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DAS-C01 Exam Dumps - AWS Certified Data Analytics - Specialty

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Question # 41

An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.

A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.

How should the data analyst meet this requirement while minimizing costs?

Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.

Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the

.csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the fileâ€™s S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.

Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.

Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.

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Question # 42

A company uses an Amazon EMR cluster with 50 nodes to process operational data and make the data available for data analysts These jobs run nightly use Apache Hive with the Apache Jez framework as a processing model and write results to Hadoop Distributed File System (HDFS) In the last few weeks, jobs are failing and are producing the following error message

"File could only be replicated to 0 nodes instead of 1"

A data analytics specialist checks the DataNode logs the NameNode logs and network connectivity for potential issues that could have prevented HDFS from replicating data The data analytics specialist rules out these factors as causes for the issue

Which solution will prevent the jobs from failing'?

Monitor the HDFSUtilization metric. If the value crosses a user-defined threshold add task nodes to the EMR cluster

Monitor the HDFSUtilization metri.c If the value crosses a user-defined threshold add core nodes to the EMR cluster

Monitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold, add task nodes to the EMR cluster

Monitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold, add core nodes to the EMR cluster.

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Question # 43

An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.

Which options will help meet these requirements in the MOST efficient way? (Choose two.)

Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.

Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.

Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.

Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.

Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.

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Question # 44

A large energy company is using Amazon QuickSight to build dashboards and report the historical usage data of its customers This data is hosted in Amazon Redshift The reports need access to all the fact tables' billions ot records to create aggregation in real time grouping by multiple dimensions

A data analyst created the dataset in QuickSight by using a SQL query and not SPICE Business users have noted that the response time is not fast enough to meet their needs

Which action would speed up the response time for the reports with the LEAST implementation effort?

Use QuickSight to modify the current dataset to use SPICE

Use AWS Glue to create an Apache Spark job that joins the fact table with the dimensions. Load the data into a new table

Use Amazon Redshift to create a materialized view that joins the fact table with the dimensions

Use Amazon Redshift to create a stored procedure that joins the fact table with the dimensions Load the data into a new table

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Question # 45

A financial company uses Amazon Athena to query data from an Amazon S3 data lake. Files are stored in the S3 data lake in Apache ORC format. Data analysts recently introduced nested fields in the data lake ORC files, and noticed that queries are taking longer to run in Athena. A data analysts discovered that more data than what is required is being scanned for the queries.

What is the MOST operationally efficient solution to improve query performance?

Flatten nested data and create separate files for each nested dataset.

Use the Athena query engine V2 and push the query filter to the source ORC file.

Use Apache Parquet format instead of ORC format.

Recreate the data partition strategy and further narrow down the data filter criteria.

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Question # 46

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync

Which solution will simplify permissions management with minimal development effort?

Set AWS Identity and Access Management (1AM) permissions tor AWS Glue

Use AWS Lake Formation permissions

Manage AWS Glue and S3 permissions by using bucket policies

Use Amazon Cognito user pools.

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Question # 47

A companyâ€™s data analyst needs to ensure that queries executed in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately.

What should the data analyst do to achieve this?

Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.

For each workgroup, set the control limit for each query to the prescribed threshold.

Enforce the prescribed threshold on all Amazon S3 bucket policies

For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.

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Question # 48

A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.

How should the data analyst resolve the issue?

Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.

Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.

Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.

Edit the permissions for the new S3 bucket from within the S3 console.

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