Free Amazon DAS-C01 Exam Questions

Question 1

A manufacturing company is storing data from its operational systems in Amazon S3. The company's business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athena. The company needs to access the Athena service from the on-premises network by using a JDBC connection. The company has created a VPC. Security policies mandate that requests to AWS services cannot traverse the internet. Which combination of steps should a data analytics specialist take to meet these requirements? (Select TWO.)

A : Establish an AWS Direct Connect connection between the on-premises network and the VPC.

B : Configure the JDBC connection to connect to Athena through Amazon API Gateway.

C : Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.

D : Configure the JDBC connection to use an interface VPC endpoint for Athena.

E : Deploy Athena within a private subnet.

Answer: A,D

Question 2

A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster. A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort. Which combination of steps will create a solution that meets these requirements? (Select THREE.)

A : Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.

B : Use an AWS Glue crawler to catalog the data.

C : Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.

D : Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.

E : Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.

F : Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

Answer: B,D,F

Question 3

A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their dat
a. The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department.
Which two steps are required for this process? (Choose two.)

A : finance department grants Lake Formation permissions for the tables to the external account for the marketing department.

B : finance department creates cross-account IAM permissions to the table for the marketing department role.

C : marketing department creates an IAM role that has permissions to the Lake Formation tables.

Answer: A,B

Question 4

A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney. Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.

What should the data analyst do to ensure the appropriate data security is in place?

A : ace the data sources for Australia and the US into separate SPICE capacity pools.

B : t up an Amazon Redshift VPC security group for Australia and the US.

C : ploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

D : ploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

Answer: D

Question 5

A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?

A : Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.

B : Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a Kinesis Agent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.

C : Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.

D : Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Answer: B