New Year Special Sale Limited Time 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: scxmas70

Note! Following DAS-C01 Exam is Retired now. Please select the alternative replacement for your Exam Certification.

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

Go to page:
Question # 49

A bank wants to migrate a Teradata data warehouse to the AWS Cloud The bank needs a solution for reading large amounts of data and requires the highest possible performance. The solution also must maintain the separation of storage and compute

Which solution meets these requirements?

A.

Use Amazon Athena to query the data in Amazon S3

B.

Use Amazon Redshift with dense compute nodes to query the data in Amazon Redshift managed storage

C.

Use Amazon Redshift with RA3 nodes to query the data in Amazon Redshift managed storage

D.

Use PrestoDB on Amazon EMR to query the data in Amazon S3

Full Access
Question # 50

An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.

Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

A.

Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.

B.

Use an S3 bucket in the same account as Athena.

C.

Compress the objects to reduce the data transfer I/O.

D.

Use an S3 bucket in the same Region as Athena.

E.

Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.

F.

Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

Full Access
Question # 51

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

Which visualization solution will meet these requirements?

A.

Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in 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.

Full Access
Question # 52

An analytics software as a service (SaaS) provider wants to offer its customers business intelligence

The provider wants to give customers two user role options

• Read-only users for individuals who only need to view dashboards

• Power users for individuals who are allowed to create and share new dashboards with other users

Which QuickSight feature allows the provider to meet these requirements'?

A.

Embedded dashboards

B.

Table calculations

C.

Isolated namespaces

D.

SPICE

Full Access
Question # 53

A financial services company needs to aggregate daily stock trade data from the exchanges into a data store. The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency. The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.

Which solution meets the company’s requirements?

A.

Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.

B.

Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.

C.

Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.

D.

Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.

Full Access
Question # 54

A data analyst notices the following error message while loading data to an Amazon Redshift cluster:

"The bucket you are attempting to access must be addressed using the specified endpoint."

What should the data analyst do to resolve this issue?

A.

Specify the correct AWS Region for the Amazon S3 bucket by using the REGION option with the COPY command.

B.

Change the Amazon S3 object's ACL to grant the S3 bucket owner full control of the object.

C.

Launch the Redshift cluster in a VPC.

D.

Configure the timeout settings according to the operating system used to connect to the Redshift cluster.

Full Access
Question # 55

A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.

Which approach would enable the desired outcome while keeping data persistence costs low?

A.

Ingest the data stream with Amazon Kinesis Data Streams. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.

B.

Ingest the data stream with Amazon Kinesis Data Streams. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status codes. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehose.

C.

Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of I minute or I MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.

D.

Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or I MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a I-minute window using the RCF function and summarize the count of status codes. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Full Access
Question # 56

A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day.

A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.

How can the data analyst meet these requirements with the lowest maintenance overhead?

A.

Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.

B.

Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.

C.

Load all the data into the new table and grant all users read-only permissions to non-sensitive columns. Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns.

D.

Load all the data into the new table and grant the auditing group permission to read from the table. Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table.

Full Access
Go to page: