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

 

Question 241:

A data engineer needs to query data stored in Amazon S3 using SQL without managing database infrastructure. Which AWS service should be used?

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

Correct Answer: 1

Explanation:

Amazon Athena is a serverless interactive query service that allows data engineers to run SQL queries directly against data stored in Amazon S3. It does not require provisioning or managing database servers. Athena can query structured and semi-structured formats such as CSV, JSON, ORC, and Parquet. It commonly uses the AWS Glue Data Catalog to obtain table definitions and schema information. Amazon RDS is a managed relational database, ElastiCache provides in-memory caching, and DynamoDB is a NoSQL database. Therefore, Athena is the best choice when the requirement is to query S3 data directly using SQL.

Question 242:

A company stores transaction data in Amazon S3 using Parquet files. Analysts frequently query only a few columns from these files. Which Parquet feature helps reduce the amount of data Amazon Athena needs to scan?

1. Row-level locking
2. Columnar storage
3. Database replication
4. Object versioning

Correct Answer: 2

Explanation:

Parquet is a columnar storage format, meaning data is organized and stored by columns rather than complete rows. When an Athena query requests only specific columns, Athena can read the required columns without processing unrelated columns. This reduces the amount of data scanned, which can improve query performance and lower Athena costs. Row-level locking is associated with transactional databases, replication is unrelated to Parquet’s storage structure, and S3 Versioning maintains previous object versions. Therefore, columnar storage is the important Parquet feature that allows Athena to efficiently retrieve only the required columns from large datasets.

Question 243:

A company receives CSV files from multiple sources. The files have inconsistent column names, different data types, and missing optional fields. Which AWS service is BEST suited for standardizing the data before loading it into an analytics data lake?

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

Correct Answer: 3

Explanation:

AWS Glue is a managed data integration and ETL service that can discover, transform, and prepare data from different sources. A Glue ETL job can rename columns, convert data types, handle missing values, remove invalid records, and apply business rules before writing standardized data to Amazon S3 or another destination. Glue also integrates with the AWS Glue Data Catalog for managing metadata. CloudWatch focuses on monitoring, Macie specializes in sensitive-data discovery, and Route 53 is a DNS service. Therefore, AWS Glue is the most appropriate choice for transforming and standardizing inconsistent CSV datasets before analytical processing.

Question 244:

A data engineer wants to automatically discover the schema of JSON files stored in Amazon S3 and create metadata that Amazon Athena can use. Which AWS service should be used?

1. AWS Glue Crawler
2. Amazon Macie
3. AWS CloudTrail
4. Amazon CloudWatch

Correct Answer: 1

Explanation:

AWS Glue Crawlers can scan data stored in Amazon S3, identify the file format, infer the schema, and create or update tables in the AWS Glue Data Catalog. Amazon Athena can then use these catalog tables to query the underlying S3 data. This approach is especially useful when new datasets are frequently added and manually creating table definitions would be inefficient. Macie is intended for sensitive-data discovery, CloudTrail records API activity, and CloudWatch provides monitoring and logging capabilities. Therefore, AWS Glue Crawler is the correct service for automatically discovering schemas and registering metadata for Athena.

Question 245:

A company wants to migrate an Amazon RDS database to another database while keeping the source database available. Changes made to the source should continue replicating to the target until the final cutover. Which service should be used?

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

Correct Answer: 2

Explanation:

AWS Database Migration Service (AWS DMS) is designed to migrate databases while minimizing downtime. It can perform an initial full load and then use Change Data Capture (CDC) to continuously replicate changes made to the source database. This allows the source database to remain operational while the target database is synchronized. Once the target is sufficiently caught up, the organization can perform the final cutover. Athena is an interactive query service, QuickSight provides business intelligence and visualization, and Macie discovers sensitive data. Therefore, AWS DMS is the best choice for this migration requirement.

Question 246:

A company stores sensitive customer information in Amazon S3 and requires customer-controlled encryption keys with auditing of key usage. Which solution should the data engineer implement?

1. S3 Transfer Acceleration
2. Amazon Macie
3. S3 Versioning
4. SSE-KMS using a customer managed AWS KMS key

Correct Answer: 4

Explanation:

SSE-KMS allows Amazon S3 objects to be encrypted using AWS Key Management Service keys. Customer managed KMS keys provide organizations with greater control over key policies, permissions, rotation, and usage. AWS CloudTrail can also record KMS API activity, providing an audit trail for key usage. S3 Transfer Acceleration is intended to improve data transfer speeds, Macie focuses on sensitive-data discovery, and Versioning maintains previous object versions. None of those features provide equivalent customer-controlled encryption-key management. Therefore, using SSE-KMS with a customer managed KMS key is the most appropriate solution for protecting sensitive S3 data.

Question 247:

An Amazon Athena query scans a large amount of data even though the dataset is organized by year and month. Which approach can reduce the amount of data scanned?

1. Remove the WHERE clause
2. Partition the dataset by year and month
3. Convert Parquet files to CSV
4. Store all data in one large file

Correct Answer: 2

Explanation:

Partitioning allows Athena to skip entire portions of a dataset when the query contains predicates on partition columns. If an S3 dataset is partitioned by year and month, a query requesting only a particular year and month can scan only the relevant partitions rather than the complete dataset. This reduces scanned data, lowers Athena query costs, and generally improves query performance. Removing the WHERE clause would increase scanning, CSV is generally less efficient than Parquet for analytics, and putting all data into one large file does not provide partition pruning. Therefore, partitioning by year and month is the best solution.

Question 248:

A data engineer needs to trigger an AWS Glue ETL job whenever a new object is uploaded to a specific Amazon S3 bucket. Which solution is MOST appropriate?

1. Amazon EventBridge rule
2. Amazon Macie classification job
3. Amazon Redshift scheduled query
4. CloudWatch dashboard

Correct Answer: 1

Explanation:

Amazon EventBridge can receive events generated when objects are created in Amazon S3 and use those events to trigger downstream AWS services. An EventBridge rule can be configured to match object-created events from a particular S3 bucket and invoke an AWS Glue ETL job. This creates an event-driven pipeline that starts processing when new data arrives. Macie is designed for sensitive-data discovery, Redshift scheduled queries are intended for analytical SQL workloads, and CloudWatch dashboards are used to visualize metrics. Therefore, an EventBridge rule is the most appropriate solution for automatically starting the Glue job after an S3 upload.

Question 249:

A data engineer needs to analyze application logs stored in Amazon CloudWatch Logs and find all events containing the term “ERROR”. Which feature should be used?

1. AWS Glue Data Catalog
2. Amazon Athena
3. AWS Lake Formation
4. CloudWatch Logs Insights

Correct Answer: 4

Explanation:

CloudWatch Logs Insights provides an interactive query capability for analyzing log data stored in CloudWatch Logs. Data engineers can write queries to filter events containing specific terms such as ERROR, calculate statistics, identify patterns, and investigate application issues. It is specifically designed for analyzing operational log data without requiring the logs to be moved to another system. Glue Data Catalog manages dataset metadata, Athena is primarily used to query data sources such as Amazon S3, and Lake Formation manages data lake governance. Therefore, CloudWatch Logs Insights is the best choice for searching and analyzing application logs.

Question 250:

A company wants to prevent accidental public access to all Amazon S3 buckets in an AWS account. Which feature should be enabled?

1. S3 Versioning
2. S3 Object Lock
3. S3 Block Public Access
4. S3 Lifecycle

Correct Answer: 3

Explanation:

Amazon S3 Block Public Access provides controls that help prevent buckets and objects from becoming publicly accessible. It can be configured at the account level, providing broad protection against accidental public exposure across the organization’s S3 environment. This is particularly important for data lakes containing confidential, proprietary, or customer information. S3 Versioning preserves previous object versions, Object Lock protects objects from deletion or modification during retention periods, and Lifecycle rules automate storage transitions and expiration. These features do not specifically prevent public access. Therefore, S3 Block Public Access is the correct security feature for preventing accidental public exposure.

Question 251:

A data engineer needs to orchestrate AWS Glue jobs, Lambda functions, and conditional processing steps. The workflow must support retries and error handling. Which AWS service should be used?

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

Correct Answer: 2

Explanation:

AWS Step Functions is a managed workflow orchestration service that can coordinate multiple AWS services and maintain the state of a workflow. A state machine can invoke Glue jobs, Lambda functions, and other services while supporting sequencing, conditional branching, retries, timeouts, and error handling. This makes Step Functions particularly useful for complex data engineering pipelines that contain multiple dependent processing stages. Athena is an interactive query service, Macie discovers sensitive data, and S3 provides object storage. Therefore, AWS Step Functions is the most appropriate solution for orchestrating the described multi-step data pipeline.

Question 252:

A company needs to continuously capture inserts, updates, and deletes from a relational database and send those changes to a downstream data pipeline. Which AWS DMS capability should be used?

1. AWS Glue Crawler
2. S3 Lifecycle
3. Amazon Athena
4. Change Data Capture (CDC)

Correct Answer: 4

Explanation:

AWS Database Migration Service provides Change Data Capture (CDC), which continuously captures changes made to a source database after the initial full load. CDC can capture inserts, updates, and deletes and replicate those changes to supported target systems. This allows downstream systems to stay synchronized with the source without repeatedly performing complete database loads. AWS Glue Crawlers are used for schema discovery, S3 Lifecycle manages object transitions and expiration, and Athena provides SQL-based analytics. Therefore, CDC is the appropriate AWS DMS capability for continuously capturing database changes for downstream processing.

Question 253:

An Amazon Redshift table is frequently queried using date-range filters on a timestamp column. Which design choice can improve query performance?

1. Use the timestamp column as an appropriate sort key
2. Store the timestamp as JSON
3. Remove all sort keys
4. Convert the timestamp to a large text field

Correct Answer: 1

Explanation:

Amazon Redshift uses sort keys to determine how table data is organized on disk. When queries frequently filter or perform range scans using a particular column, choosing that column as an appropriate sort key can improve query performance by reducing the amount of data that must be scanned. A timestamp column commonly used in date-range filters can therefore be a good candidate for a sort key, depending on the workload. Storing timestamps as JSON or large text fields is inefficient for analytical queries, while removing sort keys can reduce performance. Therefore, an appropriate timestamp sort key is the best option.

Question 254:

A company wants to identify personally identifiable information (PII) stored in Amazon S3. Which AWS service is specifically designed for this purpose?

1. Amazon EventBridge
2. Amazon Macie
3. AWS CloudTrail
4. AWS Step Functions

Correct Answer: 2

Explanation:

Amazon Macie is an AWS service designed to discover, classify, and help protect sensitive data stored in Amazon S3. It can identify sensitive information such as personally identifiable information and provide findings that help organizations understand potential security and compliance risks. Macie is particularly valuable when organizations have large amounts of S3 data that would be difficult to inspect manually. EventBridge routes events, CloudTrail records API activity, and Step Functions orchestrates workflows. These services do not specialize in sensitive-data discovery. Therefore, Amazon Macie is the correct choice when the requirement is to identify PII and other sensitive information in S3.

Question 255:

A financial organization must store records in Amazon S3 so they cannot be deleted or overwritten during a legally required retention period, even by an administrator. Which configuration should be used?

1. S3 Versioning
2. S3 Lifecycle
3. S3 Intelligent-Tiering
4. S3 Object Lock in Compliance mode

Correct Answer: 4

Explanation:

S3 Object Lock in Compliance mode provides strong protection against deletion or modification during a defined retention period. In Compliance mode, protected objects cannot be deleted or overwritten during the retention period, including by users with elevated administrative permissions. This makes it suitable for financial records, regulatory documents, and other data that must remain immutable for legal or compliance reasons. S3 Versioning preserves previous object versions but does not by itself enforce the same retention requirements. Lifecycle rules manage object transitions or expiration, while Intelligent-Tiering optimizes storage costs. Therefore, Object Lock in Compliance mode is the correct solution.

Question 256:

A company uses Amazon Kinesis Data Streams as the source for an AWS Lambda function. How does Lambda normally retrieve records from the stream?

1. Lambda polls the stream and processes records in batches
2. Lambda waits for S3 object notifications
3. Lambda automatically converts all records to Parquet
4. Lambda replaces Kinesis shards with S3 objects

Correct Answer: 1

Explanation:

When AWS Lambda is configured with an Amazon Kinesis Data Streams event source mapping, Lambda polls the stream and retrieves records in batches. Lambda then invokes the configured function to process those records. This provides a serverless method of consuming streaming data without requiring the data engineer to maintain separate polling infrastructure. Lambda does not depend on S3 notifications for Kinesis processing, does not automatically convert all incoming records to Parquet, and does not replace Kinesis shards with S3 objects. Therefore, polling the Kinesis stream and processing records in batches is the correct description of Lambda’s integration with Kinesis Data Streams.

Question 257:

A data pipeline receives CSV files from several sources. Some sources use different column names and data types for the same business fields. What should the data engineer do before loading the data into the analytics layer?

1. Ignore all schema differences
2. Delete files containing inconsistencies
3. Normalize the schema through an ETL transformation
4. Store every file without transformation

Correct Answer: 3

Explanation:

Schema normalization during an ETL process allows data engineers to standardize datasets received from different sources. The transformation process can rename columns, convert data types, add missing optional fields, remove invalid records, and apply common business rules. This produces a consistent structure for downstream analytics and reduces complexity for data consumers. AWS Glue is commonly used to perform these transformations. Ignoring schema differences can lead to query failures, deleting inconsistent files can result in unnecessary data loss, and storing everything unchanged shifts the transformation problem downstream. Therefore, normalizing the schema during ETL is the most reliable approach.

Question 258:

A streaming pipeline creates thousands of small Parquet files in Amazon S3. Amazon Athena queries have become slower because of the large number of objects. What should the data engineer implement?

1. Disable compression
2. Periodically compact small files
3. Enable S3 Transfer Acceleration
4. Disable partitioning

Correct Answer: 2

Explanation:

A large number of small files can create overhead for distributed query engines because each individual object must be discovered and processed. Periodic compaction combines multiple small Parquet files into fewer, appropriately sized files. This reduces file-management overhead and can improve Athena query performance. Keeping Parquet compression enabled is generally beneficial because it reduces storage size and the amount of data scanned. S3 Transfer Acceleration improves data transfer performance but does not solve the small-file problem. Disabling partitioning can increase the amount of data scanned. Therefore, periodically compacting small files is the most appropriate solution for this issue.

Question 259:

A data engineer needs to securely store database credentials used by an ETL application. The credentials should not be hard-coded and should support automatic rotation. Which AWS service should be used?

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

Correct Answer: 3

Explanation:

AWS Secrets Manager is designed to securely store and manage sensitive information such as database usernames, passwords, API keys, and other credentials. Applications can retrieve secrets at runtime rather than embedding them directly in source code or configuration files. Secrets Manager also supports fine-grained IAM permissions and automatic rotation for supported services and secret types. Amazon S3 is object storage, CloudWatch provides monitoring and logging, and Athena is an interactive query service. Therefore, Secrets Manager provides the appropriate combination of secure credential storage, controlled access, and credential rotation for an ETL application.

Question 260:

A company needs to run an AWS Glue ETL job automatically every day at 2:00 AM. No external event is required to start the job. Which solution is MOST appropriate?

1. Amazon Kinesis Data Streams
2. AWS DMS Change Data Capture
3. Amazon Macie
4. EventBridge Scheduler

Correct Answer: 4

Explanation:

Amazon EventBridge Scheduler allows organizations to invoke AWS services according to a defined schedule. A recurring schedule can be configured to start an AWS Glue ETL job every day at 2:00 AM without requiring an external event. This is useful for batch-processing pipelines that need to execute at predictable times. Kinesis Data Streams is designed for streaming ingestion, DMS Change Data Capture continuously captures database changes, and Macie focuses on sensitive-data discovery. EventBridge Scheduler provides a managed scheduling mechanism without requiring additional servers or custom cron infrastructure. Therefore, EventBridge Scheduler is the best solution for this daily Glue ETL requirement.