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

 

Question 121

A data engineer needs to transform large amounts of data stored in Amazon S3 using Apache Spark without managing the underlying servers. Which AWS service is most appropriate?

1. Amazon RDS
2. AWS Glue
3. Amazon Route 53
4. Amazon SQS

Correct Answer: 2

Explanation:

AWS Glue provides a serverless data integration and ETL platform that supports Apache Spark for distributed data processing. A data engineer can create Glue jobs to read data from Amazon S3, transform it using Spark, and write the processed data back to S3 or another supported destination. AWS manages the underlying infrastructure, including provisioning and scaling compute resources. Amazon RDS is designed for relational databases, Route 53 provides DNS services, and SQS provides message queuing. Therefore, AWS Glue is the most appropriate option when large datasets need Spark-based transformations without requiring the data engineer to manage servers.

Question 122

A company wants to prevent unauthorized IAM users from accessing objects in an S3 bucket. Which mechanism should primarily be used to control access?

1. IAM and S3 bucket policies
2. S3 Glacier
3. Amazon CloudWatch Logs
4. Amazon Athena

Correct Answer: 1

Explanation:

IAM policies and Amazon S3 bucket policies are fundamental mechanisms for controlling access to S3 resources. IAM policies can grant or restrict permissions for users and roles, while bucket policies can define access rules directly at the bucket level. Together, they can enforce least-privilege access and prevent unauthorized identities from accessing objects. S3 Glacier is a storage class, CloudWatch Logs provides logging capabilities, and Athena is a query service. Therefore, IAM and S3 bucket policies are the appropriate mechanisms for controlling access to S3 objects and ensuring that only authorized identities can perform permitted actions.

Question 123

A data engineer wants to improve the performance of Athena queries by organizing S3 data according to frequently filtered columns such as year and month. What should the engineer implement?

1. Data replication
2. S3 versioning
3. S3 partitioning
4. Object Lock

Correct Answer: 3

Explanation:

Partitioning organizes datasets into separate logical sections based on one or more columns, such as year, month, region, or department. When Athena queries include filters on partition columns, Athena can avoid scanning partitions that are not relevant to the query. This reduces the amount of data scanned and can improve query performance while lowering query costs. Data replication creates additional copies, S3 Versioning preserves object versions, and Object Lock protects objects from modification or deletion. Therefore, partitioning S3 datasets based on commonly filtered columns is an effective strategy for optimizing Athena queries.

Question 124

A company receives duplicate events in a streaming data pipeline. Which technique should a data engineer use to prevent duplicate records from reaching the final dataset?

1. Deduplication using unique event identifiers
2. Increasing S3 storage capacity
3. Disabling retries
4. Compressing all incoming records

Correct Answer: 1

Explanation:

Duplicate events can occur in distributed streaming systems because of retries, network issues, or at-least-once delivery behavior. A common solution is to assign or use a unique event identifier and maintain deduplication logic in the processing layer. When an event with an identifier that has already been processed arrives again, the pipeline can ignore it or update the existing record instead of creating another copy. Increasing storage capacity does not solve duplication, disabling retries can reduce reliability, and compression only reduces data size. Therefore, unique event identifiers combined with deduplication logic provide an effective way to maintain data quality.

Question 125

A data engineer wants to capture API activity, including who made an AWS API request and when the request occurred. Which AWS service should be used?

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

Correct Answer: 2

Explanation:

AWS CloudTrail records AWS API activity and provides information about actions performed against AWS resources. CloudTrail events can include details such as the identity that made the request, the API operation, the resource involved, the time of the request, and other request metadata. This information is useful for security monitoring, auditing, compliance investigations, and troubleshooting. Amazon Macie focuses on sensitive data discovery in S3, Athena provides SQL-based analysis, and Glue provides data integration and ETL capabilities. Therefore, AWS CloudTrail is the appropriate service for capturing and reviewing information about AWS API requests and user activity.

Question 126

A data engineer wants to store a dataset in a format that supports column pruning and efficient compression for analytical workloads. Which format is most appropriate?

1. CSV
2. JSON
3. XML
4. Apache Parquet

Correct Answer: 4

Explanation:

Apache Parquet is a columnar storage format designed for efficient analytical workloads. Because data is organized by columns, query engines can read only the columns required by a query instead of scanning the entire dataset. Parquet also supports efficient compression and encoding techniques, which can reduce storage requirements and improve query performance. CSV, JSON, and XML are generally row-oriented or less optimized for analytical scanning and typically require more data to be processed for similar queries. Therefore, Apache Parquet is the most appropriate format when a data engineer needs efficient compression and column pruning for analytical workloads.

Question 127

A company wants to automatically retry a failed step in a data processing workflow before marking the entire workflow as failed. Which AWS service provides built-in workflow retry and error handling capabilities?

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

Correct Answer: 1

Explanation:

AWS Step Functions provides workflow orchestration capabilities, including built-in retry and error-handling mechanisms. A state can be configured with retry behavior so that transient failures are retried according to specified settings such as retry count and intervals. This is useful for data pipelines where temporary service errors should not immediately cause the entire workflow to fail. Route 53 provides DNS functionality, S3 Glacier provides archival storage, and KMS manages encryption keys. Therefore, Step Functions is the appropriate service when a data engineer needs controlled retries and error handling within a multi-step data processing workflow.

Question 128

A data engineer needs to migrate an existing relational database to AWS while minimizing downtime and continuously replicating changes during the migration. Which service should be used?

1. Amazon Athena
2. AWS Database Migration Service (AWS DMS)
3. Amazon CloudWatch
4. AWS Lake Formation

Correct Answer: 2

Explanation:

AWS Database Migration Service (AWS DMS) can migrate databases to AWS while supporting ongoing replication of changes from the source database. During a migration, DMS can perform an initial full load and then use change data capture (CDC) to replicate subsequent changes. This approach helps minimize downtime because the source database can continue operating while changes are replicated to the target. Athena is a query service, CloudWatch provides monitoring, and Lake Formation manages data lake permissions and governance. Therefore, AWS DMS is the appropriate choice for database migrations that require continuous replication and reduced downtime.

Question 129

A company wants to provide fine-grained permissions to specific databases, tables, and columns in a data lake. Which AWS service should be used?

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

Correct Answer: 1

Explanation:

AWS Lake Formation provides centralized data lake governance and fine-grained access control for data stored in services such as Amazon S3. It can manage permissions at granular levels, including databases, tables, columns, and other data resources. This helps organizations enforce consistent security policies across analytical services that access the data lake. CloudFront is a content delivery service, SQS is a message queuing service, and Route 53 is a DNS service. Therefore, Lake Formation is the most appropriate choice when an organization needs centralized governance and fine-grained permissions across datasets in a data lake.

Question 130

A data engineer needs to remove duplicate rows from a SQL query result based on all selected columns. Which SQL keyword should be used?

1. ORDER BY
2. GROUP BY
3. DISTINCT
4. LIMIT

Correct Answer: 3

Explanation:

The SQL DISTINCT keyword removes duplicate rows from the result set based on the selected columns. For example, SELECT DISTINCT customer_id FROM customers returns each customer ID only once even if multiple records contain the same ID. ORDER BY sorts query results, GROUP BY groups rows for aggregation or grouped processing, and LIMIT restricts the number of rows returned. Therefore, DISTINCT is the correct SQL keyword when the requirement is to eliminate duplicate rows from query results. It is particularly useful during data analysis when a dataset contains repeated values and only unique combinations of selected columns are required.

Question 131

A company stores sensitive data in S3 and wants to automatically detect whether the data contains personally identifiable information (PII). Which service is most appropriate?

1. AWS Glue
2. Amazon Macie
3. Amazon Redshift
4. Amazon EventBridge

Correct Answer: 2

Explanation:

Amazon Macie is designed to discover and protect sensitive data stored in Amazon S3. It can use automated data discovery and sensitive-data detection techniques to identify information such as personally identifiable information (PII) within supported S3 objects. This helps organizations understand where sensitive information exists and supports security, privacy, and compliance requirements. AWS Glue focuses on data integration and ETL, Redshift is a data warehouse, and EventBridge provides event routing and scheduling. Therefore, Amazon Macie is the most appropriate service for automatically discovering potentially sensitive information and PII in Amazon S3 datasets.

Question 132

A data engineer wants to ensure that an S3 bucket automatically deletes temporary files after 30 days. Which S3 feature should be configured?

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

Correct Answer: 1

Explanation:

Amazon S3 Lifecycle rules can automatically transition or expire objects based on age and other conditions. A rule can be configured to delete temporary objects after 30 days, helping reduce unnecessary storage costs and automate data retention management. Object Lock is designed to prevent objects from being deleted or overwritten during a retention period, Access Points provide controlled access to shared datasets, and Transfer Acceleration improves data transfer performance. Therefore, S3 Lifecycle rules are the appropriate feature for automatically deleting temporary files after a defined period such as 30 days.

Question 133

A data engineer wants to process records from Amazon Kinesis Data Streams as they arrive and execute custom code for each batch of records. Which service can be integrated directly with Kinesis Data Streams for this purpose?

1. AWS Lambda
2. Amazon Route 53
3. Amazon EBS
4. Amazon CloudFront

Correct Answer: 1

Explanation:

AWS Lambda can be configured as an event source for Amazon Kinesis Data Streams. Lambda automatically polls the stream and invokes the function with batches of records, allowing custom processing logic to execute without managing servers. This architecture is useful for real-time transformations, validation, filtering, enrichment, and routing of streaming data. Route 53 provides DNS services, EBS provides block storage, and CloudFront is a content delivery network. Therefore, AWS Lambda is the appropriate service for executing custom processing code on records arriving in Kinesis Data Streams.

Question 134

A company wants to monitor a critical data pipeline and receive an alert when a metric exceeds a predefined threshold. Which CloudWatch feature should be used?

1. CloudWatch Logs Insights
2. CloudWatch Dashboard
3. CloudWatch Alarm
4. CloudWatch Agent

Correct Answer: 3

Explanation:

Amazon CloudWatch Alarms monitor metrics and automatically perform actions when specified threshold conditions are met. For example, a data engineer can configure an alarm to trigger when failed ETL jobs exceed a certain number or when processing latency becomes too high. The alarm can notify an Amazon SNS topic or initiate another configured action. Dashboards provide visualization, Logs Insights is used to query logs, and the CloudWatch Agent collects system-level metrics and logs. Therefore, a CloudWatch Alarm is the appropriate feature when a data engineer needs an automatic alert based on a metric crossing a predefined threshold.

Question 135

A data engineer needs to securely store database credentials used by an ETL job without placing passwords directly in the source code. Which AWS service should be used?

1. Amazon S3
2. AWS Secrets Manager
3. Amazon Athena
4. Amazon CloudFront

Correct Answer: 2

Explanation:

AWS Secrets Manager provides secure storage and management of sensitive information such as database credentials, API keys, and passwords. Applications and ETL jobs can retrieve secrets programmatically at runtime rather than storing credentials directly in source code or configuration files. Secrets Manager also supports features such as automatic secret rotation for supported services. Amazon S3 is object storage, Athena provides serverless SQL queries, and CloudFront distributes content. Therefore, AWS Secrets Manager is the appropriate solution for securely storing database credentials used by an ETL process while avoiding hard-coded passwords in application code.

Question 136

A company wants to run analytical SQL queries against large datasets in Amazon S3 without first loading the data into a database. Which service should be used?

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

Correct Answer: 1

Explanation:

Amazon Athena allows users to run SQL queries directly against data stored in Amazon S3 without requiring the data to be loaded into a traditional database. Athena is serverless, so there is no infrastructure to provision or manage for query execution. It can use metadata from the AWS Glue Data Catalog to understand table schemas and can query formats such as Parquet, ORC, JSON, and CSV. RDS is a managed relational database, DynamoDB is a NoSQL database, and ElastiCache is an in-memory caching service. Therefore, Athena is the best choice for querying large S3 datasets directly using SQL.

Question 137

A data engineer wants to ensure that only encrypted connections are allowed when clients access an S3 bucket. Which security control should be used?

1. Require Secure Transport in the S3 bucket policy
2. Enable S3 Lifecycle rules
3. Enable S3 Intelligent-Tiering
4. Create an Athena workgroup

Correct Answer: 1

Explanation:

Amazon S3 bucket policies can enforce the use of secure transport by denying requests where the aws:SecureTransport condition is false. This prevents clients from accessing the bucket through unencrypted HTTP connections and requires HTTPS for data transmission. This is an important control for protecting data in transit. Lifecycle rules manage object retention, Intelligent-Tiering manages storage costs based on access patterns, and Athena workgroups organize query workloads. Therefore, requiring secure transport through an S3 bucket policy is the appropriate security control when an organization wants to ensure that clients use encrypted connections when accessing S3.

Question 138

A company wants to automatically scale compute resources for a large distributed data processing workload using Apache Spark. Which AWS service is appropriate?

1. Amazon EMR
2. Amazon Route 53
3. Amazon SQS
4. AWS Secrets Manager

Correct Answer: 1

Explanation:

Amazon EMR is a managed cluster platform designed to run big data frameworks such as Apache Spark, Hadoop, and related technologies. EMR can provision and scale compute resources for large distributed processing workloads, making it suitable for processing very large datasets. Data engineers can use Spark on EMR for transformations, aggregations, machine learning, and other distributed workloads. Route 53 provides DNS, SQS provides message queuing, and Secrets Manager manages sensitive credentials. Therefore, Amazon EMR is the appropriate AWS service when a company needs scalable infrastructure for Apache Spark-based distributed data processing.

Question 139

A data engineer wants to create a scheduled data pipeline that runs every day at midnight. Which AWS service can provide the schedule that starts the pipeline?

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

Correct Answer: 2

Explanation:

Amazon EventBridge can create scheduled rules that invoke supported AWS targets at specified times or intervals. A data engineer can configure an EventBridge schedule to run every day at midnight and use the event to start a Lambda function, Step Functions workflow, Glue job, or another supported target. Macie is used for sensitive-data discovery, EBS provides block storage, and ElastiCache provides in-memory caching. Therefore, EventBridge is the appropriate service for scheduling a recurring data pipeline. It allows organizations to automate routine processing without requiring someone to manually start the pipeline every day.

Question 140

A data engineer wants to improve the reliability of a pipeline by sending failed records to a separate location for later investigation instead of stopping the entire process. What approach should be used?

1. Delete failed records permanently
2. Use a dead-letter or quarantine location
3. Disable all validation rules
4. Ignore processing errors

Correct Answer: 2

Explanation:

A dead-letter queue or quarantine location allows failed records to be isolated from successfully processed data while allowing the main pipeline to continue processing valid records. Data engineers can later investigate the failed records, identify the cause of the errors, correct the underlying issue, and potentially reprocess the records. Permanently deleting failed records can cause data loss, while disabling validation or ignoring errors reduces data quality and reliability. Therefore, using a dead-letter or quarantine mechanism is a strong approach for building resilient data pipelines that can handle malformed or problematic records without unnecessarily stopping the entire workflow.