Free Snowflake DEA-C01 Exam Dumps Questions & Answers

A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads. Which solution will meet these requirements with the LEAST operational overhead?

• A.  Use Amazon SageMaker Processing jobs to run PySpark code that uses native SageMaker libraries.
• B.  Use AWS Glue Studio to build and schedule PySpark jobs. Configure an AWS Glue data connection that includes the custom libraries.
• C.  Use Amazon EMR to run PySpark jobs. Use bootstrap actions to install the custom libraries
• D.  Use Amazon EC2 Auto Scaling groups with a custom AMI that contains the custom libraries to run a PySpark application.

A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:

The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?

• A.  Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
• B.  Create a filter expression that uses RiderID and RideStatus.
• C.  Create a global secondary index (GSI) that uses DriverID as the partition key and RideStatus as the sort key.
• D.  Create a local secondary index (LSI) on DriverID.

A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.

• A.  Configure the column.push_down_predicate setting and the catalogPartitionPredicate settings for the postal code column in the DynamicFrame.
• B.  Use AWS Glue workflow properties to allow job state sharing. Configure the AWS Glue jobs to read values from the postal code column by using the properties from a previously successful run of the jobs.
• C.  Set the DynamicFrame additional_options parameter `useS3ListImplementation' to True.
• D.  Create a schema definition for PySpark that matches the format the processing workflow requires for postal codes. Pass the schema to the DynamicFrame during processing.

A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
Which solution will meet this requirement with the LEAST manual intervention?

• A.  Create a table in AWS Glue Data Catalog. Write custom SQL queries to identify PII in the table.Use Amazon Athena to run the queries.
• B.  Create an AWS Lambda function to identify PII in S3 objects. Schedule the function to run periodically.
• C.  Configure AWS CloudTrail to monitor S3 PUT operations. Inspect the CloudTrail trails to identify operations that save PII.
• D.  Configure the S3 bucket and S3 objects to allow access to Amazon Macie. Use automated sensitive data discovery in Macie.

A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline.
The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow. Which solutions will meet these requirements? (Choose two.)

• A.  Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
• B.  Provision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined schedule
• C.  Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
• D.  Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
• E.  Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.