Amazon DAS-C01 Exam Questions Instant Access

Question 1

A bank is using Amazon Managed Streaming for Apache Kafka (Amazon MSK) to populate real-time data into a data lake The data lake is built on Amazon S3, and data must be accessible from the data lake within 24 hours Different microservices produce messages to different topics in the cluster The cluster is created with 8 TB of Amazon Elastic Block Store (Amazon EBS) storage and a retention period of 7 days

The customer transaction volume has tripled recently and disk monitoring has provided an alert that the cluster is almost out of storage capacity

What should a data analytics specialist do to prevent the cluster from running out of disk space1?

AUse the Amazon MSK console to triple the broker storage and restart the cluster

BCreate an Amazon CloudWatch alarm that monitors the KafkaDataLogsDiskUsed metric Automatically flush the oldest messages when the value of this metric exceeds 85%

CCreate a custom Amazon MSK configuration Set the log retention hours parameter to 48 Update the cluster with the new configuration file

DTriple the number of consumers to ensure that data is consumed as soon as it is added to a topic.

Answer : B

Question 2

A market data company aggregates external data sources to create a detailed view of product consumption in different countries. The company wants to sell this data to external parties through a subscription. To achieve this goal, the company needs to make its data securely available to external parties who are also AWS users.

What should the company do to meet these requirements with the LEAST operational overhead?

AStore the data in Amazon S3. Share the data by using presigned URLs for security.

BStore the data in Amazon S3. Share the data by using S3 bucket ACLs.

CUpload the data to AWS Data Exchange for storage. Share the data by using presigned URLs for security.

DUpload the data to AWS Data Exchange for storage. Share the data by using the AWS Data Exchange sharing wizard.

Answer : A

Question 3

A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.

Which solution meets these requirements?

AUse Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the data catalog in Aurora. Schedule the Lambda functions periodically.

BUse the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes. Schedule the crawlers periodically to update the metadata catalog.

CUse Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalog. Schedule the Lambda functions periodically.

DUse the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.

Answer : D

Question 4

A company hosts an on-premises PostgreSQL database that contains historical dat

a. An internal legacy application uses the database for read-only activities. The company's business team wants to move the data to a data lake in Amazon S3 as soon as possible and enrich the data for analytics.

The company has set up an AWS Direct Connect connection between its VPC and its on-premises network. A data analytics specialist must design a solution that achieves the business team's goals with the least operational overhead.

Which solution meets these requirements?

AUpload the data from the on-premises PostgreSQL database to Amazon S3 by using a customized batch upload process. Use the AWS Glue crawler to catalog the data in Amazon S3. Use an AWS Glue job to enrich and store the result in a separate S3 bucket in Apache Parquet format. Use Amazon Athena to query the data.

BCreate an Amazon RDS for PostgreSQL database and use AWS Database Migration Service (AWS DMS) to migrate the data into Amazon RDS. Use AWS Data Pipeline to copy and enrich the data from the Amazon RDS for PostgreSQL table and move the data to Amazon S3. Use Amazon Athena to query the data.

CConfigure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises database. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet format. Create an Amazon Redshift cluster and use Amazon Redshift Spectrum to query the data.

DConfigure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises database. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet format. Use Amazon Athena to query the data.

Answer : B

Question 5

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 dat

a. 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?

AUse enhanced fan-out in Kinesis Data Streams.

BIncrease the number of shards for the Kinesis data stream.

CReduce the propagation delay by overriding the KCL default settings.

DDevelop consumers by using Amazon Kinesis Data Firehose.

Answer : C

The KCL defaults are set to follow the best practice of polling every 1 second. This default results in average propagation delays that are typically below 1 second.

Question 6

A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.

Which solution meets these requirements?

AUse AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.

BUse Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.

CUse AWS Glue to convert the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.

DUse AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Answer : D

Question 7

A marketing company has data in Salesforce, MySQL, and Amazon S3. The company wants to use data from these three locations and create mobile dashboards for its users. The company is unsure how it should create the dashboards and needs a solution with the least possible customization and coding.

Which solution meets these requirements?

AUse Amazon Athena federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.

BUse AWS Lake Formation to migrate the data sources into Amazon S3. Use Amazon QuickSight to generate the mobile dashboards.

CUse Amazon Redshift federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.

DUse Amazon QuickSight to connect to the data sources and generate the mobile dashboards.

Answer : C

Amazon AWS Certified Data Analytics - Specialty DAS-C01 Exam Questions