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

 

Question 81

A data engineer needs to load data from Amazon S3 into Amazon Redshift for analytical processing. Which AWS service can be used to orchestrate and transform the data before loading it?

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

Correct Answer: 2

Explanation:

AWS Glue is a managed data integration service that can extract data from sources such as Amazon S3, transform it according to business requirements, and load the processed data into destinations such as Amazon Redshift. Glue provides serverless ETL capabilities, allowing data engineers to build and schedule data processing jobs without managing the underlying infrastructure. Route 53 provides DNS services, CloudFront is a content delivery service, and SQS provides message queuing. Therefore, AWS Glue is the most appropriate option when a data engineer needs to prepare and transform S3 data before loading it into Amazon Redshift for analytical workloads.

Question 82

A company wants to prevent accidental deletion of important objects in an Amazon S3 bucket for a specific retention period. Which feature should the data engineer configure?

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

Correct Answer: 1

Explanation:

Amazon S3 Object Lock helps protect objects from deletion or overwriting for a defined retention period. It is especially useful for compliance workloads, financial records, backups, and other datasets that must remain immutable. Object Lock supports retention modes and legal holds, allowing organizations to control when protected objects can be modified or deleted. S3 Transfer Acceleration improves transfer speeds, Intelligent-Tiering automatically optimizes storage based on access patterns, and Access Points provide specialized access controls. Therefore, S3 Object Lock is the appropriate feature when important objects must be protected against accidental deletion or modification during a specified retention period.

Question 83

A data engineer needs to process streaming records in real time and perform custom transformations before delivering the results to Amazon S3. Which combination is most appropriate?

1. Amazon RDS and Amazon Athena
2. Amazon Kinesis Data Streams and AWS Lambda
3. Amazon Route 53 and Amazon CloudFront
4. Amazon EBS and Amazon SQS

Correct Answer: 2

Explanation:

Amazon Kinesis Data Streams can continuously ingest real-time records, while AWS Lambda can process those records using custom application logic. This combination is useful for event-driven data pipelines where incoming records need to be transformed before being stored or sent to another destination. Lambda can perform filtering, enrichment, validation, or formatting operations and can then pass the processed data to downstream AWS services. RDS and Athena are not designed as a real-time streaming transformation combination, while Route 53, CloudFront, EBS, and SQS serve different purposes. Therefore, Kinesis Data Streams combined with Lambda is an appropriate architecture for real-time record processing and transformation.

Question 84

A company stores large analytical datasets in Amazon S3 and wants to improve query performance by compressing the data and storing only the columns needed by queries. Which format is most appropriate?

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

Correct Answer: 3

Explanation:

Apache Parquet is a columnar storage format that is highly suitable for analytical workloads. It stores data by columns, allowing query engines such as Amazon Athena to read only the columns required by a query. Parquet also supports efficient compression, which can reduce storage requirements and the amount of data scanned during analytical queries. CSV and JSON are commonly used data interchange formats but are not as efficient for column-based analytical workloads, while XML generally introduces additional processing overhead. Therefore, Parquet is the most appropriate format when a company wants to improve analytical query performance and reduce storage and scanning costs for large datasets stored in Amazon S3.

Question 85

A data engineer needs to identify which users made API calls that modified AWS resources during a specific period. Which service should be queried?

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

Correct Answer: 2

Explanation:

AWS CloudTrail records API activity across AWS services and provides information about actions performed on resources. CloudTrail events can include the identity that made the API call, the requested operation, the affected resource, the timestamp, and other relevant information. This makes CloudTrail useful for security investigations, compliance audits, and tracking changes to AWS resources. CloudWatch focuses primarily on monitoring metrics and logs, Athena is a query service, and Glue is a data integration platform. Therefore, AWS CloudTrail should be used when a data engineer needs to determine which users or identities performed API actions that modified AWS resources during a specific period.

Question 86

A company needs to run a recurring data pipeline every six hours without manually starting the workflow. Which AWS service can provide the scheduling capability?

1. Amazon EventBridge
2. Amazon S3
3. Amazon DynamoDB
4. Amazon ElastiCache

Correct Answer: 1

Explanation:

Amazon EventBridge can schedule events using rate or cron expressions and trigger supported AWS services or workflows automatically. A schedule can be configured to run a data processing pipeline every six hours without requiring manual intervention. EventBridge can integrate with services such as AWS Lambda, AWS Step Functions, and other AWS targets. Amazon S3 provides object storage, DynamoDB is a NoSQL database, and ElastiCache provides in-memory caching. Therefore, Amazon EventBridge is the appropriate service for implementing a recurring schedule that automatically starts a data pipeline at a specified interval.

Question 87

A data engineer wants to query only the data belonging to a particular region from a dataset partitioned by region. Which technique can reduce the amount of data scanned?

1. Data encryption
2. Partition pruning
3. Data replication
4. Data masking

Correct Answer: 2

Explanation:

Partition pruning allows a query engine to skip partitions that do not match the query conditions. If a dataset stored in Amazon S3 is partitioned by region and the query filters for a specific region, Amazon Athena can scan only the relevant partition rather than reading the entire dataset. This can improve query performance and reduce the amount of data scanned, which can lower Athena query costs. Encryption protects data, replication creates additional copies, and masking hides sensitive values. Therefore, partition pruning is the correct technique when a data engineer wants to efficiently query a specific region from a partitioned dataset.

Question 88

A company wants to discover personally identifiable information stored in Amazon S3 and receive findings about sensitive data. Which AWS service should be used?

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

Correct Answer: 1

Explanation:

Amazon Macie is an AWS security and privacy service designed to discover and help protect sensitive data stored in Amazon S3. It can use machine learning and pattern matching to identify certain types of sensitive information, including personally identifiable information. Macie can provide findings that help organizations understand where sensitive data exists and improve their security and compliance posture. Amazon Redshift is a data warehouse, CloudFront is a content delivery service, and Step Functions orchestrates workflows. Therefore, Amazon Macie is the most appropriate service when the requirement is to discover sensitive information and personally identifiable data stored in Amazon S3.

Question 89

A data engineer needs to combine records from two tables and retain all records from the left table, including records that do not have a matching record in the right table. Which SQL JOIN should be used?

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

Correct Answer: 3

Explanation:

A LEFT OUTER JOIN returns all records from the left table and the matching records from the right table. If a row in the left table does not have a corresponding match in the right table, the right-side columns are returned as NULL values. An INNER JOIN returns only matching records, a FULL OUTER JOIN returns unmatched records from both tables, and a CROSS JOIN produces combinations of rows between the tables. Therefore, LEFT OUTER JOIN is the correct choice when all records from the left dataset must be retained regardless of whether a matching record exists in the right dataset.

Question 90

A company needs to ensure that an AWS Glue job can access an encrypted Amazon S3 bucket using an AWS KMS key. Which permissions may be required for the Glue job’s IAM role?

1. Permissions to use the KMS key and access the S3 objects
2. Only Amazon CloudFront permissions
3. Only Amazon Route 53 permissions
4. Only Amazon SQS permissions

Correct Answer: 1

Explanation:

When an AWS Glue job accesses objects encrypted with an AWS KMS key, its execution role generally needs appropriate permissions to access the S3 objects and use the KMS key for encryption or decryption operations. The exact permissions depend on the workload and key policy configuration, but the IAM role and KMS key policy must allow the required operations. CloudFront, Route 53, and SQS permissions do not provide the necessary access to encrypted S3 objects. Therefore, the correct approach is to grant the Glue job’s role the required S3 permissions and appropriate KMS permissions while following the principle of least privilege.

Question 91

A data engineer needs to remove duplicate rows from the result of a SQL query. Which SQL keyword should be used?

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

Correct Answer: 3

Explanation:

The SQL DISTINCT keyword removes duplicate rows from the result set based on the selected columns. When SELECT DISTINCT is used, the query returns only unique combinations of the selected column values. WHERE is used to filter rows based on conditions, GROUP BY organizes rows into groups for aggregation, and ORDER BY sorts the results. Therefore, DISTINCT is the correct keyword when a data engineer needs to remove duplicate rows from query results. It should be used carefully on large datasets because determining unique rows can require additional processing depending on the query engine and dataset size.

Question 92

A company wants to separate raw, transformed, and curated datasets within an Amazon S3-based data lake. Which approach is most appropriate?

1. Store every dataset in one directory without organization
2. Use separate logical S3 prefixes or zones for each data stage
3. Store all data in Amazon EBS
4. Move all datasets to Amazon SQS

Correct Answer: 2

Explanation:

Separating raw, transformed, and curated datasets into logical S3 prefixes or data lake zones provides better organization and governance. A common design uses a raw or landing zone for source data, a transformed zone for processed datasets, and a curated zone for business-ready information. This structure makes it easier to apply appropriate permissions, lifecycle policies, processing rules, and data quality controls at each stage. EBS is block storage for compute instances, while SQS is a messaging service and is not designed to store analytical datasets. Therefore, using separate logical S3 prefixes or zones is an effective approach for organizing different stages of a data lake.

Question 93

A data engineer wants to run a SQL query on Amazon Athena and limit the results to 100 rows. Which SQL clause can be used in supported query syntax?

1. LIMIT
2. GROUP BY
3. HAVING
4. UNION

Correct Answer: 1

Explanation:

The LIMIT clause restricts the number of rows returned by a SQL query. For example, a query can use LIMIT 100 to return no more than 100 rows from the result set. This is useful when testing queries, previewing data, or retrieving a small sample instead of returning a very large result set. GROUP BY groups records for aggregation, HAVING filters groups after aggregation, and UNION combines compatible result sets. Therefore, LIMIT is the appropriate SQL clause for restricting the number of rows returned by a query in Amazon Athena and other SQL-compatible analytics engines.

Question 94

A company wants to encrypt data while it is being transmitted between an application and an AWS service. Which security mechanism is commonly used?

1. Amazon S3 Lifecycle
2. TLS
3. S3 Object Lock
4. AWS Glue Crawler

Correct Answer: 2

Explanation:

Transport Layer Security, or TLS, is commonly used to encrypt data while it is being transmitted between systems. It helps protect information from interception or unauthorized observation while traveling across a network. This is different from encryption at rest, which protects stored data using mechanisms such as AWS KMS or Amazon S3 server-side encryption. S3 Lifecycle manages object transitions and expiration, Object Lock protects objects from deletion or modification, and Glue Crawlers discover metadata. Therefore, TLS is the appropriate security mechanism when the requirement is to protect data while it is being transmitted between an application and an AWS service.

Question 95

A data engineer needs to analyze large amounts of data using distributed computing and wants the processing cluster to automatically scale based on workload requirements. Which AWS service is suitable?

1. Amazon EMR
2. Amazon Route 53
3. Amazon SQS
4. Amazon CloudFront

Correct Answer: 1

Explanation:

Amazon EMR is a managed service designed for distributed data processing using frameworks such as Apache Spark and Hadoop. EMR supports scalable processing environments and provides capabilities that allow clusters to be configured and adjusted according to workload requirements. This makes it suitable for large-scale data transformation, analytics, and batch processing workloads. Route 53 provides DNS services, SQS provides message queuing, and CloudFront distributes content globally. Therefore, Amazon EMR is the appropriate service when a data engineer needs distributed computing for large datasets and wants to use scalable managed infrastructure for processing workloads.

Question 96

A company wants to automatically detect when new objects arrive in an Amazon S3 bucket and start a data processing workflow. Which AWS capability can be used?

1. S3 event notifications
2. S3 Glacier
3. S3 Object Lock
4. S3 Transfer Acceleration

Correct Answer: 1

Explanation:

Amazon S3 event notifications allow S3 to generate events when specific actions occur, such as when a new object is created. These events can be integrated with services such as AWS Lambda, Amazon SQS, Amazon SNS, and event-driven workflows to automatically initiate processing. This enables organizations to build event-driven data pipelines that respond quickly when new files arrive. Glacier is a storage class for archival data, Object Lock protects objects from modification or deletion, and Transfer Acceleration improves data transfer performance. Therefore, S3 event notifications are the appropriate capability for detecting newly uploaded objects and triggering downstream processing automatically.

Question 97

A data engineer wants to create an alert when an AWS Glue job fails. Which CloudWatch feature should be configured?

1. CloudWatch dashboard only
2. CloudWatch alarm
3. CloudWatch log group deletion
4. CloudWatch metric removal

Correct Answer: 2

Explanation:

A CloudWatch alarm can monitor a metric or condition and initiate an action when a defined threshold or state is reached. For data processing workloads, CloudWatch can be integrated with AWS Glue job monitoring so that job failures or other operational conditions can be detected and alerts can be generated. Dashboards are useful for visualizing metrics but do not themselves provide the same threshold-based alerting capability. Deleting log groups or metrics would remove monitoring information rather than improve it. Therefore, a CloudWatch alarm is the appropriate feature for detecting conditions such as failed Glue jobs and notifying the responsible team.

Question 98

A company wants to store database credentials securely without embedding them directly in application code. Which AWS service is designed for securely storing secrets?

1. Amazon S3
2. AWS Secrets Manager
3. Amazon CloudFront
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 other secrets. Applications can retrieve secrets programmatically rather than embedding credentials directly in source code or configuration files. Secrets Manager also supports features such as automatic rotation for supported secrets and integration with AWS identity and access management mechanisms. Amazon S3 is object storage, CloudFront provides content delivery, and Athena is a serverless query service. Therefore, AWS Secrets Manager is the most appropriate service when database credentials or other sensitive secrets need to be securely stored and accessed by applications.

Question 99

A data engineer needs to identify whether a data pipeline is producing records with missing required fields before they are loaded into a curated dataset. Which data quality technique should be applied?

1. Data validation
2. Data replication
3. Data compression
4. Data encryption

Correct Answer: 1

Explanation:

Data validation checks whether incoming records meet predefined quality rules before they are accepted into a downstream system. For example, a pipeline can verify that required fields are present, values have the correct data types, and business rules are satisfied. Records that fail validation can be rejected, quarantined, or sent for remediation. Replication creates copies of data, compression reduces storage size, and encryption protects data from unauthorized access. Therefore, data validation is the correct technique for identifying records with missing required fields before they enter a curated dataset. Implementing validation early helps maintain data quality and improves trust in analytical results.

Question 100

A company needs to provide analysts with a centralized and governed data lake while applying fine-grained permissions to databases, tables, and columns. Which AWS service should be used?

1. Amazon ElastiCache
2. Amazon CloudFront
3. AWS Lake Formation
4. Amazon SQS

Correct Answer: 3

Explanation:

AWS Lake Formation helps organizations build and govern data lakes on AWS and provides centralized management of data access. It supports fine-grained permissions that can be applied to data lake resources such as databases, tables, and columns. This allows organizations to give analysts access to only the information they are authorized to use while maintaining centralized governance. Amazon ElastiCache provides in-memory caching, CloudFront is a content delivery service, and SQS provides message queuing. Therefore, AWS Lake Formation is the most appropriate service when a company needs a governed data lake with centralized and fine-grained access controls for analytical users.