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

 

Question 141

A data engineer needs to catalog datasets stored in Amazon S3 so that they can be queried by Amazon Athena. Which AWS service should be used to store the table metadata?

1. Amazon CloudWatch
2. AWS Glue Data Catalog
3. Amazon SQS
4. AWS Secrets Manager

Correct Answer: 2

Explanation:

The AWS Glue Data Catalog is a centralized metadata repository that stores information about datasets, including their schemas, locations, partitions, and formats. Amazon Athena can use this metadata to understand the structure of data stored in Amazon S3 and query it using SQL. Glue crawlers can automatically discover schemas and populate the catalog. CloudWatch is primarily used for monitoring, SQS provides message queuing, and Secrets Manager securely stores credentials and other secrets. Therefore, the AWS Glue Data Catalog is the appropriate service for maintaining metadata about S3 datasets that need to be queried with Athena.

Question 142

A company needs to encrypt sensitive data stored in Amazon S3 using customer-managed encryption keys. Which AWS service should manage the encryption keys?

1. AWS Key Management Service (AWS KMS)
2. Amazon Athena
3. Amazon EventBridge
4. AWS Glue

Correct Answer: 1

Explanation:

AWS Key Management Service (AWS KMS) allows organizations to create and manage cryptographic keys used to protect data. Amazon S3 can integrate with KMS to encrypt objects using server-side encryption with AWS KMS keys (SSE-KMS). Customer-managed KMS keys provide greater control over permissions, key policies, and auditing compared with keys managed entirely by AWS. Athena is used for querying data, EventBridge handles events and schedules, and Glue provides data integration and ETL capabilities. Therefore, AWS KMS is the correct choice when sensitive S3 data needs to be encrypted using customer-managed encryption keys.

Question 143

A data engineer is designing an S3-based data lake and wants to separate incoming raw data from transformed data and curated datasets. Which approach is most appropriate?

1. Store everything in a single S3 prefix
2. Create separate data lake zones or prefixes
3. Store all data in Amazon RDS
4. Convert every dataset to JSON

Correct Answer: 2

Explanation:

A common data lake architecture separates data into logical zones such as raw, transformed, and curated layers. The raw zone preserves data in its original form, while transformed data has undergone cleaning or processing, and curated data is prepared for business and analytical consumption. Separating these layers using S3 buckets or prefixes makes the environment easier to manage, govern, secure, and process. Storing everything in one prefix makes lifecycle management and access control more difficult. RDS is not designed as a general-purpose data lake, and converting everything to JSON does not provide architectural separation. Therefore, separate data lake zones are the best approach.

Question 144

A data engineer needs to combine customer information from one table with order information from another table while returning only customers who have matching orders. Which SQL operation should be used?

1. LEFT JOIN
2. FULL OUTER JOIN
3. INNER JOIN
4. CROSS JOIN

Correct Answer: 3

Explanation:

An INNER JOIN returns only rows where the join condition matches in both tables. For example, joining a customer table with an orders table using customer_id will return customers who have corresponding orders. A LEFT JOIN would return all customers, including those without orders, while a FULL OUTER JOIN would include unmatched rows from both tables. A CROSS JOIN creates combinations between every row in both tables and is generally inappropriate for this requirement. Therefore, an INNER JOIN is the correct SQL operation when the result should contain only customers with matching order records.

Question 145

A data engineer wants to reduce the amount of data scanned by Athena when querying a large S3 dataset. Which strategy is most effective?

1. Store all data as uncompressed CSV
2. Use partitioning and columnar formats
3. Increase the number of S3 buckets
4. Disable query result caching

Correct Answer: 2

Explanation:

Using partitioning and columnar formats such as Apache Parquet can significantly reduce the amount of data Athena needs to scan. Partitioning allows Athena to skip entire partitions that do not match query filters, while columnar formats allow the query engine to read only the required columns. Compression further reduces the amount of data that must be read. Storing data as uncompressed CSV generally requires more data to be scanned. Increasing the number of S3 buckets does not automatically improve query efficiency, and disabling result caching would not reduce the initial amount of data scanned. Therefore, partitioning combined with columnar storage is the best strategy.

Question 146

A company wants to automatically detect schema changes in files arriving in an S3 data lake. Which AWS Glue capability can help discover the structure of the incoming data?

1. AWS Glue Crawler
2. AWS Glue Connection only
3. AWS Glue Workflow only
4. AWS Glue Trigger only

Correct Answer: 1

Explanation:

An AWS Glue Crawler can inspect data sources such as Amazon S3 and determine the structure of datasets. It can identify formats, infer schemas, detect partitions, and populate or update metadata in the AWS Glue Data Catalog. This makes crawlers useful when files arrive regularly and their schema may change over time. Glue connections are primarily used to provide connection information for data sources, while workflows and triggers help orchestrate jobs and processes. Therefore, an AWS Glue Crawler is the most appropriate capability for discovering and updating metadata about the structure of incoming S3 data.

Question 147

A data engineer needs to ensure that an ETL job can access an encrypted S3 bucket. Which permission is required when the bucket uses SSE-KMS encryption?

1. Only s3:ListBucket
2. Only s3:GetBucketLocation
3. Appropriate S3 permissions and KMS permissions
4. Only CloudWatch permissions

Correct Answer: 3

Explanation:

When S3 objects are encrypted using AWS KMS keys, an application or ETL job generally needs both the appropriate S3 permissions and permissions to use the relevant KMS key. For example, reading encrypted objects can require S3 permissions such as s3:GetObject and KMS permissions such as kms:Decrypt. The exact permissions depend on the operations being performed. Simply granting bucket listing permission is insufficient, and CloudWatch permissions do not provide access to encrypted S3 objects. Therefore, the ETL job must have the required S3 permissions along with the necessary permissions on the KMS key.

Question 148

A company wants to process a large dataset stored in Amazon S3 using SQL without managing servers. Which service provides this capability?

1. Amazon Athena
2. Amazon EC2
3. Amazon EBS
4. Amazon Route 53

Correct Answer: 1

Explanation:

Amazon Athena is a serverless interactive query service that allows users to analyze data directly in Amazon S3 using standard SQL. It does not require the data engineer to provision or manage database servers or clusters. Athena can query many common formats, including CSV, JSON, ORC, and Parquet, and can integrate with the AWS Glue Data Catalog for metadata. EC2 provides virtual compute instances that require infrastructure management, EBS provides block storage for EC2, and Route 53 provides DNS services. Therefore, Athena is the most suitable option for running serverless SQL queries directly against large datasets stored in Amazon S3.

Question 149

A streaming application must preserve the order of records for events belonging to the same customer. Which Kinesis Data Streams concept should be used?

1. S3 prefix
2. Partition key
3. IAM policy
4. Glue crawler

Correct Answer: 2

Explanation:

Amazon Kinesis Data Streams uses partition keys to determine which shard receives a record. Records with the same partition key are routed to the same shard, allowing their relative ordering to be maintained within that shard. A customer identifier can therefore be used as the partition key when events for each customer need to be processed in order. S3 prefixes organize objects in S3, IAM policies control permissions, and Glue crawlers discover metadata. Therefore, using a suitable customer-based partition key is the correct approach for maintaining ordering of records belonging to the same customer in Kinesis Data Streams.

Question 150

A data engineer wants to prevent an Amazon S3 object from being deleted or overwritten for a defined retention period. Which feature should be enabled?

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

Correct Answer: 3

Explanation:

Amazon S3 Object Lock helps protect objects from deletion or overwriting for a specified retention period. It is useful for compliance requirements and workloads that need immutable data. Object Lock supports retention settings and legal holds that can prevent modifications or deletion according to the configured protection model. S3 Transfer Acceleration improves transfer performance, Intelligent-Tiering automatically moves objects between access tiers, and S3 Inventory provides reports about objects and their metadata. Therefore, S3 Object Lock is the correct feature when objects must remain protected from deletion or overwriting during a defined retention period.

Question 151

A company wants to send notifications when an AWS Glue ETL job fails. Which combination is appropriate for implementing this requirement?

1. AWS Glue and Amazon SNS
2. Amazon Route 53 and Amazon EBS
3. Amazon S3 Glacier and Athena
4. Amazon DynamoDB and CloudFront

Correct Answer: 1

Explanation:

AWS Glue can provide job execution status information, and Amazon SNS can be used to deliver notifications to subscribers when an important event occurs. A data engineering workflow can detect Glue job failures and publish a notification to an SNS topic, which can then send messages through supported notification channels. Route 53 is a DNS service, EBS provides block storage, Glacier is a storage class, Athena is a query service, DynamoDB is a NoSQL database, and CloudFront is a content delivery service. Therefore, integrating AWS Glue job monitoring with Amazon SNS is an appropriate way to notify teams about ETL failures.

Question 152

A data engineer wants to make an ETL pipeline idempotent so that rerunning the same input does not create duplicate results. Which approach is best?

1. Always append data without checking existing records
2. Use unique identifiers and upsert or deduplication logic
3. Disable all retries
4. Delete the source data immediately

Correct Answer: 2

Explanation:

An idempotent pipeline produces the same correct result even when the same operation is executed multiple times. Using unique identifiers allows the pipeline to recognize records that have already been processed. Upsert logic can update existing records rather than creating duplicates, while deduplication can remove repeated events. Simply appending every record can create duplicate results, disabling retries can reduce reliability, and deleting source data immediately can cause data loss. Therefore, unique identifiers combined with upsert or deduplication logic is an effective strategy for building idempotent ETL pipelines that can safely handle retries and repeated executions.

Question 153

A company needs to store frequently accessed key-value data in memory to reduce database latency. Which AWS service is most appropriate?

1. Amazon ElastiCache
2. Amazon Glacier
3. Amazon Athena
4. AWS CloudTrail

Correct Answer: 1

Explanation:

Amazon ElastiCache provides managed in-memory caching using technologies such as Redis and Memcached. It can store frequently accessed data in memory so applications can retrieve information faster and reduce the number of requests sent to backend databases. This can improve application performance and reduce database load. Amazon Glacier is designed for archival storage, Athena is used for SQL analytics on data sources such as S3, and CloudTrail records AWS API activity. Therefore, Amazon ElastiCache is the appropriate service when frequently accessed key-value data needs to be stored in memory to reduce latency.

Question 154

A data engineer needs to monitor the number of records processed by a pipeline and create a visual representation of the metric over time. Which CloudWatch feature should be used?

1. CloudWatch Dashboard
2. AWS KMS
3. Amazon Macie
4. AWS Secrets Manager

Correct Answer: 1

Explanation:

Amazon CloudWatch Dashboards provide customizable visualizations of metrics and other monitoring information. A data engineer can use a dashboard to display metrics such as records processed, processing duration, failed records, or pipeline execution counts over time. Dashboards make it easier to monitor the health and performance of data pipelines from a centralized view. AWS KMS manages encryption keys, Macie helps discover sensitive data in S3, and Secrets Manager securely stores credentials and secrets. Therefore, a CloudWatch Dashboard is the most appropriate feature for creating a visual representation of pipeline metrics and monitoring their behavior over time.

Question 155

A company receives JSON files in Amazon S3 and wants to convert them into Parquet files to improve analytical query performance. Which service can perform this transformation as part of an ETL process?

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

Correct Answer: 2

Explanation:

AWS Glue provides serverless ETL capabilities that can read JSON data from Amazon S3, transform the records, and write the results in optimized formats such as Apache Parquet. Converting JSON to Parquet can improve analytical performance because Parquet is columnar and supports efficient compression and column pruning. Glue can automate this transformation through Glue jobs and can use metadata from the Glue Data Catalog. Route 53 manages DNS, CloudFront provides content delivery, and CloudTrail records AWS API activity. Therefore, AWS Glue is the appropriate service for converting JSON files into Parquet as part of an ETL pipeline.

Question 156

A data engineer wants to query only records from the year 2026 in an S3 dataset partitioned by year. Which query technique helps Athena avoid scanning other years?

1. Filtering on the partition column
2. Selecting every column
3. Removing the partition structure
4. Converting Parquet to CSV

Correct Answer: 1

Explanation:

When an S3 dataset is partitioned by a column such as year, Athena can use a filter on that partition column to perform partition pruning. For example, a query with a condition such as WHERE year = 2026 allows Athena to focus on the relevant partition instead of scanning data from other years. This can reduce data scanned, improve query performance, and lower Athena query costs. Selecting every column does not improve scanning efficiency, removing partitions eliminates this optimization, and converting Parquet to CSV generally makes analytical queries less efficient. Therefore, filtering on the partition column is the correct approach.

Question 157

A company wants to automatically start a Step Functions workflow whenever a specific event occurs in AWS. Which service can route the event to the workflow?

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

Correct Answer: 2

Explanation:

Amazon EventBridge is an event-driven service that can receive events from AWS services and applications and route them to configured targets. A Step Functions state machine can be configured as a target, allowing an event to automatically start a workflow. This is useful for event-driven data pipelines where processing should begin when a specific condition occurs, such as an object being created or a service status changing. EBS provides block storage, RDS provides managed relational databases, and Macie focuses on sensitive-data discovery. Therefore, EventBridge is the appropriate service for connecting AWS events to a Step Functions workflow.

Question 158

A data engineer wants to ensure that sensitive data stored in S3 is encrypted at rest. Which encryption option provides server-side encryption using AWS-managed S3 keys?

1. SSE-S3
2. TLS
3. Client-side compression
4. S3 Lifecycle

Correct Answer: 1

Explanation:

SSE-S3 provides server-side encryption for Amazon S3 objects using encryption keys managed by Amazon S3. It protects data at rest without requiring the data engineer to manage encryption keys directly. TLS is used to protect data in transit rather than encrypting objects at rest, compression reduces data size, and S3 Lifecycle manages object transitions and expiration. If an organization needs more control over encryption keys, SSE-KMS can be used instead. Therefore, SSE-S3 is the appropriate option when the requirement is server-side encryption at rest using keys managed by Amazon S3.

Question 159

A company wants to identify failed records during an ETL transformation and prevent those records from being included in the curated dataset. What should the data engineer implement?

1. Data validation and error handling
2. Disable schema validation
3. Ignore transformation failures
4. Copy every record directly to the curated layer

Correct Answer: 1

Explanation:

Data validation and error handling allow an ETL pipeline to identify records that do not meet expected quality or schema requirements. Invalid records can be separated into a quarantine or error location for later investigation, while valid records continue through the transformation process into the curated dataset. This approach protects the quality and reliability of downstream data. Disabling validation or ignoring transformation errors can allow incorrect information into analytical datasets, while copying every record directly to the curated layer bypasses quality controls. Therefore, implementing validation and appropriate error handling is the best approach for keeping invalid records out of the curated dataset.

Question 160

A data engineer needs to store data that is accessed frequently for a short period and then becomes rarely accessed. Which S3 storage class can automatically move objects between access tiers based on changing access patterns?

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

Correct Answer: 3

Explanation:

S3 Intelligent-Tiering is designed for data with changing or unknown access patterns. It automatically moves objects between appropriate access tiers based on observed access patterns, helping optimize storage costs without requiring the data engineer to manually predict when objects will become infrequently accessed. S3 Standard is designed for frequently accessed data, while Glacier Deep Archive is intended for long-term archival with very infrequent access. S3 One Zone-IA is designed for infrequently accessed data stored in a single Availability Zone. Therefore, S3 Intelligent-Tiering is the best choice when object access patterns change over time.