Updated Amazon DAS-C01 Exam Dumps (June, 2024)

AWS Certified Data Analytics - Specialty

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Exam Code DAS-C01
Exam Name AWS Certified Data Analytics - Specialty
Questions 157
Update Date June 05,2024
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What is AWS Certified Data Analytics - Specialty (DAS-C01) Exam ?

The AWS Certified Data Analytics - Specialty Exam is a certification exam offered by Amazon Web Services (AWS) that validates an individual's expertise in designing and implementing scalable, cost-effective, and secure data analytics solutions on AWS.

This exam is designed for individuals who have a deep understanding of data analytics technologies and at least five years of experience with data analytics. It covers a range of topics including data collection, storage, processing, analysis, visualization, and security.

To pass the exam, candidates must demonstrate their ability to design and implement scalable and cost-effective data analytics solutions using AWS services, as well as troubleshoot common issues and optimize performance.

Achieving this certification can help individuals demonstrate their skills and knowledge to potential employers and enhance their career prospects in the cloud computing industry. Additionally, this certification can help data analytics professionals differentiate themselves in the market and demonstrate their ability to design and manage advanced data analytics solutions on the AWS platform.

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Amazon DAS-C01 Exam Sample Questions

Question 1

A gaming company is building a serverless data lake. The company is ingesting streamingdata into Amazon Kinesis Data Streams and is writing the data to Amazon S3 throughAmazon Kinesis Data Firehose. The company is using 10 MB as the S3 buffer size and isusing 90 seconds as the buffer interval. The company runs an AWS Glue ET L job tomerge 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 AWSGlue ETL jobs are frequently showing an OutOfMemoryError error.Which solutions will resolve this issue without incurring additional costs? (Select TWO.)

A. Place the small files into one S3 folder. Define one single table for the small S3 files inAWS Glue Data Catalog. Rerun the AWS Glue ET L jobs against this AWS Glue table.
B. Create 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.
C. Run the S3DistCp utility in Amazon EMR to merge a large number of small S3 filesbefore running the AWS Glue ETL jobs.
D. Use the groupFiIes setting in the AWS Glue ET L job to merge small S3 files and rerunAWS Glue E TL jobs.
E. Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to900 seconds.

Answer: A,D

Question 2

A retail company has 15 stores across 6 cities in the United States. Once a month, thesales team requests a visualization in Amazon QuickSight that provides the ability to easilyidentify revenue trends across cities and stores.The visualization also helps identify outliersthat need to be examined with further analysis.Which visual type in QuickSight meets the sales team's requirements?

A. Geospatial chart
B. Line chart
C. Heat map
D. Tree map

Answer: A

Question 3

A company uses Amazon EC2 instances to receive files from external vendors throughouteach 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 totalsize 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 toload the files into an Amazon Redshift cluster.Which solution will MOST accelerate the COPY process?

A. Upload the individual files to Amazon S3. Run the COPY command as soon as the filesbecome available.
B. Split the files so that the number of files is equal to a multiple of the number of slices inthe Redshift cluster. Compress and upload the files to Amazon S3. Run the COPYcommand on the files.
C. Split the files so that each file uses 50% of the free storage on each compute node inthe Redshift cluster. Compress and upload the files to Amazon S3. Run the COPYcommand on the files.
D. pply sharding by breaking up the files so that the DISTKEY columns with the samevalues go to the same file. Compress and upload the sharded files to Amazon S3. Run theCOPY command on the files.

Answer: B

Question 4

A bank is building an Amazon S3 data lake. The bank wants a single data repository forcustomer data needs, such as personalized recommendations. The bank needs to useAmazon 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?

A. Invoke an AWS Lambda function from Kinesis Data Firehose to mask the PII beforeKinesis Data Firehose delivers the data to the S3 bucket.
B. Use Amazon Macie to scan the S3 bucket. Configure Macie to discover Pll. Invoke anAWS Lambda function from S3 events to mask the Pll.
C. Configure server-side encryption (SSE) for the S3 bucket. Invoke an AWS Lambdafunction from S3 events to mask the PII.
D. Create an AWS Lambda function to read the objects, mask the Pll, and store the objectsback with same key. Invoke the Lambda function from S3 events.

Answer: A

Question 5

A company developed a new voting results reporting website that uses Amazon KinesisData Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company isnow seeking a solution to perform this infrequent data analysis with data visualizationcapabilities in a way that requires minimal development effort.Which solution MOST cost-effectively meets these requirements?

A. Use an AWS Glue crawler to create and update a table in the AWS Glue data catalogfrom the logs. Use Amazon Athena to perform ad-hoc analyses. Develop datavisualizations by using Amazon QuickSight.
B. Configure Kinesis Data Firehose to deliver the logs to an Amazon OpenSearch Servicecluster. Use OpenSearch Service REST APIs to analyze the data. Visualize the data bybuilding an OpenSearch Service dashboard.
C. Create an AWS Lambda function to convert the logs to CSV format. Add the Lambdafunction to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift toperform a one-time analysis of the logs by using SQL queries. Develop data visualizationsby using Amazon QuickSight.
D. Create an Amazon EMR cluster and use Amazon S3 as the data source. Create anApache Spark job to perform a one-time analysis of the logs. Develop data visualizationsby using Amazon QuickSight.

Answer: A

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