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

 

Question 361:

A data engineer needs to ingest messages from a Kafka-compatible streaming platform into AWS and process them using managed services. Which AWS service is designed to provide a managed Apache Kafka environment?

1. Amazon Kinesis Data Firehose
2. Amazon MSK
3. Amazon Athena
4. AWS Glue Data Catalog

Correct Answer: 2

Explanation:

Amazon Managed Streaming for Apache Kafka (Amazon MSK) provides a fully managed Apache Kafka environment in AWS. It allows organizations to build applications that use Kafka for real-time event streaming without having to manage the underlying Kafka infrastructure themselves. MSK is useful for applications that require Kafka-compatible APIs, durable event streams, and multiple independent consumers. Kinesis Data Firehose focuses on delivering streaming data to destinations, Athena provides SQL-based analytics, and Glue Data Catalog stores metadata. Therefore, Amazon MSK is the appropriate service when a company needs a managed Apache Kafka environment for streaming workloads.

Question 362:

A data engineer needs to monitor the number of records processed by an AWS Glue job and create an alarm when processing unexpectedly decreases. Which AWS service should be used?

1. Amazon CloudWatch
2. Amazon Macie
3. AWS DMS
4. Amazon S3 Glacier

Correct Answer: 1

Explanation:

Amazon CloudWatch provides monitoring capabilities for AWS services and applications. A data engineer can use CloudWatch metrics, logs, and alarms to monitor Glue job behavior and detect unexpected changes in processing activity. For example, an alarm can be configured to identify when a relevant metric falls below an expected threshold, allowing the operations team to investigate the pipeline. Macie is used for sensitive-data discovery, DMS handles database migration and replication, and S3 Glacier provides archival storage. Therefore, CloudWatch is the appropriate service for monitoring Glue processing metrics and generating operational alarms.

Question 363:

A company wants to provide different teams with access to only the tables they need in a shared AWS data lake. Which service is designed to manage fine-grained data lake permissions?

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

Correct Answer: 2

Explanation:

AWS Lake Formation provides centralized governance and fine-grained access control for data lakes. It allows organizations to manage permissions on databases, tables, columns, and other data lake resources. This makes it possible to provide different teams with access to only the datasets or attributes required for their responsibilities. CloudFront is a content delivery service, ElastiCache provides in-memory caching, and Route 53 provides DNS services. Lake Formation can work with services such as AWS Glue and Amazon Athena to help establish governed data access. Therefore, Lake Formation is the appropriate service for fine-grained data lake permissions.

Question 364:

A data engineer wants to automatically trigger a Lambda function whenever a new object is uploaded to a specific Amazon S3 bucket. Which solution is appropriate?

1. S3 event notification
2. S3 Lifecycle rule
3. S3 Inventory
4. S3 Storage Class Analysis

Correct Answer: 1

Explanation:

Amazon S3 event notifications allow S3 to automatically send notifications when specific events occur, such as object creation. These notifications can be integrated with services such as AWS Lambda, Amazon SQS, and Amazon SNS. A Lambda function can therefore be triggered automatically whenever a new object is uploaded to a configured bucket and prefix. Lifecycle rules manage object transitions and expiration, S3 Inventory provides object reports, and Storage Class Analysis helps analyze access patterns. Therefore, an S3 event notification is the appropriate mechanism for triggering a Lambda function in response to object creation.

Question 365:

A company needs to store relational data for an operational application and wants AWS to manage database backups, patching, and infrastructure. Which service is most appropriate?

1. Amazon RDS
2. Amazon S3
3. Amazon Kinesis Data Streams
4. Amazon Athena

Correct Answer: 1

Explanation:

Amazon Relational Database Service (Amazon RDS) provides managed relational database engines and handles many infrastructure-management tasks, including backups, patching, and provisioning. It is suitable for operational applications that require relational database capabilities and SQL-based transactions. Amazon S3 is object storage, Kinesis Data Streams handles real-time streaming data, and Athena is a serverless analytical query service for data sources such as S3. Therefore, Amazon RDS is the appropriate choice when an application requires a managed relational database with AWS handling much of the underlying infrastructure and maintenance.

Question 366:

A data engineer wants to process records from an Amazon Kinesis Data Stream using AWS Lambda without continuously managing servers. Which integration should be configured?

1. Lambda event source mapping
2. S3 Lifecycle
3. Glue Data Catalog
4. Redshift Spectrum

Correct Answer: 1

Explanation:

AWS Lambda can consume records from Amazon Kinesis Data Streams using an event source mapping. Lambda polls the stream and invokes the function with batches of records, allowing the application to process streaming data without managing dedicated consumer servers. The Lambda function can transform, validate, enrich, or route the records to downstream services. S3 Lifecycle manages object storage policies, Glue Data Catalog stores metadata, and Redshift Spectrum queries external data. Therefore, Lambda event source mapping is the appropriate integration for processing Kinesis records through serverless compute.

Question 367:

A company wants to ensure that an AWS Glue ETL job can access an Amazon S3 bucket but cannot access unrelated AWS resources. Which security principle should be applied?

1. Least privilege
2. Public access
3. Full administrator access
4. Anonymous access

Correct Answer: 1

Explanation:

The principle of least privilege requires granting an identity only the permissions necessary to perform its intended tasks. For an AWS Glue ETL job, the associated IAM role should contain permissions for the required S3 buckets, objects, catalog resources, and other services while avoiding unnecessary access. Granting administrator access creates excessive privileges and increases the potential impact of compromised credentials or configuration errors. Public or anonymous access is inappropriate for protected data. Therefore, applying least privilege to the Glue job’s IAM role provides a more secure and controlled data-processing environment.

Question 368:

A data engineer needs to store frequently accessed analytical data in a format that supports efficient compression and column-level reads. Which format should be selected?

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

Correct Answer: 3

Explanation:

Apache Parquet is a columnar file format designed for efficient analytical workloads. Because data is stored by column, query engines can read only the columns required by a query rather than scanning every field. Parquet also supports efficient compression and encoding, which can reduce storage requirements and the amount of data that needs to be scanned. CSV, JSON Lines, and XML are generally less optimized for large-scale analytical workloads. Therefore, Parquet is the preferred format when a data engineer wants efficient compression and column-level reads for analytical datasets.

Question 369:

A company wants to automatically discover sensitive information such as financial data stored in Amazon S3. Which service should be used?

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

Correct Answer: 2

Explanation:

Amazon Macie is designed to discover and protect sensitive data stored in Amazon S3. It can identify certain types of sensitive information, including personally identifiable information and financial-related data, using managed data identifiers and machine-learning techniques. This helps organizations understand where sensitive information exists and assess potential security risks. Athena is an analytical query service, Redshift is a data warehouse, and EventBridge is used for event routing and automation. Therefore, Amazon Macie is the most appropriate AWS service when the primary requirement is discovering sensitive information in S3 datasets.

Question 370:

A data pipeline needs to execute a Lambda function, wait for its result, and then run an AWS Glue job only if the Lambda function succeeds. Which service can orchestrate this workflow?

1. Amazon S3
2. AWS Step Functions
3. Amazon Macie
4. Amazon CloudFront

Correct Answer: 2

Explanation:

AWS Step Functions provides workflow orchestration using state machines. A workflow can invoke a Lambda function, evaluate its result, and then transition to another state such as an AWS Glue job when the preceding task succeeds. Step Functions also supports branching, retries, error handling, waiting, and parallel execution. This makes it suitable for coordinating multi-step data pipelines without requiring custom orchestration code. S3 provides storage, Macie discovers sensitive data, and CloudFront provides content delivery. Therefore, Step Functions is the appropriate service for coordinating Lambda and Glue operations in a controlled sequence.

Question 371:

A data engineer wants to reduce the number of S3 objects that Athena must open during a query because a dataset contains millions of tiny files. What should be done?

1. Split the files into even smaller objects
2. Compact the files into appropriately sized larger objects
3. Disable all compression
4. Convert the files to XML

Correct Answer: 2

Explanation:

A very large number of small S3 objects can introduce additional overhead during analytical queries because the query engine must process many individual files. Compacting small files into appropriately sized larger objects can reduce this overhead and improve query performance. The data should preferably remain in an analytical format such as Parquet, with suitable compression enabled. Splitting files further increases the number of objects, while disabling compression can increase storage and scanning requirements. XML is generally not an efficient analytical format. Therefore, file compaction is an effective optimization for workloads suffering from excessive small-file overhead.

Question 372:

A company wants to make an Amazon S3 dataset available to multiple analytical services while maintaining table and column metadata centrally. Which solution is most appropriate?

1. AWS Glue Data Catalog
2. Amazon CloudFront
3. AWS Secrets Manager
4. Amazon ElastiCache

Correct Answer: 1

Explanation:

AWS Glue Data Catalog provides centralized metadata management for datasets. It can store table definitions, schemas, partition information, and S3 locations, allowing supported analytics services to discover and query the underlying data. This is especially useful in data lake architectures where multiple services and teams need consistent metadata. CloudFront is a content delivery network, Secrets Manager manages credentials and other secrets, and ElastiCache provides in-memory caching. Therefore, the Glue Data Catalog is the appropriate solution for centrally managing table and column metadata for an S3-based analytical dataset.

Question 373:

A data engineer needs to move data from an on-premises database into Amazon S3 on a recurring schedule and transform the data before storing it. Which AWS service is well suited for the transformation step?

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

Correct Answer: 1

Explanation:

AWS Glue is a managed data integration and ETL service that can extract data from supported sources, transform it, and load it into destinations such as Amazon S3. Glue jobs can perform transformations including filtering, mapping, joining, cleansing, and format conversion. It can work with metadata stored in the Glue Data Catalog and can be incorporated into scheduled or event-driven pipelines. Route 53 provides DNS services, CloudFront handles content delivery, and Macie focuses on sensitive-data discovery. Therefore, AWS Glue is the appropriate service for performing the transformation step in a recurring data pipeline.

Question 374:

A company wants to ensure that only encrypted HTTPS requests can access objects in an Amazon S3 bucket. Which bucket policy condition should be used?

1. aws:SecureTransport set to false with Deny
2. aws:SecureTransport set to false with Allow
3. s3:VersionId with Allow
4. s3:x-amz-storage-class with Allow

Correct Answer: 1

Explanation:

The aws:SecureTransport condition key can be used in an Amazon S3 bucket policy to identify whether a request uses secure transport. A common security pattern is to create a Deny statement when aws:SecureTransport is false. This prevents requests that are not transmitted using HTTPS from accessing the protected bucket. Using an Allow statement for insecure transport would not enforce the desired restriction. Version IDs and storage-class conditions serve different purposes. Therefore, explicitly denying requests where aws:SecureTransport is false is an effective way to require HTTPS access to S3 resources.

Question 375:

A data engineer wants to identify columns that contain NULL values before loading records into a curated data warehouse. Which data-quality rule is most appropriate?

1. Completeness check
2. Compression check
3. Partition pruning
4. Encryption check

Correct Answer: 1

Explanation:

A completeness check evaluates whether required fields contain values and identifies missing or NULL data. For example, if customer_id and transaction_date are mandatory fields, a completeness rule can verify that these columns are populated before records are accepted into a curated dataset. This helps prevent incomplete records from entering downstream analytical systems. Compression checks concern storage efficiency, partition pruning concerns query optimization, and encryption checks concern data security. Therefore, a completeness check is the appropriate data-quality rule when the objective is to identify missing values in required columns before loading the target system.

Question 376:

A company wants to run a query against a very large Amazon S3 dataset but only needs records from September 2026. The dataset is partitioned by year and month. Which query design is most efficient?

1. Filter using the year and month partition columns
2. Select every column without a filter
3. Scan every partition and filter afterward
4. Convert all data to XML first

Correct Answer: 1

Explanation:

When an S3 dataset is partitioned by year and month, Athena can use partition pruning when the query includes predicates on those partition columns. A query that specifies the appropriate year and month can avoid scanning unrelated partitions, significantly reducing the amount of data processed. This can improve performance and lower query costs. Selecting all columns without filtering may scan unnecessary data, while scanning every partition defeats the benefits of partitioning. Converting the dataset to XML would generally make analytical processing less efficient. Therefore, filtering directly on the partition columns is the most efficient approach.

Question 377:

A streaming application needs to ensure that a temporary network failure does not immediately cause the entire data-processing workflow to fail. Which design is most appropriate?

1. Implement retries with exponential backoff
2. Disable all retries
3. Delete the stream after the first error
4. Ignore all processing failures

Correct Answer: 1

Explanation:

Transient network failures can occur in distributed data pipelines and may resolve automatically after a short period. Implementing retries with exponential backoff allows the application to attempt the failed operation again while progressively increasing the delay between attempts. This reduces the risk of overwhelming the affected service and improves pipeline resilience. Disabling retries can cause unnecessary failures, deleting the stream is destructive, and ignoring failures can result in lost or corrupted processing. Therefore, controlled retries combined with exponential backoff are appropriate for handling temporary network problems in streaming workloads.

Question 378:

A data engineer needs to query a DynamoDB table for a specific partition key and efficiently retrieve related items ordered by a sort key. Which operation should be used?

1. Scan
2. Query
3. Export to S3
4. BatchWriteItem

Correct Answer: 2

Explanation:

The DynamoDB Query operation retrieves items based on a specific partition key and can also use conditions on the sort key. This makes Query efficient for applications that know the partition key and need to retrieve related items from that partition. A Scan examines every item in the table or index and is generally less efficient for targeted access. Exporting to S3 is useful for analytical or backup scenarios, while BatchWriteItem is used to write or delete multiple items. Therefore, Query is the correct DynamoDB operation for efficient partition-key-based retrieval.

Question 379:

A company needs to schedule a daily workflow that invokes an AWS service at 2:00 AM and wants the schedule to run automatically without maintaining a server. Which solution is appropriate?

1. EventBridge Scheduler
2. Amazon RDS
3. Amazon S3 Versioning
4. Amazon Macie

Correct Answer: 1

Explanation:

Amazon EventBridge Scheduler is a serverless scheduling capability that can invoke supported AWS targets according to a recurring schedule. A data engineer can configure a daily schedule for a specified time, such as 2:00 AM, without maintaining a dedicated scheduling server. This is useful for triggering ETL jobs, Lambda functions, workflows, and other automated operations. RDS provides relational databases, S3 Versioning maintains object versions, and Macie discovers sensitive information. Therefore, EventBridge Scheduler is the appropriate solution for running a daily serverless workflow at a specified time.

Question 380:

A data engineer wants to prevent sensitive AWS credentials from appearing in application source code and logs. Which practice is recommended?

1. Store credentials in plain-text configuration files
2. Store credentials in Amazon S3 without encryption
3. Use AWS Secrets Manager and retrieve secrets securely at runtime
4. Write credentials to CloudWatch Logs for centralized access

Correct Answer: 3

Explanation:

AWS Secrets Manager provides a secure location for storing sensitive credentials and allows applications to retrieve secrets at runtime. This prevents passwords and other credentials from being embedded directly in source code or configuration files. Applications should also avoid writing secret values to logs because logs may be accessible to multiple users or systems. Secrets Manager supports access control through IAM and can also support automatic rotation for supported secrets. Storing credentials in plain text or unprotected S3 objects creates significant security risks. Therefore, using Secrets Manager and retrieving credentials securely at runtime is the recommended approach.