes. Which S3 feature should be used?

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

Correct Answer: 1

Explanation:

Amazon S3 Lifecycle rules allow organizations to automatically transition objects between storage classes or expire objects based on defined conditions such as object age. For example, frequently accessed data can initially be stored in S3 Standard and later transitioned to an appropriate infrequent-access or archival storage class. This can reduce storage costs while maintaining data according to business requirements. S3 Access Points simplify access management, Transfer Acceleration helps speed up uploads over long distances, and Object Lock helps protect objects from deletion or modification. Therefore, S3 Lifecycle rules are the appropriate feature for automatically moving older data to lower-cost storage classes.

Question 37

A data engineer needs to query only specific columns from a large Parquet dataset in Amazon S3. Which characteristic of Parquet helps improve query efficiency?

1. Row-level encryption
2. Columnar storage
3. Object versioning
4. DNS resolution

Correct Answer: 2

Explanation:

Parquet uses a columnar storage format, meaning data is organized by columns rather than storing complete records together in rows. This allows analytical query engines such as Amazon Athena to read only the columns required by a query instead of scanning every column in the dataset. For large analytical datasets, this can significantly reduce data scanned and improve query performance. Row-level encryption is not the defining feature that provides this benefit, while S3 object versioning and DNS resolution are unrelated to Parquet’s query efficiency. Therefore, columnar storage is the key characteristic that makes Parquet highly efficient for analytical workloads.

Question 38

A company wants to ensure that an ETL process can retry a failed processing step without restarting the entire workflow. Which capability is important when designing the workflow?

1. Error handling and retry logic
2. DNS routing
3. Object versioning only
4. Static IP allocation

Correct Answer: 1

Explanation:

Error handling and retry logic are important capabilities when designing reliable data processing workflows. If an individual ETL step fails because of a temporary issue, such as a service interruption or transient network problem, the workflow can retry that specific step rather than restarting the entire pipeline. Services such as AWS Step Functions provide mechanisms for retries, catches, and conditional workflow behavior. DNS routing and static IP allocation do not provide ETL recovery functionality. S3 object versioning can protect previous object versions but does not itself orchestrate ETL retries. Therefore, implementing appropriate error handling and retry logic improves pipeline reliability and reduces unnecessary reprocessing.

Question 39

A data engineer needs to identify sensitive data such as personally identifiable information in Amazon S3 objects. Which AWS service is designed to help discover sensitive data in S3?

1. Amazon CloudFront
2. Amazon Macie
3. Amazon Route 53
4. Amazon SQS

Correct Answer: 2

Explanation:

Amazon Macie is a data security and privacy service designed to discover and help protect sensitive data stored in Amazon S3. It can use machine learning and pattern matching to identify sensitive information, including certain types of personally identifiable information. This capability helps organizations understand where sensitive data is located and improve data protection and compliance practices. CloudFront is a content delivery network, Route 53 provides DNS services, and SQS provides message queuing. Therefore, Amazon Macie is the most appropriate service when a data engineer or security team needs to identify sensitive information stored in Amazon S3.

Question 40

A company wants to provide analysts with a governed data lake where permissions can be managed centrally for datasets stored in Amazon S3. Which AWS service is designed to help centrally manage fine-grained access to data lake resources?

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

Correct Answer: 3

Explanation:

AWS Lake Formation helps organizations build, secure, and manage data lakes on AWS. It provides centralized capabilities for configuring permissions and managing access to data stored in services such as Amazon S3. Lake Formation can help implement fine-grained access controls for databases, tables, and columns and integrates with AWS analytics services. CloudFront is designed for content delivery, ElastiCache provides in-memory caching, and Route 53 provides DNS services. Therefore, AWS Lake Formation is the most appropriate choice when a company needs centralized governance and fine-grained permissions for data lake resources and wants to provide controlled access to datasets for analysts.