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最新的 AWS Certified Data Engineer Data-Engineer-Associate 免費考試真題:

1. A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A) Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
B) Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
C) Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
D) Use the Amazon Redshift Data API to publish an event to Amazon EventBridqe. Configure an EventBridge rule to invoke the Lambda function.

2. A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

A) Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
B) Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.
C) Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
D) Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.

3. A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)

A) Use SQL to invoke a remote SageMaker endpoint for prediction.
B) Use Amazon Redshift as a file storage system to archive old inventory management reports.
C) Use Amazon Redshift ML to generate inventory recommendations.
D) Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
E) Use Amazon Redshift ML to schedule regular data exports for offline model training.

4. A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

A) Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
B) Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
C) Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables.
Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
D) Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
E) Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

5. A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

A) Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
B) Create an Amazon EventBridge rule to match AWS Glue job failure events. Configure the rule to target an AWS Lambda function to process events. Configure the function to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
C) Create an Amazon EventBridge rule to match AWS Glue job failure events. Define an Amazon CloudWatch metric based on the EventBridge rule. Set up a CloudWatch alarm based on the metric to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
D) Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to target an AWS Lambda function that reads the logs and sends notifications to an Amazon Simple Notification Service (Amazon SNS) topic if AWS Glue job failure logs are present.

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