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Amazon AWS Certified Data Analytics – Specialty (DAS-C01) Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3 Each day the data is stored in an individual csv file in an S3 bucket This is an example of the naming structure
20210707_datacsv 20210708_datacsv
To allow for data querying in QuickSight through Amazon Athena the specialist used an AWS Glue crawler to create a table with the path “s3 //powertransformer/20210707_data csv” However when the data is queried, it returns zero rows How can this issue be resolved?

  • A. Modify the IAM policy for the AWS Glue crawler to access Amazon S3.
  • B. Update the table path to “s3://powertransformer/”.
  • C. Store the files in Apache Parquet format.
  • D. Ingest the files again.

Answer: B

NEW QUESTION # 32
A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

  • A. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.
  • B. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key.
    Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.
  • C. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
  • D. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

Answer: C

NEW QUESTION # 33
A company uses Amazon Redshift as its data warehouse A new table includes some columns that contain sensitive data and some columns that contain non-sensitive data The data in the table eventually will be referenced by several existing queries that run many times each day A data analytics specialist must ensure that only members of the company’s auditing team can read the columns that contain sensitive data All other users must have read-only access to the columns that contain non-sensitive data Which solution will meet these requirements with the LEAST operational overhead?

  • A. Grant the auditing team permission to read from the table. Load the columns that contain non-sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.
  • B. Grant all users read-only permissions to the columns that contain non-sensitive data Use the GRANT SELECT command to allow the auditing team to access the columns that contain sensitive data
  • C. Grant the auditing team permission to read from the table Create a view of the table that includes the columns that contain non-sensitive data Grant the appropriate users read-only permissions to that view
  • D. Grant all users read-only permissions to the columns that contain non-sensitive data Attach an 1AM policy to the auditing team with an explicit Allow action that grants access to the columns that contain sensitive data

Answer: B

Explanation:
https://aws.amazon.com/jp/about-aws/whats-new/2020/03/announcing-column-level-access-control-for-amazon-redshift/

NEW QUESTION # 34
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection.
Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

  • A. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
  • B. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.
  • C. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
  • D. Query all the datasets in place with Apache Presto running on Amazon EMR.

Answer: C

NEW QUESTION # 35
A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most cost-effective solution.
Which approach meets these requirements for optimizing and querying the log data?

  • A. Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and day. Use Apache Presto to query the optimized format.
  • B. Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
  • C. Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query
  • D. Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and day. Use Amazon Redshift Spectrum to query the data.

Answer: D

Explanation:
data.

NEW QUESTION # 36
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