Snowflake SnowPro Advanced: Data Engineer Certification : DEA-C01

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Snowflake DEA-C01 考試大綱：

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

最新的 SnowPro Advanced DEA-C01 免費考試真題：

問題 #1
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

A. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
B. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
C. Establish WebSocket connections to Amazon Redshift.
D. Use the Amazon Redshift Data API.

問題 #2
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation. Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to
15%.
B. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
C. Deploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.
D. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.

問題 #3
A company is creating near real-time dashboards to visualize time series data. The company ingests data into Amazon Managed Streaming for Apache Kafka (Amazon MSK). A customized data pipeline consumes the data. The pipeline then writes data to Amazon Keyspaces (for Apache Cassandra), Amazon OpenSearch Service, and Apache Avro objects in Amazon S3.
Which solution will make the data available for the data visualizations with the LEAST latency?

A. Use AWS Glue to catalog the data. Use S3 Select to query the Avro objects in Amazon S3.Connect Amazon QuickSight to the S3 bucket to create the dashboards.
B. Use Amazon Athena to query the data from the Avro objects in Amazon S3. Configure Amazon Keyspaces as the data catalog. Connect Amazon QuickSight to Athena to create the dashboards.
C. Create OpenSearch Dashboards by using the data from OpenSearch Service.
D. Use Amazon Athena with an Apache Hive metastore to query the Avro objects in Amazon S3.
Use Amazon Managed Grafana to connect to Athena and to create the dashboards.

問題 #4
A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company's data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region.
A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.
Which solution will meet these requirements with the LEAST effort?

A. Configure each of the Regional EFS file systems to replicate data to the data science team's Region. In the data science team's Region, configure the Lambda functions to mount the replica file systems. Use the Lambda functions to process the data.
B. Peer the VPCs that host the EFS file systems in each Region with the VPC that is in the data science team's Region. Enable EFS file locking.
Configure the Lambda functions in the data science team's Region to mount each of the Region specific file systems. Use the Lambda functions to process the data.
C. Use AWS DataSync to transfer files from each of the Regional EFS files systems to the file system that is in the data science team's Region.Configure the Lambda functions in the data science team's Region to mount the file system that is in the same Region. Use the Lambda functions to process the data.
D. Deploy the Lambda functions to each Region. Mount the Regional EFS file systems to the Lambda functions. Use the Lambda functions to process the data. Store the output in an Amazon S3 bucket in the data science team's Region.

問題 #5
A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.
The company wants to transform the data to optimize query runtime and storage costs.
Which file format and compression solution will meet these requirements for Athena queries?

A. Apache Parquet format compressed with Snappy
B. Apache Avro format compressed with LZO
C. .csv format compressed with zip
D. JSON format compressed with bzip2

問題與答案：