DEA-C01試験無料問題集「Snowflake SnowPro Advanced: Data Engineer Certification 認定」

A. One row to one column

B. Only one column to one row

C. Multiple rows into multiple columns

D. Multiple columns into multiple rows

A. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS '<account ID 1>' AND '<account ID 2>';

B. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES '<account ID 1>' AND
'<account ID 2>';

C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 2>';

D. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';

A. Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.

B. Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.

C. Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

D. Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.

A. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

B. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view.
Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

D. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.

A. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.

B. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.

C. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.

D. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.

A. Add the S3 bucket to an AWS Glue Data Catalog. Configure Amazon Redshift Spectrum to access the Data Catalog.

B. Enable the Amazon Redshift data sharing feature. Set the S3 bucket as a target bucket for data sharing.

C. Store login credentials for Amazon Redshift in AWS Secrets Manager. Add a reference to the secret to the Glue job configuration.

D. Create an IAM role that has permission to write to the S3 bucket. Associate the IAM role with the Amazon Redshift cluster.

A. Use Amazon S3 analytics to query the clickstream data.

B. Use Amazon Redshift to store and query the clickstream data.

C. Access the query data through a QuickSight direct SQL query.

D. Access the query data through QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine). Configure a daily refresh for the dataset.

E. Use Amazon Athena to query the clickstream data

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B. Amazon Kinesis

C. AWS Glue Data Catalog

D. Amazon AppFlow

A. Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.

B. Use AWS Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.

C. Create materialized views and enable Amazon Redshift datashares for each domain. Configure cross-domain access policies.

D. Use AWS Glue Data Catalog views to perform analysis. Use AWS CloudTrail logs to audit data access. Use AWS Lake Formation to manage access permissions. Use security definer views to mask PII.

A. Create an external schema that maps to the MSK cluster. Create a materialized view that references the external schema to consume the streaming data from the MSK topic.

B. Develop an AWS Glue streaming extract, transform, and load (ETL) job to process the incoming data from Amazon MSK. Load the data into Amazon S3. Use Amazon Redshift Spectrum to read the data from Amazon S3.

C. Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.

D. Create an Amazon S3 bucket. Ingest the data from Amazon MSK. Create an event-driven AWS Lambda function to load the data from the S3 bucket to a new Amazon Redshift table.