Newest Amazon DAS-C01 Practice Questions at Zero Cost

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Question 1

A company has a mobile app that has millions of users. The company wants to enhance the mobile app by including interactive data visualizations that show user trends.

The data for visualization is stored in a large data lake with 50 million rows. Data that is used in the visualization should be no more than two hours old.

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

ARun an hourly batch process that renders user-specific data visualizations as static images that are stored in Amazon S3.

BPrecompute aggregated data hourly. Store the data in Amazon DynamoDB. Render the data by using the D3.js JavaScript library.

CEmbed an Amazon QuickSight Enterprise edition dashboard into the mobile app by using the QuickSight Embedding SDK. Refresh data in SPICE hourly.

DRun Amazon Athena queries behind an Amazon API Gateway API. Render the data by using the D3.js JavaScript library.

Correct Answer: 1

Question 2

A bank is building an Amazon S3 data lake. The bank wants a single data repository for customer data needs, such as personalized recommendations. The bank needs to use Amazon Kinesis Data Firehose to ingest customers' personal information, bank accounts, and transactions in near real time from a transactional relational database.

All personally identifiable information (Pll) that is stored in the S3 bucket must be masked. The bank has enabled versioning for the S3 bucket.

Which solution will meet these requirements?

AInvoke an AWS Lambda function from Kinesis Data Firehose to mask the PII before Kinesis Data Firehose delivers the data to the S3 bucket.

BUse Amazon Macie to scan the S3 bucket. Configure Macie to discover Pll. Invoke an AWS Lambda function from S3 events to mask the Pll.

CConfigure server-side encryption (SSE) for the S3 bucket. Invoke an AWS Lambda function from S3 events to mask the PII.

DCreate an AWS Lambda function to read the objects, mask the Pll, and store the objects back with same key. Invoke the Lambda function from S3 events.

Correct Answer: 2

Question 3

A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehose. The company is using 10 MB as the S3 buffer size and is using 90 seconds as the buffer interval. The company runs an AWS Glue ET L job to merge and transform the data to a different format before writing the data back to Amazon S3.

Recently, the company has experienced substantial growth in its data volume. The AWS Glue ETL jobs are frequently showing an OutOfMemoryError error.

Which solutions will resolve this issue without incurring additional costs? (Select TWO.)

APlace the small files into one S3 folder. Define one single table for the small S3 files in AWS Glue Data Catalog. Rerun the AWS Glue ET L jobs against this AWS Glue table.

BCreate an AWS Lambda function to merge small S3 files and invoke them periodically. Run the AWS Glue ETL jobs after successful completion of the Lambda function.

CRun the S3DistCp utility in Amazon EMR to merge a large number of small S3 files before running the AWS Glue ETL jobs.

DUse the groupFiIes setting in the AWS Glue ET L job to merge small S3 files and rerun AWS Glue E TL jobs.

EUpdate the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.

Correct Answer: 3

A, D

Question 4

A company hosts its analytics solution on premises. The analytics solution includes a server that collects log files. The analytics solution uses an Apache Hadoop cluster to analyze the log files hourly and to produce output files. All the files are archived to another server for a specified duration.

The company is expanding globally and plans to move the analytics solution to multiple AWS Regions in the AWS Cloud. The company must adhere to the data archival and retention requirements of each country where the data is stored.

Which solution will meet these requirements?

ACreate an Amazon S3 bucket in one Region to collect the log files. Use S3 event notifications to invoke an AWS Glue job for log analysis. Store the output files in the target S3 bucket. Use S3 Lifecycle rules on the target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

BCreate a Hadoop Distributed File System (HDFS) file system on an Amazon EMR cluster in one Region to collect the log files. Set up a bootstrap action on the EMR cluster to run an Apache Spark job. Store the output files in a target Amazon S3 bucket. Schedule a job on one of the EMR nodes to delete files that no longer need to be retained.

CCreate an Amazon S3 bucket in each Region to collect log files. Create an Amazon EMR cluster. Submit steps on the EMR cluster for analysis. Store the output files in a target S3 bucket in each Region. Use S3 Lifecycle rules on each target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

DCreate an Amazon Kinesis Data Firehose delivery stream in each Region to collect log data. Specify an Amazon S3 bucket in each Region as the destination. Use S3 Storage Lens for data analysis. Use S3 Lifecycle rules on each destination S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

Correct Answer: 4

Question 5

A large media company is looking for a cost-effective storage and analysis solution for its daily media recordings formatted with embedded metadat

a. Daily data sizes range between 10-12 TB with stream analysis required on timestamps, video resolutions, file sizes, closed captioning, audio languages, and more. Based on the analysis,

processing the datasets is estimated to take between 30-180 minutes depending on the underlying framework selection. The analysis will be done by using business intelligence (Bl) tools that can be connected to data sources with AWS or Java Database Connectivity (JDBC) connectors.

Which solution meets these requirements?

AStore the video files in Amazon DynamoDB and use AWS Lambda to extract the metadata from the files and load it to DynamoDB. Use DynamoDB to provide the data to be analyzed by the Bl tools.

BStore the video files in Amazon S3 and use AWS Lambda to extract the metadata from the files and load it to Amazon S3. Use Amazon Athena to provide the data to be analyzed by the BI tools.

CStore the video files in Amazon DynamoDB and use Amazon EMR to extract the metadata from the files and load it to Apache Hive. Use Apache Hive to provide the data to be analyzed by the Bl tools.

DStore the video files in Amazon S3 and use AWS Glue to extract the metadata from the files and load it to Amazon Redshift. Use Amazon Redshift to provide the data to be analyzed by the Bl tools.

Correct Answer: 5

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