Free Amazon DAS-C01 Exam Questions

Question 1

An IOT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions. The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data sothat the company can scale accordingly. The company needs to know which sensor types have the largest values for the following metrics: ByteslnPerSec and MessageslnPerSec. Which level of monitoring for Amazon MSK will meet these requirements?

A : DEFAULT level

B : PER TOPIC PER BROKER level

C : PER BROKER level

D : PER TOPIC level

Answer: B

Question 2

A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket The reports are delivered to the S3 bucket in the following format
< examp/e-reporT-prefix >/< examp/e-report-rtame >/yyyymmdd-yyyymmdd/<examp/e-report-name> parquet
An AWS Glue crawler crawls the S3 bucket and populates an AWS Glue Data Catalog with a table Business analysts use Amazon Athena to query the table and create monthly summary reports for the AWS accounts The business analysts are experiencing slow queries because of the accumulation of reports from the last 5 years The business analysts want the operations team to make changes to improve query performance
Which action should the operations team take to meet these requirements?

A : ange the file format to csv.zip.

B : rtition the data by date and account ID

C : rtition the data by month and account ID

D : rtition the data by account ID, year, and month

Answer: B

Question 3

A company has an application that ingests streaming dat
a. 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 : gest 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 : gest 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 Firehouse

C : gest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute or 1 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 : gest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or 1 MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a 1-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.

Answer: B

Question 4

A company with a video streaming website wants to analyze user behavior to make recommendations to users in real time Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3 The company wants a solution that can use standard SQL quenes The solution must also provide a way to look up pre-calculated reference data while making recommendations
Which solution meets these requirements?

A : an AWS Glue Python shell job to process incoming data from Kinesis Data Streams Use the Boto3 library to write data to Amazon Redshift

B : AWS Glue streaming and Scale to process incoming data from Kinesis Data Streams Use the AWS Glue connector to write data to Amazon Redshift

C : Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use a data stream to write results to Amazon Redshift

D : Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift

Answer: D

Question 5

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadat
a. 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?

A : t AWS Identity and Access Management (1AM) permissions tor AWS Glue

B : AWS Lake Formation permissions

C : nage AWS Glue and S3 permissions by using bucket policies

D : Amazon Cognito user pools.

Answer: B