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Amazon Data-Engineer-Associate Exam Vce: AWS Certified Data Engineer - Associate (DEA-C01) - ExamsLabs Easy to Pass

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q86-Q91):

NEW QUESTION # 86
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?

A. Store self-managed certificates on the EC2 instances.
B. Use AWS Certificate Manager (ACM).
C. Implement custom automation scripts in AWS Secrets Manager.
D. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.

Answer: B

Explanation:
The best solution for managing SSL/TLS certificates on EC2 instances with minimal operational overhead is to use AWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.
* AWS Certificate Manager (ACM):
* ACM manages SSL/TLS certificates for EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.
* ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.

NEW QUESTION # 87
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
B. Use Amazon Aurora PostgreSQL to load all the data into Aurora.
C. Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).
D. Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).

Answer: C

Explanation:
* Problem Analysis:
* The company requires acentralized data warehousefor consolidating data from various sources.
* They useAmazon QuickSight in direct query mode, necessitatingfast response timesfor analytical queries.
* Users query the data intermittently, withunpredictable spikesduring the day.
* Operational overhead should be minimal.
* Key Considerations:
* The solution must support fast, SQL-based analytics.
* It must handle unpredictable spikes efficiently.
* Must integrate seamlessly withQuickSightfor direct querying.
* Minimize operational complexity and scaling concerns.
* Solution Analysis:
* Option A: Amazon Redshift Serverless
* Redshift Serverless eliminates the need for provisioning and managing clusters.
* Automatically scales compute capacity up or down based on query demand.
* Reduces operational overhead by handling performance optimization.
* Fully integrates withAmazon QuickSight, ensuring low-latency analytics.
* Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.
* Option B: Amazon Athena with S3 (Apache Parquet)
* Athena supports querying data directly from S3 in Parquet format.
* While it's cost-effective, performance depends on the size and complexity of the data.
* It is not optimized for high-speed analytics needed by QuickSight in direct query mode.
* Option C: Amazon Redshift Provisioned Clusters
* Requires manual cluster provisioning, scaling, and maintenance.
* Higher operational overhead compared to Redshift Serverless.
* Option D: Amazon Aurora PostgreSQL
* Aurora is optimized for transactional databases, not data warehousing or analytics.
* Does not meet the requirement for fast analytics queries.
* Final Recommendation:
* Amazon Redshift Serverlessis the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.
:
Amazon Redshift Serverless Overview
Amazon QuickSight and Redshift Integration
Athena vs. Redshift

NEW QUESTION # 88
A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS CloudFormation templates to automatically process the analytics workload.
B. Use the AWS CLI to automatically process the analytics workload.
C. Use Amazon Redshift Serverless to automatically process the analytics workload.
D. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.

Answer: C

Explanation:
Amazon Redshift Serverless is a new feature of Amazon Redshift that enables you to run SQL queries on data in Amazon S3 without provisioning or managing any clusters. You can use Amazon Redshift Serverless to automatically process the analytics workload, as it scales up and down the compute resources based on the query demand, and charges you only for the resources consumed. This solution will meet the requirements with the least operational overhead, as it does not require the data engineer to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI, AWS SDK, or AWS Lambda functions12.
The other options are not optimal for the following reasons:
* A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month. This option is not recommended, as it would still require the data engineer to create and delete a new Redshift provisioned cluster every month, which can incur additional costs and time. Moreover, this option would require the data engineer to use Amazon Step Functions to orchestrate the workflow of pausing and resuming the cluster, which can add complexity and overhead.
* C. Use the AWS CLI to automatically process the analytics workload. This option is vague and does not specify how the AWS CLI is used to process the analytics workload. The AWS CLI can be used to run queries on data in Amazon S3 using Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these services has different features and benefits. Moreover, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to provision and configure some resources, such as Amazon EMR clusters or Amazon Athena workgroups.
* D. Use AWS CloudFormation templates to automatically process the analytics workload. This option is also vague and does not specify how AWS CloudFormation templates are used to process the analytics workload. AWS CloudFormation is a service that lets you model and provision AWS resources using templates. You can use AWS CloudFormation templates to create and delete a Redshift provisioned cluster every month, or to create and configure other AWS resources, such as Amazon EMR, Amazon Athena, or Amazon Redshift Serverless. However, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to write and maintain the AWS CloudFormation templates, and to monitor the status and performance of the resources.
1: Amazon Redshift Serverless
2: Amazon Redshift Data API
3: Amazon Step Functions
4: AWS CLI
5: AWS CloudFormation

NEW QUESTION # 89
A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.
B. Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.
C. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.
D. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

Answer: C

NEW QUESTION # 90
A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
B. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
C. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

Answer: A

Explanation:
This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. Reference:
Amazon Redshift streaming ingestion
Creating an external schema for Amazon Kinesis Data Streams
Creating a materialized view for Amazon Kinesis Data Streams

NEW QUESTION # 91
......

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