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

 

Question 41

A company needs to ingest data from an on-premises PostgreSQL database into AWS while keeping the source database operational during the migration. Which AWS service is most appropriate?

1. Amazon Athena
2. AWS Database Migration Service
3. Amazon QuickSight
4. Amazon CloudFront

Correct Answer: 2

Explanation:

AWS Database Migration Service (AWS DMS) is designed to migrate databases to AWS while keeping the source database available during the migration process. It can perform a full load and then continue replicating ongoing changes from the source to the target. This approach helps minimize downtime and supports both homogeneous and heterogeneous migrations. Amazon Athena is used for querying data, QuickSight provides business intelligence and visualization, and CloudFront is a content delivery service. Therefore, AWS DMS is the most appropriate choice when a data engineer needs to migrate an operational PostgreSQL database while maintaining source availability during the migration.

Question 42

A data engineer needs to store data in Amazon S3 and ensure that objects cannot be deleted or modified for a defined retention period. Which S3 feature should be used?

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

Correct Answer: 3

Explanation:

Amazon S3 Object Lock helps protect objects from being deleted or overwritten for a specified retention period. It is useful for compliance requirements and workloads where data must remain immutable. Object Lock supports retention periods and legal holds that can prevent unauthorized deletion or modification of protected objects. S3 Transfer Acceleration is designed to speed up transfers, Lifecycle rules manage object transitions and expiration, and Intelligent-Tiering automatically moves data between access tiers based on usage patterns. Therefore, S3 Object Lock is the correct choice when a company needs to ensure that objects remain protected from deletion or modification during a defined retention period.

Question 43

A company wants to automatically classify and organize objects in Amazon S3 based on access patterns to reduce storage costs without manually selecting storage classes. Which S3 storage class is most appropriate?

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

Correct Answer: 1

Explanation:

S3 Intelligent-Tiering is designed for data with changing or unknown access patterns. It automatically moves objects between access tiers based on how frequently they are accessed, helping organizations optimize storage costs without requiring manual lifecycle management for every object. S3 Standard is intended for frequently accessed data, while Glacier Deep Archive is designed for very long-term archival data. S3 One Zone-IA is intended for infrequently accessed data that does not require multi-Availability Zone storage resilience. Therefore, S3 Intelligent-Tiering is the most appropriate choice when access patterns are unpredictable and the company wants automatic cost optimization.

Question 44

A data engineer needs to run a SQL query against an Amazon Redshift data warehouse and return only rows matching a specific condition. Which SQL clause should be used?

1. GROUP BY
2. ORDER BY
3. WHERE
4. UNION

Correct Answer: 3

Explanation:

The SQL WHERE clause is used to filter rows based on specified conditions. For example, a query can use WHERE to return only records where a column matches a particular value or falls within a specified range. GROUP BY is used to group rows for aggregate calculations, ORDER BY sorts query results, and UNION combines the results of compatible queries. Therefore, the WHERE clause is the correct choice when a data engineer needs to restrict query results to rows that satisfy a particular condition. Proper filtering can also help analytical queries process only the relevant records required by the business logic.

Question 45

A company wants to combine rows from two compatible SQL query results into a single result set while removing duplicate rows. Which SQL operator should be used?

1. UNION
2. JOIN
3. WHERE
4. ORDER BY

Correct Answer: 1

Explanation:

The SQL UNION operator combines the results of two or more compatible SELECT statements into a single result set and removes duplicate rows by default. The participating queries generally need to return the same number of columns with compatible data types. A JOIN combines columns from related tables based on a specified relationship, while WHERE filters rows and ORDER BY sorts the final results. Therefore, UNION is the appropriate operator when the requirement is to combine rows from multiple query results while eliminating duplicates. If duplicate rows needed to be preserved, UNION ALL would generally be used instead.

Question 46

A data engineer needs to combine columns from two datasets based on a common customer ID. Which SQL operation should be used?

1. GROUP BY
2. JOIN
3. UNION
4. DISTINCT

Correct Answer: 2

Explanation:

A SQL JOIN is used to combine data from multiple tables or datasets based on a related column or condition. In this scenario, customer ID can be used as the join key to match records belonging to the same customer. Different join types, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN, can be selected depending on whether unmatched records should be retained. GROUP BY is used for aggregation, UNION combines rows from compatible query results, and DISTINCT removes duplicate rows. Therefore, JOIN is the correct SQL operation for combining columns from two datasets based on a common customer ID.

Question 47

A company wants to ensure that only authorized users can query a sensitive Amazon Redshift database. Which AWS service should primarily be used to manage user identities and permissions?

1. Amazon Athena
2. AWS Glue
3. AWS IAM
4. Amazon CloudFront

Correct Answer: 3

Explanation:

AWS Identity and Access Management (IAM) provides centralized control over identities, authentication, and authorization for AWS resources. IAM users, groups, roles, and policies can be used to control who can access AWS services and what actions they are allowed to perform. For Redshift environments, IAM can also be integrated with appropriate authentication mechanisms and permissions. Amazon Athena is a query service, AWS Glue provides data integration and metadata capabilities, and CloudFront is a content delivery service. Therefore, AWS IAM is the appropriate AWS service for managing identities and permissions that control access to sensitive resources and services.

Question 48

A data engineer needs to identify duplicate records based on a unique transaction ID before loading data into an analytical system. Which approach is most appropriate?

1. Deduplication using the transaction ID
2. Data compression
3. Data encryption
4. Data partitioning only

Correct Answer: 1

Explanation:

Deduplication removes repeated records from a dataset and helps maintain data quality. Using a unique transaction ID as the deduplication key allows the data engineer to identify records that represent the same transaction. The processing logic can retain one valid record while removing or ignoring additional copies. Compression reduces storage size but does not identify duplicates. Encryption protects data from unauthorized access, while partitioning organizes data into logical groups but does not inherently remove duplicate records. Therefore, using the transaction ID as a deduplication key is the most appropriate approach for ensuring that repeated transaction records do not enter the analytical system.

Question 49

A company wants to process large datasets using Apache Spark without manually installing and maintaining the Spark software. Which AWS service should be used?

1. Amazon S3
2. Amazon EMR
3. Amazon SQS
4. Amazon Route 53

Correct Answer: 2

Explanation:

Amazon EMR is a managed AWS service that supports big data frameworks such as Apache Spark, Hadoop, Hive, and other technologies. It reduces the operational effort required to install, configure, and maintain distributed processing environments. Data engineers can use EMR to process large datasets stored in Amazon S3 and other data sources. Amazon S3 provides object storage, SQS provides message queuing, and Route 53 provides DNS services. Therefore, Amazon EMR is the most appropriate service when an organization needs to run Apache Spark workloads while relying on AWS to manage much of the underlying cluster infrastructure and configuration.

Question 50

A data engineer needs to store sensitive data in Amazon S3 using server-side encryption with a key managed through AWS Key Management Service. Which encryption option should be used?

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

Correct Answer: 3

Explanation:

SSE-KMS, or server-side encryption with AWS KMS keys, allows Amazon S3 to encrypt objects using keys managed through AWS Key Management Service. It provides additional control over encryption keys and can support auditing and access management through AWS services. SSE-S3 uses keys managed by Amazon S3, while client-side encryption requires the application or client to perform encryption before uploading the object. TLS protects data during transmission rather than providing server-side encryption for stored objects. Therefore, SSE-KMS is the correct choice when the company specifically requires Amazon S3 server-side encryption using AWS KMS-managed keys.

Question 51

A company needs to create a reusable ETL job that reads data from Amazon S3, transforms it, and writes the results back to another S3 location. Which AWS service is best suited for this task?

1. AWS Glue
2. Amazon Route 53
3. Amazon CloudFront
4. AWS IAM

Correct Answer: 1

Explanation:

AWS Glue provides managed and serverless ETL capabilities that allow data engineers to create jobs for reading, transforming, and writing datasets. A Glue ETL job can read data from Amazon S3, apply transformations using supported processing frameworks, and write the processed results to another S3 location or other supported destinations. Route 53 is a DNS service, CloudFront provides content delivery, and IAM manages identities and permissions. Therefore, AWS Glue is the most appropriate service for creating reusable ETL jobs that process data from Amazon S3 and produce transformed datasets for analytics or downstream applications.

Question 52

A data engineer wants to query only records from the year 2026 in a dataset partitioned by year. Which technique can reduce the amount of data scanned by Amazon Athena?

1. Data replication
2. Partition pruning
3. Data encryption
4. Object versioning

Correct Answer: 2

Explanation:

Partition pruning allows query engines such as Amazon Athena to avoid scanning partitions that do not match the query conditions. If a dataset is partitioned by year and a query filters for year 2026, Athena can focus on the relevant partition instead of scanning data from all available years. This can reduce the amount of data scanned, improve query performance, and lower query costs. Data replication creates additional copies, encryption protects data, and object versioning maintains multiple versions of objects. Therefore, partition pruning is the appropriate technique for efficiently querying a partitioned dataset when the query includes a partition key filter.

Question 53

A company wants to schedule a recurring data processing job that runs every day at midnight. Which AWS service can be used to define a scheduled event that triggers the workflow?

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

Correct Answer: 2

Explanation:

Amazon EventBridge can create scheduled rules that trigger AWS services and workflows at specified times or intervals. A recurring schedule can be configured using a cron or rate expression, allowing a data processing workflow to run automatically every day at a specified time. EventBridge can invoke services such as AWS Lambda, Step Functions, and other supported targets. Amazon EBS provides block storage, RDS is a relational database service, and ElastiCache provides in-memory caching. Therefore, Amazon EventBridge is an appropriate choice for creating a scheduled event that automatically starts a recurring data processing workflow at midnight.

Question 54

A data engineer needs to ensure that a data pipeline continues processing even when one temporary service call fails. Which design principle should be implemented?

1. Hard-coded credentials
2. Retry and error-handling mechanisms
3. Removing all monitoring
4. Disabling logging

Correct Answer: 2

Explanation:

Retry and error-handling mechanisms improve the reliability and resilience of data pipelines. Temporary failures can occur because of network issues, service throttling, or other transient conditions. A well-designed pipeline can retry failed operations using controlled intervals and limits, while error-handling logic can route permanently failed records or tasks for further investigation. Hard-coded credentials create security risks, and disabling monitoring or logging makes failures more difficult to detect and troubleshoot. Therefore, retry and error-handling mechanisms are essential when designing reliable data pipelines. They help prevent temporary problems from unnecessarily causing an entire workflow to fail.

Question 55

A company wants to query data stored in Amazon S3 using SQL while using metadata stored in the AWS Glue Data Catalog. Which service can directly use this catalog metadata?

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

Correct Answer: 1

Explanation:

Amazon Athena integrates with the AWS Glue Data Catalog to obtain metadata about datasets stored in Amazon S3. The catalog contains information such as table names, schemas, columns, partitions, and data locations. Athena uses this metadata to interpret the structure of S3 data and execute SQL queries against it. CloudFront is a content delivery service, SQS provides message queuing, and Route 53 provides DNS services. Therefore, Amazon Athena is the correct choice when a data engineer needs to query S3 data using SQL while relying on metadata maintained in the AWS Glue Data Catalog.

Question 56

A data engineer needs to create a low-latency analytical dashboard that refreshes frequently from a data warehouse. Which AWS service provides business intelligence dashboards and visualizations?

1. Amazon S3
2. AWS Glue
3. Amazon QuickSight
4. Amazon Kinesis Data Streams

Correct Answer: 3

Explanation:

Amazon QuickSight is an AWS business intelligence service that allows users to create interactive dashboards, visualizations, reports, and analytical insights. It can connect to supported AWS data sources and provide business users with an accessible way to analyze organizational data. Amazon S3 provides object storage, AWS Glue provides data integration and ETL capabilities, and Kinesis Data Streams is designed for real-time streaming data ingestion. Therefore, Amazon QuickSight is the most appropriate service when the primary requirement is to present analytical information through interactive dashboards and visualizations for business users.

Question 57

A company wants to prevent unauthorized access to an Amazon S3 bucket by allowing access only from a specific IAM role. Which security mechanism can enforce this requirement?

1. S3 bucket policy
2. Amazon CloudFront
3. Amazon Athena workgroup
4. Amazon SQS queue policy

Correct Answer: 1

Explanation:

An Amazon S3 bucket policy can define detailed access permissions for a bucket and its objects. The policy can restrict access based on identities such as IAM roles, conditions, source networks, and other supported attributes. This allows an organization to specify that only a particular IAM role or authorized principal can access sensitive data. CloudFront is primarily used for content delivery, Athena workgroups manage query-related settings, and SQS queue policies control access to queues rather than S3 buckets. Therefore, an S3 bucket policy is the appropriate mechanism for enforcing access restrictions on an S3 bucket.

Question 58

A data engineer needs to store an extremely large amount of raw, structured, semi-structured, and unstructured data for future analytics. Which architecture is most appropriate?

1. A data lake using Amazon S3
2. A single Amazon EC2 instance
3. An Amazon SQS queue
4. An Amazon CloudFront distribution

Correct Answer: 1

Explanation:

A data lake built on Amazon S3 is well suited for storing large volumes of raw, structured, semi-structured, and unstructured data. S3 provides scalable object storage and integrates with many AWS analytics and processing services. Data can be stored in its original form and transformed when needed for downstream analysis. An EC2 instance is compute infrastructure rather than a scalable data lake architecture, SQS is designed for message queuing, and CloudFront is designed for content delivery. Therefore, an Amazon S3-based data lake is the most appropriate architecture for retaining diverse datasets at large scale and making them available for future analytics and processing.

Question 59

A company wants to track who accessed or modified resources in its AWS account for compliance and auditing. Which AWS service should be used?

1. Amazon QuickSight
2. AWS CloudTrail
3. Amazon Redshift
4. AWS Glue

Correct Answer: 2

Explanation:

AWS CloudTrail records API activity and actions performed within an AWS account. It can provide information about the identity that performed an action, the requested service, the time of the activity, and other event details. This makes CloudTrail valuable for compliance, security investigations, auditing, and operational troubleshooting. QuickSight is a business intelligence service, Redshift is a data warehouse, and Glue provides data integration capabilities. Therefore, AWS CloudTrail is the appropriate service when an organization needs to track resource access and changes for auditing and compliance purposes.

Question 60

A data engineer needs to reduce storage and query costs for a large analytical dataset in Amazon S3 while preserving the information required for analysis. Which approach is most appropriate?

1. Store everything as uncompressed CSV
2. Use columnar compression formats such as Parquet
3. Duplicate the dataset several times
4. Convert the dataset to uncompressed JSON

Correct Answer: 2

Explanation:

Using columnar formats such as Apache Parquet with compression can significantly reduce both storage requirements and analytical query costs. Parquet organizes data by columns, allowing query engines such as Athena to read only the columns required by a query. Compression further reduces the physical amount of data that must be stored and scanned. Uncompressed CSV and JSON generally require more storage and can result in larger query scan volumes. Duplicating datasets increases storage consumption without improving efficiency. Therefore, storing suitable analytical datasets in compressed Parquet format is an effective approach for reducing storage and query costs while preserving the information needed for analysis.