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

 

Question 61

A company needs to process a large number of records continuously as they arrive and wants to retain the records for later consumption by multiple applications. Which AWS service is most appropriate?

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

Correct Answer: 3

Explanation:

Amazon Kinesis Data Streams is designed to collect and retain real-time streaming data for consumption by multiple applications or services. Producers can continuously send records to a stream, while different consumers can independently process those records. This makes Kinesis Data Streams useful for real-time analytics, application monitoring, log processing, and event-driven workloads. Amazon Athena is primarily used to query data, RDS provides relational database capabilities, and Redshift is a data warehouse designed for analytical workloads. Therefore, Kinesis Data Streams is the best choice when an organization needs to continuously ingest records and make them available for multiple downstream consumers.

Question 62

A data engineer wants to prevent an AWS Glue ETL job from accessing resources that are not required for its processing. Which security principle should be followed?

1. Least privilege
2. Maximum availability
3. Horizontal scaling
4. Data duplication

Correct Answer: 1

Explanation:

The principle of least privilege requires granting users, applications, and services only the permissions they need to perform their specific tasks. For an AWS Glue ETL job, the associated IAM role should provide access only to the required S3 buckets, databases, tables, or other resources. This limits the potential impact if credentials or the workload are compromised. Maximum availability focuses on system uptime, horizontal scaling concerns resource capacity, and data duplication involves creating additional copies of information. Therefore, least privilege is the correct security principle for restricting an ETL job’s access to only the resources necessary for its operation.

Question 63

A company needs to create a table definition for data stored in Amazon S3 so that Amazon Athena can query the data. Which component provides the required metadata?

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

Correct Answer: 2

Explanation:

The AWS Glue Data Catalog stores metadata such as table names, column definitions, data types, partitions, and S3 locations. Amazon Athena can use this catalog metadata to understand the structure of datasets stored in Amazon S3 and execute SQL queries against them. CloudWatch is used for monitoring, SQS provides message queuing, and AWS KMS manages encryption keys. Therefore, the AWS Glue Data Catalog provides the metadata required by Athena to query S3-based datasets. Data engineers can populate the catalog manually or use AWS Glue Crawlers to automatically discover schemas from supported data sources.

Question 64

A data engineer needs to transform raw JSON data into Parquet format before storing the processed data in Amazon S3. Which service can perform this transformation as part of an ETL pipeline?

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

Correct Answer: 3

Explanation:

AWS Glue provides managed ETL capabilities that can read data from sources such as Amazon S3, apply transformations, and write the resulting data in another format or location. A Glue ETL job can transform raw JSON data into Parquet and store the processed output back in Amazon S3. Using Parquet can improve analytical query efficiency because it is a columnar format. Route 53 provides DNS services, CloudFront provides content delivery, and SQS provides asynchronous messaging. Therefore, AWS Glue is the most appropriate service for transforming JSON data into Parquet as part of a managed ETL pipeline.

Question 65

A company wants to keep frequently queried data in memory to reduce query latency for an application. Which AWS service is designed for this purpose?

1. Amazon ElastiCache
2. Amazon S3 Glacier
3. AWS Glue
4. Amazon Athena

Correct Answer: 1

Explanation:

Amazon ElastiCache provides managed in-memory caching using technologies such as Redis and Memcached. Keeping frequently accessed data in memory can significantly reduce latency compared with retrieving the same information from a persistent database or storage system. This makes ElastiCache useful for applications that require fast access to frequently requested data. S3 Glacier is designed for archival storage, AWS Glue provides data integration and ETL capabilities, and Athena is used for SQL queries against data sources such as Amazon S3. Therefore, Amazon ElastiCache is the appropriate service when an application needs low-latency access to frequently queried data stored in memory.

Question 66

A data engineer wants to ensure that a pipeline can identify when a processing job has failed and investigate the cause later. Which capability is most important?

1. Disabling logs
2. Logging and monitoring
3. Removing error handling
4. Increasing storage replication only

Correct Answer: 2

Explanation:

Logging and monitoring are essential for identifying pipeline failures and understanding their causes. Logs can provide detailed information about processing steps, errors, warnings, and application behavior, while monitoring systems can track metrics and trigger alerts when predefined conditions occur. AWS services such as Amazon CloudWatch can collect logs and metrics from many workloads and provide alarms for operational conditions. Disabling logs makes troubleshooting more difficult, removing error handling reduces reliability, and storage replication alone does not provide pipeline observability. Therefore, implementing appropriate logging and monitoring is critical for detecting failures, investigating issues, and maintaining reliable data processing workflows.

Question 67

A company has data stored in Amazon S3 that is accessed unpredictably. The company wants to automatically optimize storage costs based on changing access patterns. Which storage class should be considered?

1. S3 Glacier Deep Archive
2. S3 Standard
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 their access behavior, helping reduce storage costs without requiring the data engineer to manually determine when objects should transition. S3 Standard is suitable for frequently accessed data, Glacier Deep Archive is intended for long-term archival workloads, and One Zone-IA is intended for infrequently accessed data that can tolerate storage in a single Availability Zone. Therefore, S3 Intelligent-Tiering is the most appropriate choice when access patterns are unpredictable and automatic cost optimization is desired.

Question 68

A data engineer needs to execute a function automatically whenever a new file is uploaded to an Amazon S3 bucket. Which AWS service can provide the serverless compute capability?

1. Amazon Redshift
2. AWS Lambda
3. Amazon RDS
4. Amazon EBS

Correct Answer: 2

Explanation:

AWS Lambda provides serverless compute that can execute code in response to events. Amazon S3 can generate object-created events when new files are uploaded, and those events can trigger a Lambda function automatically. This allows data engineers to build event-driven processing pipelines without managing servers. The Lambda function could validate the file, transform metadata, initiate an ETL process, or perform another required action. Amazon Redshift and RDS are database services, while EBS provides block storage. Therefore, AWS Lambda is the appropriate service when custom code needs to execute automatically in response to new objects arriving in an S3 bucket.

Question 69

A company wants to analyze historical data while ensuring that only the necessary columns are read from a large analytical dataset. Which file format is best suited for this workload?

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

Correct Answer: 4

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 needed by a query rather than scanning the complete dataset. This can reduce data scanned, improve query performance, and lower costs when using services such as Amazon Athena. CSV, JSON, and XML are not generally optimized for column-level analytical access in the same way. Therefore, Apache Parquet is the best choice when a data engineer needs to analyze large datasets while minimizing the amount of unnecessary data read during analytical queries.

Question 70

A data engineer needs to orchestrate a workflow that includes AWS Lambda, AWS Glue, and Amazon EMR tasks with retries and conditional branching. Which AWS service should be used?

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

Correct Answer: 1

Explanation:

AWS Step Functions is a serverless workflow orchestration service that can coordinate multiple AWS services and application components. It supports sequential and parallel execution, conditional branching, retries, error handling, and state management. This makes it suitable for complex data workflows involving services such as Lambda, AWS Glue, and Amazon EMR. Amazon S3 provides object storage, Athena provides serverless SQL querying, and CloudFront provides content delivery. Therefore, AWS Step Functions is the most appropriate service for orchestrating multiple data processing tasks while providing workflow control, retries, and conditional execution.

Question 71

A company wants to reduce the amount of data scanned by Amazon Athena by storing data in smaller logical groups based on a frequently filtered column. Which technique should be used?

1. Replication
2. Partitioning
3. Encryption
4. Tokenization

Correct Answer: 2

Explanation:

Partitioning divides a dataset into separate logical portions based on one or more columns, such as date, region, or department. When queries filter on the partition key, Amazon Athena can use partition pruning to avoid scanning unrelated partitions. This can reduce the amount of data scanned and improve query performance while lowering query costs. Replication creates additional copies of data, encryption protects information, and tokenization replaces sensitive values with tokens. Therefore, partitioning is the appropriate technique when a data engineer wants to organize large S3 datasets into logical groups and allow Athena to efficiently process only the relevant data.

Question 72

A data engineer wants to make a data pipeline resilient to temporary network or service failures. Which approach is recommended?

1. Use retry mechanisms with appropriate backoff
2. Disable all monitoring
3. Remove error handling
4. Store credentials in source code

Correct Answer: 1

Explanation:

Retry mechanisms with appropriate backoff can make data pipelines more resilient to temporary failures. Services can occasionally experience transient errors, throttling, or temporary network problems. Instead of immediately failing the entire pipeline, a workflow can retry an operation after waiting for a defined period. Exponential backoff can help avoid repeatedly overwhelming a service during an incident. Disabling monitoring reduces visibility, removing error handling decreases reliability, and storing credentials in source code creates security risks. Therefore, controlled retries with suitable backoff are an important reliability technique for handling temporary failures in data processing pipelines.

Question 73

A company wants to continuously replicate changes from a source database to a target database after an initial full data migration. Which AWS DMS capability should be used?

1. Static snapshots
2. Change data capture
3. Manual exports
4. S3 lifecycle policies

Correct Answer: 2

Explanation:

AWS Database Migration Service supports Change Data Capture, commonly known as CDC, which allows ongoing changes made to a source database to be captured and replicated to a target. This is useful after an initial full load because new inserts, updates, and deletes can continue to be transferred while the source system remains operational. Static snapshots do not continuously replicate changes, manual exports require repeated intervention, and S3 lifecycle policies manage object transitions rather than database replication. Therefore, Change Data Capture is the appropriate capability when a data engineer needs to keep the target database synchronized with changes occurring in the source.

Question 74

A data engineer needs to query a large dataset stored in Amazon S3 and wants to avoid managing any servers or database infrastructure. Which service is most appropriate?

1. Amazon EC2
2. Amazon RDS
3. Amazon Athena
4. Amazon Neptune

Correct Answer: 3

Explanation:

Amazon Athena is a serverless query service that allows users to analyze data stored directly in Amazon S3 using SQL. It does not require users to provision, configure, or maintain database servers or clusters. This makes Athena particularly useful for ad hoc analysis, data lake queries, and exploratory workloads. Amazon EC2 requires management of virtual compute instances, RDS provides managed relational databases, and Neptune is designed for graph databases. Therefore, Amazon Athena is the most appropriate service when a data engineer needs to query S3-based datasets using SQL without managing underlying server infrastructure.

Question 75

A company wants to identify unusual activity and potential threats in its AWS environment. Which AWS service is designed for threat detection?

1. Amazon GuardDuty
2. Amazon Athena
3. AWS Glue
4. Amazon QuickSight

Correct Answer: 1

Explanation:

Amazon GuardDuty is a managed threat detection service that continuously monitors supported AWS data sources and identifies suspicious or potentially malicious activity. It can help detect issues such as unusual API activity, compromised credentials, and certain network-based threats. Athena is used for querying data, Glue provides data integration and ETL capabilities, and QuickSight provides business intelligence and visualization. Therefore, Amazon GuardDuty is the appropriate service when an organization needs continuous monitoring for potential security threats within its AWS environment. GuardDuty findings can also be integrated with other security and response services for further investigation and automated remediation.

Question 76

A data engineer needs to store metadata about a dataset, including its schema and S3 location, and make that metadata available to Athena. Which AWS service should be used?

1. Amazon CloudWatch
2. AWS Glue Data Catalog
3. Amazon Kinesis Data Streams
4. AWS CloudTrail

Correct Answer: 2

Explanation:

AWS Glue Data Catalog stores metadata about datasets, including table definitions, column names, data types, partition information, and the physical location of data such as an Amazon S3 path. Amazon Athena can use this catalog metadata to interpret the underlying dataset and execute SQL queries. CloudWatch provides monitoring and logging, Kinesis Data Streams handles real-time streaming data, and CloudTrail records AWS API activity. Therefore, AWS Glue Data Catalog is the correct service for storing and sharing dataset metadata with Athena and other AWS analytics services.

Question 77

A company wants to automatically delete temporary files from an Amazon S3 bucket after a specific number of days. Which feature should be configured?

1. S3 Object Lock
2. S3 Lifecycle rule
3. S3 Transfer Acceleration
4. S3 Access Point

Correct Answer: 2

Explanation:

Amazon S3 Lifecycle rules can automatically transition or expire objects based on conditions such as object age. A lifecycle rule can be configured to delete temporary files after a specified number of days, helping organizations control storage usage and costs without manually deleting objects. S3 Object Lock is designed to prevent protected objects from being deleted or modified during retention periods. Transfer Acceleration improves data transfer speeds, while Access Points provide specialized access controls for S3 data. Therefore, an S3 Lifecycle rule is the appropriate feature when temporary objects need to be automatically deleted after a defined period.

Question 78

A data engineer needs to store data in Amazon S3 with high durability and frequent access for analytical processing. Which storage class is generally appropriate?

1. S3 Glacier Deep Archive
2. S3 Standard
3. S3 One Zone-IA
4. S3 Glacier Flexible Retrieval

Correct Answer: 2

Explanation:

S3 Standard is designed for frequently accessed data and provides high durability and availability. It is generally appropriate for datasets that are actively used by analytics applications, data processing pipelines, and other workloads requiring regular access. Glacier storage classes are optimized for archival data with less frequent retrieval, while S3 One Zone-IA is designed for infrequently accessed data stored in a single Availability Zone. Since the requirement specifies frequent access for analytical processing, S3 Standard is the most appropriate storage class. Storage class selection should always consider access frequency, retrieval requirements, durability, availability, and overall storage cost.

Question 79

A company wants to provide analysts with controlled access to specific tables and columns in a data lake. Which AWS service provides centralized fine-grained data lake permissions?

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

Correct Answer: 3

Explanation:

AWS Lake Formation provides centralized governance and access management capabilities for data lakes. It can help organizations define fine-grained permissions for data lake resources, including databases, tables, and columns. This allows organizations to provide analysts with access to only the data they are authorized to use while maintaining centralized control. CloudFront is a content delivery service, SQS provides message queuing, and ElastiCache provides in-memory caching. Therefore, AWS Lake Formation is the most appropriate service when a company needs centralized, fine-grained access control for data lake resources and wants to manage analyst permissions effectively.

Question 80

A data engineer wants to monitor the number of records processed by a data pipeline and trigger an alert when the value falls below an expected threshold. Which AWS service is appropriate?

1. AWS CloudTrail
2. AWS KMS
3. Amazon CloudWatch
4. Amazon Route 53

Correct Answer: 3

Explanation:

Amazon CloudWatch can collect metrics and logs from AWS resources and applications and can create alarms based on defined thresholds. A data pipeline can publish or expose a metric representing the number of processed records, and a CloudWatch alarm can trigger when the value falls below an expected level. CloudTrail records API activity, KMS manages encryption keys, and Route 53 provides DNS services. Therefore, CloudWatch is the appropriate service for monitoring pipeline metrics and triggering alerts when processing activity falls below a predefined threshold. This type of monitoring can help data engineers detect incomplete processing and investigate pipeline problems quickly.