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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q259-Q264):

NEW QUESTION # 259
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB of sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.
  • B. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.
  • C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets.
    Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.
  • D. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.

Answer: D

Explanation:
This solution meets the requirements with the least operational overhead by utilizing managed AWS services that simplify the data ingestion, transformation, and orchestration processes:
Amazon AppFlow is a fully managed integration service that allows data ingestion from Salesforce without custom connectors or manual ETL processes.
AWS Glue provides serverless data integration, making it suitable for extracting data from the MySQL database and transforming it as needed.
AWS Step Functions can then be used to orchestrate and automate the nightly process, minimizing the need for complex management.


NEW QUESTION # 260
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 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.
  • C. 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.
  • 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.

Answer: B


NEW QUESTION # 261
A company maintains a central Amazon Redshift data warehouse that aggregates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer notices that some complex transformation queries take hours to finish. The data engineer wants to optimize query performance to reduce query execution time as much as possible.
Which solution will meet this requirement?

  • A. Use Amazon Redshift Spectrum to create external tables based on the Redshift tables.
  • B. Use materialized views in Amazon Redshift for frequently queried data patterns.
  • C. Increase the concurrency scaling quota for the Redshift cluster.
  • D. Export the tables to an Amazon S3 bucket. Use Amazon Athena to query the data in the bucket.

Answer: B

Explanation:
Amazon Redshift materialized views precompute and store results of complex queries, allowing subsequent queries to run much faster by reading from the precomputed data. This significantly reduces execution time for frequently used transformation patterns.


NEW QUESTION # 262
A data engineer must orchestrate a series of Amazon Athena queries that will run every day.
Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

  • A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
  • B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
  • C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
  • D. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
  • E. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.

Answer: A,B


NEW QUESTION # 263
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge. All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?

  • A. Use Quick Suite dashboards that have federated query access to Amazon Redshift.
  • B. Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.
  • C. Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
  • D. Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.

Answer: B

Explanation:
Amazon Q in QuickSight enables users to create dashboards and reports using natural language queries, removing the need for SQL knowledge. It is fully integrated and managed within QuickSight, providing the most operationally efficient way for analysts of varying skill levels to generate insights quickly without additional tooling or infrastructure.


NEW QUESTION # 264
......

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