Amazon DAS-C01 Exam Dumps

AWS Certified Data Analytics - Specialty

Amazon DAS-C01 Sample Questions

Question # 1

A business intelligence (Bl) engineer must create a dashboard to visualize how oftencertain keywords are used in relation to others in social media posts about a public figure.The Bl engineer extracts the keywords from the posts and loads them into an AmazonRedshift table. The table displays the keywords and the count correspondingto each keyword.The Bl engineer needs to display the top keywords with more emphasis on the mostfrequently used keywords.Which visual type in Amazon QuickSight meets these requirements?

A. Bar charts
B. Word clouds
C. Circle packing
D. Heat maps

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Question # 2

A company uses an Amazon Redshift provisioned cluster for data analysis. The data is notencrypted at rest. A data analytics specialist must implement a solution to encrypt the dataat rest.Which solution will meet this requirement with the LEAST operational overhead?

A. Use the ALTER TABLE command with the ENCODE option to update existing columnsof the Redshift tables to use LZO encoding.
B. Export data from the existing Redshift cluster to Amazon S3 by using the UNLOADcommand with the ENCRYPTED option. Create a new Redshift cluster with encryptionconfigured. Load data into the new cluster by using the COPY command.
C. Create a manual snapshot of the existing Redshift cluster. Restore the snapshot into anew Redshift cluster with encryption configured.
D. Modify the existing Redshift cluster to use AWS Key Management Service (AWS KMS)encryption. Wait for the cluster to finish resizing.

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Question # 3

A company's data science team is designing a shared dataset repository on a Windowsserver. The data repository will store a large amount of training data that the datascience team commonly uses in its machine learning models. The data scientists create arandom number of new datasets each day.The company needs a solution that provides persistent, scalable file storage and highlevels of throughput and IOPS. The solution also must be highly available and mustintegrate with Active Directory for access control.Which solution will meet these requirements with the LEAST development effort?

A. Store datasets as files in an Amazon EMR cluster. Set the Active Directory domain forauthentication.
B. Store datasets as files in Amazon FSx for Windows File Server. Set the Active Directorydomain for authentication.
C. Store datasets as tables in a multi-node Amazon Redshift cluster. Set the ActiveDirectory domain for authentication.
D. Store datasets as global tables in Amazon DynamoDB. Build an application to integrateauthentication with the Active Directory domain.

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Question # 4

A company is creating a data lake by using AWS Lake Formation. The data that will bestored in the data lake contains sensitive customer information and must be encrypted atrest using an AWS Key Management Service (AWS KMS) customer managed key to meetregulatory requirements.How can the company store the data in the data lake to meet these requirements?

A. Store the data in an encrypted Amazon Elastic Block Store (Amazon EBS) volume.Register the Amazon EBS volume with Lake Formation.
B. Store the data in an Amazon S3 bucket by using server-side encryption with AWS KMS(SSE-KMS). Register the S3 location with Lake Formation.
C. Encrypt the data on the client side and store the encrypted data in an Amazon S3bucket. Register the S3 location with Lake Formation.
D. Store the data in an Amazon S3 Glacier Flexible Retrieval vault bucket. Register the S3Glacier Flexible Retrieval vault with Lake Formation.

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Question # 5

A financial company uses Amazon Athena to query data from an Amazon S3 data lake.Files are stored in the S3 data lake in Apache ORC format. Data analysts recentlyintroduced nested fields in the data lake ORC files, and noticed that queries are takinglonger to run in Athena. A data analysts discovered that more data than what is required isbeing scanned for the queries.What is the MOST operationally efficient solution to improve query performance?

A. Flatten nested data and create separate files for each nested dataset.
B. Use the Athena query engine V2 and push the query filter to the source ORC file.
C. Use Apache Parquet format instead of ORC format.
D. Recreate the data partition strategy and further narrow down the data filter criteria.

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Question # 6

A company collects data from parking garages. Analysts have requested the ability to runreports in near real time about the number of vehicles in each garage.The company wants to build an ingestion pipeline that loads the data into an AmazonRedshift cluster. The solution must alert operations personnel when the number of vehiclesin a particular garage exceeds a specific threshold. The alerting query will use garagethreshold values as a static reference. The threshold values are stored inAmazon S3.What is the MOST operationally efficient solution that meets these requirements?

A. Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliverthe data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application thatuses the same delivery stream as an input source. Create a reference data source inKinesis Data Analytics to temporarily store the threshold values from Amazon S3 and tocompare the number of vehicles in a particular garage to the corresponding thresholdvalue. Configure an AWS Lambda function to publish an Amazon Simple NotificationService (Amazon SNS) notification if the number of vehicles exceeds the threshold.
B. Use an Amazon Kinesis data stream to collect the data. Use an Amazon Kinesis DataFirehose delivery stream to deliver the data to Amazon Redshift. Create another Kinesisdata stream to temporarily store the threshold values from Amazon S3. Send the deliverystream and the second data stream to Amazon Kinesis Data Analytics to compare thenumber of vehicles in a particular garage to the corresponding threshold value. Configurean AWS Lambda function to publish an Amazon Simple Notification Service (Amazon SNS)notification if the number of vehicles exceeds the threshold.
C. Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliverthe data to Amazon Redshift. Automatically initiate an AWS Lambda function that queriesthe data in Amazon Redshift. Configure the Lambda function to compare the number ofvehicles in a particular garage to the correspondingthreshold value from Amazon S3. Configure the Lambda function to also publish an Amazon Simple Notification Service(Amazon SNS) notification if the number of vehicles exceeds the threshold.
D. Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliverthe data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application thatuses the same delivery stream as an input source. Use Kinesis Data Analytics to comparethe number of vehicles in a particular garage to the corresponding threshold value that isstored in a table as an in-application stream. Configure an AWS Lambda function as anoutput for the application to publish an Amazon Simple Queue Service (Amazon SQS)notification if the number of vehicles exceeds the threshold.

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Question # 7

A company is designing a data warehouse to support business intelligence reporting. Userswill access the executive dashboard heavily each Monday and Friday morningfor I hour. These read-only queries will run on the active Amazon Redshift cluster, whichruns on dc2.8xIarge compute nodes 24 hours a day, 7 days a week. There arethree queues set up in workload management: Dashboard, ETL, and System. The AmazonRedshift cluster needs to process the queries without wait time.What is the MOST cost-effective way to ensure that the cluster processes these queries?

A. Perform a classic resize to place the cluster in read-only mode while adding anadditional node to the cluster.
B. Enable automatic workload management.
C. Perform an elastic resize to add an additional node to the cluster.
D. Enable concurrency scaling for the Dashboard workload queue.

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Question # 8

A company analyzes historical data and needs to query data that is stored in Amazon S3.New data is generated daily as .csv files that are stored in Amazon S3. The company'sdata analysts are using Amazon Athena to perform SQL queries against a recent subset ofthe overall data.The amount of data that is ingested into Amazon S3 has increased to 5 PB over time. Thequery latency also has increased. The company needs to segment the data to reduce theamount of data that is scanned.Which solutions will improve query performance? (Select TWO.)Use MySQL Workbench on an Amazon EC2 instance. Connect to Athena by using a JDBCconnector. Run the query from MySQL Workbench instead ofAthena directly.

A. Configure Athena to use S3 Select to load only the files of the data subset.
B. Create the data subset in Apache Parquet format each day by using the AthenaCREATE TABLE AS SELECT (CTAS) statement. Query the Parquet data.
C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet format and topartition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data each day.
D. Create an S3 gateway endpoint. Configure VPC routing to access Amazon S3 throughthe gateway endpoint.

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Question # 9

A company wants to use a data lake that is hosted on Amazon S3 to provide analyticsservices for historical data. The data lake consists of 800 tables but is expected to grow tothousands of tables. More than 50 departments use the tables, and each department hashundreds of users. Different departments need access to specific tables and columns. Which solution will meet these requirements with the LEAST operational overhead?

A. Create an 1AM role for each department. Use AWS Lake Formation based accesscontrol to grant each 1AM role access to specific tables and columns. Use Amazon Athenato analyze the data.
B. Create an Amazon Redshift cluster for each department. Use AWS Glue to ingest intothe Redshift cluster only the tables and columns that are relevant to that department.Create Redshift database users. Grant the users access to the relevant department'sRedshift cluster. Use Amazon Redshift to analyze the data.
C. Create an 1AM role for each department. Use AWS Lake Formation tag-based accesscontrol to grant each 1AM roleaccess to only the relevant resources. Create LF-tags that are attached to tables andcolumns. Use Amazon Athena to analyze the data.
D. Create an Amazon EMR cluster for each department. Configure an 1AM service role foreach EMR cluster to access
E. relevant S3 files. For each department's users, create an 1AM role that provides accessto the relevant EMR cluster. Use Amazon EMR to analyze the data.

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Question # 10

A data analyst is designing an Amazon QuickSight dashboard using centralized sales datathat resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New Yorkcan see only United States (US) data.What should the data analyst do to ensure the appropriate data security is in place?

A. Place the data sources for Australia and the US into separate SPICE capacity pools.
B. Set up an Amazon Redshift VPC security group for Australia and the US.
C. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the salestable.
D. Deploy QuickSight Enterprise edition and set up different VPC security groups forAustralia and the US.

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Question # 11

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.

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