Snowflake SnowPro Advanced DEA-C01

Snowflake DEA-C01 認定試験の出題範囲：

参照：https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake SnowPro Advanced: Data Engineer Certification 認定 DEA-C01 試験問題：

問題 #1
A hotel management company receives daily data files from each of its hotels. The company wants to upload its data to AWS. The company plans to use Amazon Athena to access the files.
The company needs to protect the files from accidental deletion. The company will develop an application on its on-premises servers to automatically forward the files to a fully managed AWS ingestion service.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS DataSync to replicate data from the on-premises servers to Amazon Elastic File System (Amazon EFS). Configure automatic backups in AWS Backup.
B. Use a self-managed Apache Kafka agent on the on-premises servers to stream data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Store the data in an Amazon S3 bucket with versioning enabled.
C. Use AWS Glue jobs to ingest data from the on-premises servers into Amazon RDS. Enable automated backups for data protection.
D. Use the Amazon Kinesis Agent on the on-premises servers to send data to Amazon Data Firehose. Store the data in an Amazon S3 bucket that has versioning enabled.

問題 #2
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?

A. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.
B. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
C. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.

問題 #3
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

A. Use Amazon S3 as a persistent data store.
B. Use Hadoop Distributed File System (HDFS) as a persistent data store.
C. Use x86-based instances for core nodes and task nodes.
D. Use Graviton instances for core nodes and task nodes.
E. Use Spot Instances for all primary nodes.

問題 #4
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

A. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access.Restrict access based on Region.
B. Modify the IAM roles of the HR departments to add a data filter for each department's Region.
C. Use data filters for each Region to register the S3 paths as data locations.
D. Register the S3 path as an AWS Lake Formation location.
E. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.

問題 #5
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 Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).
B. Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
C. Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).
D. Use Amazon Aurora PostgreSQL to load all the data into Aurora.

解説：