DAS-C01 Online Practice Questions and Answers

A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.

The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.

Which solution will solve this issue and meet the requirements?

A. In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in apnortheast-1.

B. Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.

C. Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.

D. Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.

1.

A trips fact table for information on completed rides.

2.

A drivers dimension table for driver profiles.

3.

A customers fact table holding customer profile information.

The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.

What table design provides optimal query performance?

A. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.

B. Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.

C. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.

D. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-realtime data.

Which visualization solution will meet these requirements?

A. Select Amazon OpenSearch Service (Amazon Elasticsearch Service) as the endpoint for Kinesis Data Firehose. Set up an OpenSearch Dashboards (Kibana) using the data in Amazon OpenSearch Service (Amazon ES) with the desired analyses and visualizations.

B. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.

C. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.

D. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.

A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.

Which action would MOST likely increase the performance of accessing log data in Amazon S3?

A. Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.

B. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.

C. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.

D. Redeploy the EMR clusters that are running slowly to a different Availability Zone.

A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.

Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners' dashboards?

A. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.

B. Create and schedule an AWS Glue Spark job to run every 5 minutes. The job inserts reference data into Amazon Redshift.

C. Send reference data to Amazon Kinesis Data Streams. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.

D. Send the reference data to an Amazon Kinesis Data Firehose delivery stream. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis Data Streams to collect the data streams from smart meters. The consumer application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.

The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.

Which solution meets these requirements?

A. Use enhanced fan-out in Kinesis Data Streams.

B. Increase the number of shards for the Kinesis data stream.

C. Reduce the propagation delay by overriding the KCL default settings.

D. Develop consumers by using Amazon Kinesis Data Firehose.

A company is providing analytics services to its sales and marketing departments. The departments can access the data only through their business intelligence (BI) tools, which run queries on Amazon Redshift using an Amazon Redshift internal user to connect. Each department is assigned a user in the Amazon Redshift database with the permissions needed for that department. The marketing data analysts must be granted direct access to the advertising table, which is stored in Apache Parquet format in the marketing S3 bucket of the company data lake. The company data lake is managed by AWS Lake Formation. Finally, access must be limited to the three promotion columns in the table.

Which combination of steps will meet these requirements? (Choose three.)

A. Grant permissions in Amazon Redshift to allow the marketing Amazon Redshift user to access the three promotion columns of the advertising external table.

B. Create an Amazon Redshift Spectrum IAM role with permissions for Lake Formation. Attach it to the Amazon Redshift cluster.

C. Create an Amazon Redshift Spectrum IAM role with permissions for the marketing S3 bucket. Attach it to the Amazon Redshift cluster.

D. Create an external schema in Amazon Redshift by using the Amazon Redshift Spectrum IAM role. Grant usage to the marketing Amazon Redshift user.

E. Grant permissions in Lake Formation to allow the Amazon Redshift Spectrum role to access the three promotion columns of the advertising table.

F. Grant permissions in Lake Formation to allow the marketing IAM group to access the three promotion columns of the advertising table.

A company has 10-15 of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine. The company wants to transform the data to optimize query runtime and storage costs. Which option for data format and compression meets these requirements?

A. CSV compressed with zip

B. JSON compressed with bzip2

C. Apache Parquet compressed with Snappy

D. Apache Avro compressed with LZO

A data analytics specialist has a 50 GB data file in .csv format and wants to perform a data transformation task. The data analytics specialist is using the Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to perform the transformation. The resulting output will be used to query the data from Amazon Redshift Spectrum.

Which CTAS statement should the data analytics specialist use to provide the MOST efficient performance?

A. CREATE TABLE new_Table

WITH (

format = 'TEXTFILE',

orc_compression = 'SNAPPY')

AS SELECT *

FROM old_table;

B. CREATE TABLE new_Table

WITH (

format = 'TEXTFILE',

)

AS SELECT *

FROM old_table;

C. CREATE TABLE new_Table

WITH (

format = 'PARQUET',

parquet_compression = 'SNAPPY')

AS SELECT *

FROM old_table;

D. CREATE TABLE new_Table

WITH (

format = JSON,

)

AS SELECT *

FROM old_table;

A large digital advertising company has built business intelligence (BI) dashboards in Amazon QuickSight Enterprise edition to understand customer buying behavior. The dashboards use the Super-fast, Parallel, In-memory Calculation Engine (SPICE) as the in-memory engine to store the data. The company's Amazon S3 data lake provides the data for these dashboards, which are queried using Amazon Athena. The data files used by the dashboards consist of millions of records partitioned by year, month, and hour, and new data is continuously added. Every data file in the data lake has a timestamp column named CREATE_TS, which indicates when the data was added or updated.

Until now, the dashboards have been scheduled to refresh every night through a full reload. A data analyst must recommend an approach so the dashboards can be refreshed every hour, and include incremental data from the last hour.

How can the data analyst meet these requirements with the LEAST amount of operational effort?

A. Create new data partitions every hour in Athena by using the CREATE_TS column and schedule the QuickSight dataset to refresh every hour.

B. Use direct querying in QuickSight by using Athena to make refreshed data always available.

C. Use the CREATE_TS column to look back for incremental data in the last hour and schedule the QuickSight dataset to incrementally refresh every hour.

D. Create new datasets in QuickSight to do a full reload every hour and add the datasets to SPICE.