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

 

Question 221:

A company needs to ingest real-time application events and deliver them to Amazon S3. The solution should automatically scale and require minimal infrastructure management. Which AWS service is BEST suited for this requirement?

1. Amazon EMR
2. Amazon RDS
3. Amazon Kinesis Data Firehose
4. AWS Database Migration Service

Correct Answer: 3

Explanation:

Amazon Kinesis Data Firehose is a fully managed service designed to capture, buffer, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, and Amazon OpenSearch Service. It automatically scales according to incoming data volume, reducing the infrastructure management required by the data engineering team. Firehose can also invoke AWS Lambda for data transformation before delivery. Amazon EMR is primarily used for large-scale distributed processing, RDS provides relational databases, and AWS DMS focuses on database migration and replication. Therefore, Kinesis Data Firehose is the best choice for continuously delivering streaming events to Amazon S3.

Question 222:

A data engineer wants to reduce Amazon Athena query costs for a large dataset stored in Amazon S3. The data is frequently queried by year and month. Which approach is MOST effective?

1. Partition the data by year and month
2. Store all records in a single CSV file
3. Disable compression
4. Convert Parquet files to JSON

Correct Answer: 1

Explanation:

Partitioning allows Amazon Athena to scan only the portions of an S3 dataset that are relevant to a query. If data is partitioned by year and month, a query filtering on those columns can avoid scanning unrelated partitions. This reduces the amount of data scanned, which can lower Athena query costs and improve performance. Storing everything in a single file does not provide efficient partition pruning. Disabling compression generally increases the amount of data scanned, while converting Parquet to JSON usually makes analytical queries less efficient. Therefore, partitioning by commonly filtered attributes such as year and month is the most effective approach.

Question 223:

A streaming application occasionally sends the same transaction more than once. Each transaction contains a unique transaction ID. How should the data engineer prevent duplicate processing?

1. Increase the number of Kinesis shards
2. Use the transaction ID as an idempotency key
3. Increase the S3 bucket size
4. Disable retries

Correct Answer: 2

Explanation:

Using the unique transaction ID as an idempotency key allows the processing application to recognize whether a transaction has already been processed. When the same transaction arrives again, the application can identify the existing transaction ID and avoid performing the operation a second time. This approach is especially important in distributed and streaming systems where retries or duplicate deliveries can occur. Increasing Kinesis shard capacity improves throughput but does not prevent duplicates. S3 storage capacity is unrelated to duplicate processing, and disabling retries can reduce reliability. Therefore, an idempotency key based on the transaction ID is the correct solution.

Question 224:

A data engineer needs to automatically discover the schema of CSV files stored in Amazon S3 and create metadata that can be used by Amazon Athena. Which service should be used?

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

Correct Answer: 4

Explanation:

AWS Glue Crawlers automatically scan data sources such as Amazon S3, identify data formats, infer schemas, and create or update tables in the AWS Glue Data Catalog. These catalog tables can then be queried by Amazon Athena. This eliminates the need to manually define metadata for every dataset and is particularly useful when new files or datasets are regularly added. Amazon Macie is used to discover sensitive data, CloudTrail records API activity, and CloudWatch provides monitoring and observability. Therefore, AWS Glue Crawler is the appropriate service for automatically discovering schemas and registering metadata for Athena queries.

Question 225:

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

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

Correct Answer: 1

Explanation:

AWS Database Migration Service (AWS DMS) supports database migrations while minimizing downtime. It can perform an initial full load of the source database and then use Change Data Capture (CDC) to continuously replicate changes such as inserts, updates, and deletes. This allows the source database to remain operational while the target database is synchronized. Once replication is caught up, the organization can perform the final cutover. Athena is used for querying data, Glue DataBrew is focused on data preparation, and QuickSight provides business intelligence and visualization. Therefore, AWS DMS is the best solution for this migration requirement.

Question 226:

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

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

Correct Answer: 3

Explanation:

Using server-side encryption with AWS KMS keys (SSE-KMS) allows an organization to control encryption keys through AWS Key Management Service. Customer managed KMS keys provide greater control over key policies, permissions, rotation, and usage auditing. AWS CloudTrail can record KMS API activity, providing visibility into how keys are used. S3 Transfer Acceleration improves data transfer performance, Macie discovers sensitive information, and Versioning preserves previous object versions. These services do not provide the same customer-controlled encryption capability. Therefore, SSE-KMS with a customer managed KMS key is the appropriate solution for protecting sensitive S3 data.

Question 227:

An organization needs to protect Amazon S3 objects from accidental deletion for a defined retention period. Which S3 feature should the data engineer use?

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

Correct Answer: 1

Explanation:

Amazon S3 Object Lock allows organizations to prevent objects from being deleted or overwritten for a specified retention period. It is useful for regulatory records, financial information, backups, and other data that must remain immutable. Object Lock supports governance and compliance retention modes, depending on the required level of protection. S3 Transfer Acceleration is designed to improve transfer speeds, Access Points provide specialized access configurations, and S3 Select allows applications to retrieve specific portions of object data. Therefore, S3 Object Lock is the appropriate feature when objects must be protected from deletion or modification during a retention period.

Question 228:

A company wants an AWS Glue ETL job to start automatically whenever a new object is uploaded to a specific Amazon S3 bucket. Which solution is MOST appropriate?

1. Amazon Athena scheduled query
2. Amazon EventBridge rule
3. Amazon Macie job
4. S3 Glacier retrieval

Correct Answer: 2

Explanation:

Amazon EventBridge can receive events associated with Amazon S3 object creation and use those events to trigger downstream AWS services. An EventBridge rule can be configured to detect object-created events for a specific S3 bucket and start an AWS Glue ETL job. This creates an event-driven pipeline that begins processing as soon as new data arrives. Athena scheduled queries are intended for running SQL queries, Macie is used for sensitive-data discovery, and Glacier retrieval is related to archived object access. Therefore, an EventBridge rule is the most appropriate solution for automatically triggering the Glue job after an S3 upload.

Question 229:

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

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

Correct Answer: 3

Explanation:

CloudWatch Logs Insights provides an interactive query capability for analyzing logs stored in Amazon CloudWatch Logs. Data engineers can use its query language to filter events, search for terms such as ERROR, calculate statistics, identify trends, and investigate application problems. It is designed specifically for operational analysis of log data. The AWS Glue Data Catalog manages metadata for datasets, Athena primarily queries data stored in sources such as Amazon S3, and Lake Formation provides data lake governance and access control. Therefore, CloudWatch Logs Insights is the most suitable feature for searching application logs and identifying error events.

Question 230:

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

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

Correct Answer: 3

Explanation:

Amazon S3 Block Public Access provides controls designed to prevent public access to S3 buckets and objects. It can be applied at the account level, helping organizations establish a broad protection against accidental public exposure. This is particularly useful for data lakes and environments containing sensitive or confidential information. S3 Object Lock protects objects from deletion or modification, Versioning maintains multiple versions of objects, and Lifecycle rules automate transitions or expiration. None of those features specifically prevent public access. Therefore, S3 Block Public Access is the appropriate security control for preventing unintended public exposure.

Question 231:

A data engineer needs to orchestrate a pipeline containing AWS Glue jobs, AWS Lambda functions, conditional branches, retries, and error handling. Which AWS service is BEST suited for this requirement?

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

Correct Answer: 2

Explanation:

AWS Step Functions provides managed workflow orchestration and allows data engineers to coordinate multiple AWS services within a state machine. A workflow can invoke AWS Glue jobs, Lambda functions, and other services while supporting sequencing, branching, retries, timeouts, and error handling. Step Functions also maintains execution state and provides visibility into workflow progress. Athena is an analytical query service, S3 is object storage, and Macie is designed for sensitive-data discovery. Therefore, Step Functions is the most appropriate service for coordinating a complex data engineering pipeline that requires multiple processing steps and reliable workflow management.

Question 232:

A data engineer needs to continuously capture inserts, updates, and deletes from a relational database for downstream processing. Which AWS DMS capability should be used?

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

Correct Answer: 3

Explanation:

AWS Database Migration Service provides Change Data Capture (CDC), which continuously captures changes occurring in a source database after the initial full load. CDC can capture operations such as inserts, updates, and deletes and replicate those changes to supported target systems. This allows downstream systems to stay synchronized without repeatedly copying the entire source database. Glue Crawlers are used for metadata discovery, Athena is used for querying, and S3 Lifecycle manages object transitions and expiration. Therefore, CDC is the appropriate AWS DMS capability for continuously capturing database changes and delivering them to downstream processing systems.

Question 233:

An Amazon Redshift table is frequently queried using a timestamp column in range-based filters. Which table 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 an unstructured string

Correct Answer: 1

Explanation:

Amazon Redshift sort keys determine how table data is physically organized and can significantly improve query performance for workloads that frequently filter or range-scan on specific columns. If queries commonly use timestamp ranges, selecting an appropriate timestamp column as a sort key can allow Redshift to reduce the amount of data that needs to be scanned. The design should be based on actual query patterns and workload characteristics. Storing timestamps as JSON or unstructured strings generally makes analytical processing less efficient. Removing sort keys can also reduce performance for suitable workloads. Therefore, an appropriate timestamp sort key is the best choice.

Question 234:

A company wants to identify personally identifiable information (PII) stored in Amazon S3. Which AWS service is specifically designed to discover sensitive data in S3?

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

Correct Answer: 2

Explanation:

Amazon Macie is 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 data-security risks. Macie is especially useful for data lake environments where large numbers of S3 objects make manual inspection impractical. CloudTrail records API activity, Step Functions orchestrates workflows, and EventBridge routes events between AWS services. These services do not specialize in sensitive-data discovery. Therefore, Amazon Macie is the correct choice when the primary requirement is identifying sensitive information stored in Amazon S3.

Question 235:

A financial organization must retain records in Amazon S3 so that 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 Object Lock in Compliance mode
4. S3 Intelligent-Tiering

Correct Answer: 3

Explanation:

S3 Object Lock in Compliance mode provides strong immutability protection for objects 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 appropriate for financial records, regulatory archives, and other data subject to strict retention requirements. S3 Versioning maintains previous object versions but does not by itself enforce the same retention restrictions. Lifecycle rules manage object transitions and expiration, while Intelligent-Tiering optimizes storage costs based on access patterns. Therefore, S3 Object Lock in Compliance mode is the correct choice for legally protected immutable records.

Question 236:

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

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

Correct Answer: 3

Explanation:

When AWS Lambda is configured with an Amazon Kinesis Data Streams event source mapping, Lambda polls the stream and retrieves records in batches. It then invokes the Lambda function to process those records. This provides a serverless mechanism for processing streaming data without requiring the data engineer to maintain separate polling infrastructure. Lambda does not automatically convert every record into Parquet, nor does it directly replace or modify Kinesis shards. S3 notifications are unrelated to how Lambda consumes Kinesis records. Therefore, polling the stream and invoking the function with batches of records is the correct behavior.

Question 237:

A data pipeline receives CSV files from several sources. Some files 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 the differences
2. Delete all inconsistent files
3. Store every file without transformation
4. Normalize the schema using an ETL process

Correct Answer: 4

Explanation:

Schema normalization during an ETL process allows data engineers to standardize datasets received from different sources. The ETL job 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 the complexity faced by consumers. AWS Glue is commonly used to perform these types of transformations. Ignoring schema differences can cause query and processing failures, while deleting inconsistent files can cause unnecessary data loss. Storing everything unchanged shifts the transformation burden to downstream users. Therefore, normalizing the schema through ETL is the best approach.

Question 238:

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

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

Correct Answer: 2

Explanation:

A large number of small files can create significant overhead for distributed query engines because each object must be discovered, opened, and processed. Periodic compaction combines small Parquet files into fewer, appropriately sized files, reducing file-management overhead and improving query performance. Keeping Parquet compression enabled can also reduce storage and scanning costs. S3 Transfer Acceleration improves data transfer speed but does not solve small-file query overhead. Disabling partitioning can increase the amount of data scanned. Therefore, implementing a compaction process is the most appropriate solution for improving Athena performance when streaming ingestion produces excessive numbers of small files.

Question 239:

A data engineer needs to store database credentials securely for 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. AWS Secrets Manager
3. Amazon CloudWatch
4. Amazon Athena

Correct Answer: 2

Explanation:

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

Question 240:

A company needs to run an AWS Glue ETL job automatically every day at 2:00 AM. There is no external event that should trigger the job. Which solution should the data engineer use?

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

Correct Answer: 3

Explanation:

Amazon EventBridge Scheduler is designed to invoke AWS services according to a defined schedule. A data engineer can configure a recurring schedule to start an AWS Glue ETL job every day at 2:00 AM without requiring an external event. This is useful for batch-processing workloads that run at predictable times. Kinesis Data Streams is designed for streaming data, DMS CDC captures database changes continuously, and Macie is used for sensitive-data discovery. EventBridge Scheduler provides a managed scheduling mechanism without requiring additional servers or custom cron infrastructure. Therefore, EventBridge Scheduler is the most appropriate solution for this daily ETL requirement.