Amazon DAS-C01 dumps

Amazon DAS-C01 Dumps

Amazon AWS Certified Data Analytics - Specialty

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DAS-C01 - AWS Certified Data Analytics - Specialty

The AWS Certified Data Analytics - Specialty (DAS-C01) Exam is geared for individuals who work in the field of data analytics. The examination confirms a candidate's comprehensive understanding of how to design, build, protect, and oversee analytics systems that provide data insight utilizing AWS services.

AWS Certified Data Analytics – Specialty exam formatting

Structure of questions: Multiple choice and multi response
Cost of exam: $200-300 USD
Time allowed: 1-2 hours
Total questions: 65 questions
Language: English, Korean, Japanese
Type of exam: VUE persona VUE persona

Main characteristics to check in the DAS-C01 test

Describe the role that AWS data analytics services play in the collection, storage, processing, and visualization phases of the data lifecycle. Understand what AWS data analytics services are and how they relate to one another.

Domain for AWS Certified Data Analytics DAS-C01 Exam

How much prior work experience is necessary to earn this certificate?

A candidate has to have at least 7 years of experience to earn the AWS Certified Data Analytics certification. This includes two years of actual job experience and five years of data analyst technology.

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Amazon DAS-C01 Sample Questions

Question # 1

A company uses Amazon Connect to manage its contact center. The company usesSalesforce to manage its customer relationship management (CRM) data. The companymust build a pipeline to ingest data from Amazon Connect and Salesforce into a data lakethat is built on Amazon S3.Which solution will meet this requirement with the LEAST operational overhead?

A. Use Amazon Kinesis Data Streams to ingest the Amazon Connect data. Use AmazonAppFlow to ingest the Salesforce data.
B. Use Amazon Kinesis Data Firehose to ingest the Amazon Connect data. Use AmazonKinesis Data Streams to ingest the Salesforce data.
C. Use Amazon Kinesis Data Firehose to ingest the Amazon Connect data. Use AmazonAppFlow to ingest the Salesforce data.
D. Use Amazon AppFlow to ingest the Amazon Connect data. Use Amazon Kinesis DataFirehose to ingest the Salesforce data.


Question # 2

A financial services company is building a data lake solution on Amazon S3. The companyplans to use analytics offerings from AWS to meet user needs for one-time querying andbusiness intelligence reports. A portion of the columns will contain personally identifiableinformation (Pll). Only authorized users should be able to see plaintext PII data.What is the MOST operationally efficient solution that meets these requirements?

A. Define a bucket policy for each S3 bucket of the data lake to allow access to users whohave authorization to see PII data. Catalog the data by using AWS Glue. Create two IAMroles. Attach a permissions policy with access to PII columns to one role. Attach a policywithout these permissions to the other role.
B. Register the S3 locations with AWS Lake Formation. Create two IAM roles. Use LakeFormation data permissions to grant Select permissions to all of the columns for one role.Grant Select permissions to only columns that contain non-PII data for the other role.
C. Register the S3 locations with AWS Lake Formation. Create an AWS Glue job to createan E TL workflow that removes the Pll columns from the data and creates a separate copyof the data in another data lake S3 bucket. Register the new S3 locations with LakeFormation. Grant users the permissions to each data lake data based on whether the usersare authorized to see PII data.
D. Register the S3 locations with AWS Lake Formation. Create two IAM roles. Attach apermissions policy with access to Pll columns to one role. Attach a policy without thesepermissions to the other role. For each downstream analytics service, use its nativesecurity functionality and the IAM roles to secure the Pll data.


Question # 3

A data analytics specialist has a 50 GB data file in .csv format and wants to perform a datatransformation task. The data analytics specialist is using the Amazon Athena CREATETABLE AS SELECT (CTAS) statement to perform the transformation. The resulting outputwill be used to query the data from Amazon Redshift Spectrum.Which CTAS statement should the data analytics specialist use to provide the MOSTefficient performance?

A. Option A
B. Option B
C. Option C
D. Option D


Question # 4

A large marketing company needs to store all of its streaming logs and create near-realtimedashboards. The dashboards will be used to help the company make critical businessdecisions and must be highly available.Which solution meets these requirements?

A. Store the streaming logs in Amazon S3 with replication to an S3 bucket in a differentAvailability Zone. Create the dashboards by using Amazon QuickSight.
B. Deploy an Amazon Redshift cluster with at least three nodes in a VPC that spans twoAvailability Zones. Store the streaming logs and use the Redshift cluster as a source tocreate the dashboards by using Amazon QuickSight.
C. Store the streaming logs in Amazon S3 with replication to an S3 bucket in a differentAvailability Zone. Every time a new log is added in the bucket, invoke an AWS Lambdafunction to update the dashboards in Amazon QuickSight.
D. Store the streaming logs in Amazon OpenSearch Service deployed across threeAvailability Zones and with three dedicated master nodes. Create the dashboards by usingOpenSearch Dashboards.


Question # 5

A company uses Amazon Redshift for its data warehouse. The company is running an ET Lprocess that receives data in data parts from five third-party providers. The data partscontain independent records that are related to one specific job. The company receives thedata parts at various times throughout each day.A data analytics specialist must implement a solution that loads the data into AmazonRedshift only after the company receives all five data parts.Which solution will meet these requirements?

A. Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect thedata from the different sources andto form a single object before loading the data intoAmazon Redshift.
B. Use an AWS Lambda function that is scheduled by cron to load the data into atemporary table in Amazon Redshift. Use Amazon Redshift database triggers toconsolidate the final data when all five data parts are ready.
C. Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function thatis invoked by S3 upload events. Configure the function to validate that all five data partsare gathered before the function loads the data into Amazon Redshift.
D. Create an Amazon Kinesis Data Firehose delivery stream. Program a Python conditionthat will invoke a buffer flush when all five data parts are received.


Question # 6

A company has a mobile app that has millions of users. The company wants to enhancethe 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 isused in the visualization should be no more than two hours old.Which solution will meet these requirements with the LEAST operational overhead?

A. Run an hourly batch process that renders user-specific data visualizations as staticimages that are stored in Amazon S3.
B. Precompute aggregated data hourly. Store the data in Amazon DynamoDB. Render thedata by using the D3.js JavaScript library.
C. Embed an Amazon QuickSight Enterprise edition dashboard into the mobile app byusing the QuickSight Embedding SDK. Refresh data in SPICE hourly.
D. Run Amazon Athena queries behind an Amazon API Gateway API. Render the data byusing the D3.js JavaScript library.


Question # 7

A data analyst notices the following error message while loading data to an AmazonRedshift cluster:"The bucket you are attempting to access must be addressed using the specifiedendpoint."What should the data analyst do to resolve this issue?

A. Specify the correct AWS Region for the Amazon S3 bucket by using the REGION optionwith the COPY command.
B. Change the Amazon S3 object's ACL to grant the S3 bucket owner full control of theobject.
C. Launch the Redshift cluster in a VPC.
D. Configure the timeout settings according to the operating system used to connect to theRedshift cluster.


Question # 8

A large energy company is using Amazon QuickSight to build dashboards and report thehistorical usage data of its customers This data is hosted in Amazon Redshift The reportsneed access to all the fact tables' billions ot records to create aggregation in real timegrouping by multiple dimensionsA data analyst created the dataset in QuickSight by using a SQL query and not SPICEBusiness users have noted that the response time is not fast enough to meet their needsWhich action would speed up the response time for the reports with the LEASTimplementation effort?

A. Use QuickSight to modify the current dataset to use SPICE
B. Use AWS Glue to create an Apache Spark job that joins the fact table with thedimensions. Load the data into a new table
C. Use Amazon Redshift to create a materialized view that joins the fact table with thedimensions
D. Use Amazon Redshift to create a stored procedure that joins the fact table with thedimensions Load the data into a new table


Question # 9

A large media company is looking for a cost-effective storage and analysis solution for itsdaily media recordings formatted with embedded metadata. Daily data sizes rangebetween 10-12 TB with stream analysis required on timestamps, video resolutions, filesizes, closed captioning, audio languages, and more. Based on the analysis,processing the datasets is estimated to take between 30-180 minutes depending on theunderlying 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?

A. Store 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 Bltools.
B. Store the video files in Amazon S3 and use AWS Lambda to extract the metadata fromthe files and load it to Amazon S3. Use Amazon Athena to provide the data to be analyzedby the BI tools.
C. Store the video files in Amazon DynamoDB and use Amazon EMR to extract themetadata from the files and load it to Apache Hive. Use Apache Hive to provide the data tobe analyzed by the Bl tools.
D. Store the video files in Amazon S3 and use AWS Glue to extract the metadata from thefiles and load it to Amazon Redshift. Use Amazon Redshift to provide the data to beanalyzed by the Bl tools.


Question # 10

A company is using an AWS Lambda function to run Amazon Athena queries against across-account AWS Glue Data Catalog. A query returns the following error:HIVE METASTORE ERRORThe error message states that the response payload size exceeds the maximum allowedpayload size. The queried table is already partitioned, and the data is stored in anAmazon S3 bucket in the Apache Hive partition format.Which solution will resolve this error?

A. Modify the Lambda function to upload the query response payload as an object into theS3 bucket. Include an S3 object presigned URL as the payload in the Lambda functionresponse.
B. Run the MSCK REPAIR TABLE command on the queried table.
C. Create a separate folder in the S3 bucket. Move the data files that need to be queriedinto that folder. Create an AWS Glue crawler that points to the folder instead of the S3bucket.
D. Check the schema of the queried table for any characters that Athena does not support.Replace any unsupported characters with characters that Athena supports.



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