最新なAmazon Data-Engineer-Associate問題集（305題）、真実試験の問題を全部にカバー！

質問 1：
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company ' s application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?
A. Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.
B. Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.
C. Design the data source so events are not ingested into Kinesis Data Streams multiple times.
D. Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?
A. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
B. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
C. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
D. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importing the Pandas library to perform data deduplication.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.)
A. Delete and recreate the problematic Athena table.
B. Restart Athena.
C. Confirm that Athena is pointing to the correct Amazon S3 location.
D. Increase the query timeout duration.
E. Use the MSCK REPAIR TABLE command.
正解：C,E
解説: (Pass4Test メンバーにのみ表示されます)

質問 4：
A company needs to use an AWS Glue PySpark job to read specific data from an Amazon DynamoDB table.
The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data.
Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?
A. Perform a query on the DynamoDB table in the AWS Glue job by using only the sort key in the key condition expression. Load the data into a DynamicFrame.
B. Perform a query on the DynamoDB table in the AWS Glue job. Use the partition key in the key condition expression. Put the data into a DynamicFrame.
C. Use the AWS Glue DynamoDB ETL connector to read the DynamoDB table. Use the filter option to read the required partition key.
D. Perform a scan on the DynamoDB table in the AWS Glue job. Put the data into a DynamicFrame. Filter the DynamicFrame on the partition key.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
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 Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
D. Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable information (PII) about customers within the data.
The company ' s privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?
A. Implement custom data quality rules in Data Brew. Apply the custom rules across datasets.
B. Manually review the data for custom PII categories.
C. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.
D. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 7：
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?
A. Use AWS Glue DataBrew to read the files. Use the NEST TO MAP transformation to create the new column.
B. Use AWS Glue DataBrew to read the files. Use the NEST TO ARRAY transformation to create the new column.
C. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
D. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 8：
A data engineer needs to make tabular data available in an Amazon S3-based data lake. Users must be able to query the data by using SQL queries in Amazon Redshift, Amazon Athena, and Amazon EMR. The data is updated daily. The data engineer must ensure that updates and deletions are reflected in the data lake.
Which solution will meet these requirements with the LEAST operational overhead?
A. Store the data in S3 Standard. Configure Apache Hudi with merge-on-read in Amazon EMR. Use Apache Spark SQL in Amazon EMR to perform the daily updates and deletions. Use Amazon EMR to schedule compaction jobs. Use AWS Glue to create a data catalog of Hudi tables that are stored in Amazon S3.
B. Load the data into an Amazon EMR cluster. Use Apache Spark to perform the daily updates and deletions. Upload the data into an Amazon S3 bucket in Apache Parquet format to create the data lake.
C. Load the data into an Amazon Redshift cluster. Use SQL to perform the daily updates and deletions.
Upload the data to an Amazon S3 bucket in Apache Parquet format to create the data lake.
D. Create S3 tables for the tabular data. Use AWS Glue and an S3 tables catalog for Apache Iceberg JAR to perform the daily updates and deletions. Configure a compaction size target. Set up snapshot management and unreferenced file removal for the S3 tables bucket.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)