Amazon DAS-C01 Exam Practice Test Instant Access

Question 1

A company wants to ingest clickstream data from its website into an Amazon S3 bucket. The streaming data is in JSON format. The data in the S3 bucket must be partitioned by product_id.

Which solution will meet these requirements MOST cost-effectively?

ACreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Enable dynamic partitioning. Specify the data field of productjd as one partitioning key.

BCreate an AWS Glue streaming job to partition the data by productjd before delivering the data to the S3 bucket. Create an Amazon Kinesis Data Firehose delivery stream. Specify the AWS Glue job as the destination of the delivery stream.

CCreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an AWS Glue ETL job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

DCreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an Amazon EMR cluster that includes a job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

Answer : A

Question 2

A network administrator needs to create a dashboard to visualize continuous network patterns over time in a company's AWS account. Currently, the company has VPC Flow Logs enabled and is publishing this data to Amazon CloudWatch Logs. To troubleshoot networking issues quickly, the dashboard needs to display the new data in near-real time.

Which solution meets these requirements?

ACreate a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes the data to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

BCreate an export task from CloudWatch Logs to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

CCreate a CloudWatch Logs subscription that uses an AWS Lambda function to stream the CloudWatch Logs data directly into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

DCreate a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes to an Amazon Kinesis data stream to deliver the data into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

Answer : D

Question 3

A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster.

A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort.

Which combination of steps will create a solution that meets these requirements? (Select THREE.)

AUse an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.

BUse an AWS Glue crawler to catalog the data.

CUse an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.

DUse an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.

EUse QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.

FUse QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

Answer : B, D, F

Question 4

A financial services firm is processing a stream of real-time data from an application by using Apache Kafka and Kafka MirrorMaker. These tools run on premises and stream data to Amazon Managed Streaming for Apache Kafka (Amazon MSK) in the us-east-1 Region. An Apache Flink consumer running on Amazon EMR enriches the data in real time and transfers the output files to an Amazon S3 bucket. The company wants to ensure that the streaming application is highly available across AWS Regions with an RTO of less than 2 minutes.

Which solution meets these requirements?

ALaunch another Amazon MSK and Apache Flink cluster in the us-west-1 Region that is the same size as the original
cluster in the us-east-1 Region. Simultaneously publish and process the data in both Regions. In the event of a disaster that impacts one of the Regions, switch to the other Region.

BSet up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

CAdd an AWS Lambda function in the us-east-1 Region to read from Amazon MSK and write to a global Amazon

DDynamoDB table in on-demand capacity mode. Export the data from DynamoDB to Amazon S3 in the us-west-1 Region. In the event of a disaster that impacts the us-east-1 Region, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

ESet up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region. Store 7 days of data in on-premises Kafka clusters and recover the data missed during the recovery time from the on-premises cluster.

Answer : A

Question 5

A company uses Amazon EC2 instances to receive files from external vendors throughout each day. At the end of each day, the EC2 instances combine the files into a single file, perform gzip compression, and upload the single file to an Amazon S3 bucket. The total size of all the files is approximately 100 GB each day.

When the files are uploaded to Amazon S3, an AWS Batch job runs a COPY command to load the files into an Amazon Redshift cluster.

Which solution will MOST accelerate the COPY process?

AUpload the individual files to Amazon S3. Run the COPY command as soon as the files become available.

BSplit the files so that the number of files is equal to a multiple of the number of slices in the Redshift cluster. Compress and upload the files to Amazon S3. Run the COPY command on the files.

CSplit the files so that each file uses 50% of the free storage on each compute node in the Redshift cluster. Compress and upload the files to Amazon S3. Run the COPY command on the files.

Dpply sharding by breaking up the files so that the DISTKEY columns with the same values go to the same file. Compress and upload the sharded files to Amazon S3. Run the COPY command on the files.

Answer : B

Question 6

A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.

Which solution will meet these requirements?

AUse Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.

BUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.

CUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.

DUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

Answer : C

This solution meets the requirements because:

Amazon Kinesis Data Firehose is a fully managed service that can capture, transform, and load streaming data into AWS data stores, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB1. It can receive data from sensors and other sources and deliver it to a destination with near-real time latency.

AWS Lambda is a serverless compute service that can run code in response to events and automatically manage the underlying compute resources2.It can be used to perform custom transformations on the data during capture by Kinesis Data Firehose3. It can aggregate the data according to the desired logic and output format.

Amazon DynamoDB is a fully managed NoSQL database service that supports key-value and document data models4. It can store nested JSON data as document attributes and provide single-digit millisecond latency for queries. It can be used as a persistent data store for the aggregated sensor data.

Question 7

An event ticketing website has a data lake on Amazon S3 and a data warehouse on Amazon Redshift. Two datasets exist: events data and sales data. Each dataset has millions of records.

The entire events dataset is frequently accessed and is stored in Amazon Redshift. However, only the last 6 months of sales data is frequently accessed and is stored in Amazon Redshift. The rest of the sales data is available only in Amazon S3.

A data analytics specialist must create a report that shows the total revenue that each event has generated in the last 12 months. The report will be accessed thousands of times each week.

Which solution will meet these requirements with the LEAST operational effort?

ACreate an AWS Glue job to access sales data that is older than 6 months from Amazon S3 and to access event and sales data from Amazon Redshift. Load the results into a new table in Amazon Redshift.

BCreate a stored procedure to copy sales data that is older than 6 months and newer than 12 months from Amazon S3 to Amazon Redshift. Create a materialized view with the autorefresh option

CCreate an AWS Lambda function to copy sales data that is older than 6 months and newer than 12 months to an Amazon Kinesis Data Firehose delivery stream. Specify Amazon Redshift as the destination of the delivery stream. Create a materialized view with the autorefresh option.

DCreate a materialized view in Amazon Redshift with the autorefresh option. Use Amazon Redshift Spectrum to include sales data that is older than 6 months.

Answer : D

This solution meets the requirements because:

A materialized view is a database object that contains the results of a query.It can be used to improve query performance and reduce data processing costs by caching the query results and refreshing them periodically1.

The autorefresh option enables Amazon Redshift to automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have this option.Amazon Redshift autorefreshes materialized views as soon as possible after base tables change2.

Amazon Redshift Spectrum enables you to use your existing Amazon Redshift SQL queries to analyze data that is stored in Amazon S3.You can create external tables in your Amazon Redshift cluster and join them with other tables, including materialized views3.

By creating a materialized view in Amazon Redshift with the autorefresh option, the data analytics specialist can precompute and cache the report query results and keep them updated automatically. This can improve the report performance and reduce the load on the Amazon Redshift cluster.

By using Amazon Redshift Spectrum to include sales data that is older than 6 months, the data analytics specialist can access the data that is stored in Amazon S3 without loading it into Amazon Redshift. This can reduce the storage costs and avoid data duplication.

Amazon DAS-C01 AWS Certified Data Analytics - Specialty Exam Practice Test