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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q117-Q122):

117. Frage
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

A. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON.Use Kinesis Data Firehose to store the files in Parquet format.
B. Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
C. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
D. Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.

Antwort: D

Begründung:
The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.
* Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3.
This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.
Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose's native format conversion capabilities.
References:
* Amazon Kinesis Data Firehose Documentation

118. Frage
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?

A. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
B. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.
C. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files.
Load the files into the Amazon Redshift tables.
D. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

Antwort: C

Begründung:
* Problem Analysis:
* Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.
* Processing time is increasing due to the additional testing facilities.
* The goal is toreduce processing timewhile using the existing AWS Glue framework.
* Key Considerations:
* AWS Glue offers thedynamic frame file-groupingfeature, which consolidates small files into larger, more efficient datasets during processing.
* Grouping smaller files reduces overhead and speeds up processing.
* Solution Analysis:
* Option A: Lambda for File Grouping
* Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.
* Option B: AWS Glue Dynamic Frame File-Grouping
* This option directly addresses the issue by grouping small files during Glue job execution.
* Minimizes data processing time with no extra overhead.
* Option C: Redshift COPY Command
* COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).
* Option D: Amazon EMR
* While EMR is powerful, replacing Glue with EMR increases operational complexity.
* Final Recommendation:
* UseAWS Glue dynamic frame file-groupingfor optimized data ingestion and processing.
:
AWS Glue Dynamic Frames
Optimizing Glue Performance

119. Frage
A company manages an Amazon Redshift data warehouse. The data warehouse is in a public subnet inside a custom VPC A security group allows only traffic from within itself- An ACL is open to all traffic.
The company wants to generate several visualizations in Amazon QuickSight for an upcoming sales event.
The company will run QuickSight Enterprise edition in a second AW5 account inside a public subnet within a second custom VPC. The new public subnet has a security group that allows outbound traffic to the existing Redshift cluster.
A data engineer needs to establish connections between Amazon Redshift and QuickSight. QuickSight must refresh dashboards by querying the Redshift cluster.
Which solution will meet these requirements?

A. Assign Elastic IP addresses to the QuickSight visualizations. Configure the QuickSight security group to allow inbound traffic on the Redshift port from the Elastic IP addresses.
B. Confirm that the CIDR ranges of the Redshift VPC and the QuickSight VPC are the same. If CIDR ranges are different, reconfigure one CIDR range to match the other. Establish network peering between the VPCs.
C. Create a QuickSight gateway endpoint in the Redshift VPC. Attach an endpoint policy to the gateway endpoint to ensure only specific QuickSight accounts can use the endpoint.
D. Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.

Antwort: D

120. Frage
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
B. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
D. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

Antwort: B

Begründung:
Option A is the most operationally efficient way to meet the requirements because it minimizes the number of steps and services involved in the data export process. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including Amazon S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. By creating a view in the SQL Server databases that contains the required data elements, the AWS Glue job can select the data directly from the view without having to perform any joins or transformations on the source data. The AWS Glue job can then transfer the data in Parquet format to an S3 bucket and run on a daily schedule.
Option B is not operationally efficient because it involves multiple steps and services to export the data. SQL Server Agent is a tool that can run scheduled tasks on SQL Server databases, such as executing SQL queries. However, SQL Server Agent cannot directly export data to S3, so the query output must be saved as .csv objects on the EC2 instance. Then, an S3 event must be configured to trigger an AWS Lambda function that can transform the .csv objects to Parquet format and upload them to S3. This option adds complexity and latency to the data export process and requires additional resources and configuration.
Option C is not operationally efficient because it introduces an unnecessary step of running an AWS Glue crawler to read the view. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. However, in this scenario, the schema and format of the data elements are already known and fixed, so there is no need to run a crawler to discover them. The AWS Glue job can directly select the data from the view without using the Data Catalog. Running a crawler adds extra time and cost to the data export process.
Option D is not operationally efficient because it requires custom code and configuration to query the databases and transform the data. An AWS Lambda function is a service that can run code in response to events or triggers, such as Amazon EventBridge. Amazon EventBridge is a service that can connect applications and services with event sources, such as schedules, and route them to targets, such as Lambda functions. However, in this scenario, using a Lambda function to query the databases and transform the data is not the best option because it requires writing and maintaining code that uses JDBC to connect to the SQL Server databases, retrieve the required data, convert the data to Parquet format, and transfer the data to S3. This option also has limitations on the execution time, memory, and concurrency of the Lambda function, which may affect the performance and reliability of the data export process.
Reference:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
AWS Glue Documentation
Working with Views in AWS Glue
Converting to Columnar Formats

121. Frage
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

A. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
B. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.
C. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.
D. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Antwort: B

Begründung:
The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provide object-level tracking for S3 to capture detailed API actions on specific S3 objects, including the user who performed the action.
AWS CloudTrail:
CloudTrail can monitor API calls made to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.
CloudTrail logs include details such as the user identity, which is essential for meeting the requirement of including the username in notifications.
The CloudTrail logs can be forwarded to Amazon CloudWatch to trigger alarms based on certain access patterns (e.g., violations of specific policies).
Reference:
Amazon CloudWatch:
By forwarding CloudTrail logs to CloudWatch, you can set up alarms that are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.
Alternatives Considered:
A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.
B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.
D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API-level granularity like CloudTrail.
AWS CloudTrail Integration with S3
Amazon CloudWatch Alarms

122. Frage
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Data-Engineer-Associate Prüfungsguide: AWS Certified Data Engineer - Associate (DEA-C01) & Data-Engineer-Associate echter Test & Data-Engineer-Associate sicherlich-zu-bestehen

Falls Sie in der Amazon Data-Engineer-Associate Zertifizierungsprüfung durchfallen, zahlen wir Ihnen die gesammte Summe zurück, Unsere Fragen sind umfassend und der Preis ist rational.

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Hohe Qualität von Data-Engineer-Associate Prüfung und Antworten

Data-Engineer-Associate Studienmaterialien: AWS Certified Data Engineer - Associate (DEA-C01) - Data-Engineer-Associate Torrent Prüfung & Data-Engineer-Associate wirkliche Prüfung

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q117-Q122):

Data-Engineer-Associate Prüfungsguide: AWS Certified Data Engineer - Associate (DEA-C01) & Data-Engineer-Associate echter Test & Data-Engineer-Associate sicherlich-zu-bestehen

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