Amazon DAS-C01 Online Practice Questions and Exam Preparation

Amazon DAS-C01 Online Questions & Answers

• Question 161:

  A large university has adopted a strategic goal of increasing diversity among enrolled students. The data analytics team is creating a dashboard with data visualizations to enable stakeholders to view historical trends. All access must be authenticated using Microsoft Active Directory. All data in transit and at rest must be encrypted.

  Which solution meets these requirements?

  A. Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0. and the default encryption settings.
  B. Amazon QuickSight Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
  C. Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory. Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
  D. Amazon QuickSight Enterprise edition using AD Connector to authenticate using Active Directory. Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
  B. Amazon QuickSight Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.

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  Explanation/Reference:

  Answer is B Authentication: https://docs.aws.amazon.com/quicksight/latest/user/external-identity-providers-setting-up-saml.html#external-identity-providers-config-idp Encryption: https://docs.aws.amazon.com/quicksight/latest/user/data-encryption-at-rest.html "All keys associated with Amazon QuickSight are managed by AWS." in https://docs.aws.amazon.com/quicksight/latest/user/key-management.html - No way to use customer-provided keys in QuickSight

• Question 162:

  A company using Amazon QuickSight Enterprise edition has thousands of dashboards, analyses, and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.

  Which solution should the company implement to simplify permissions management?

  A. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign individual users permissions to these folders.
  B. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign group permissions by using these folders.
  C. Use AWS IAM resource-based policies to assign group permissions to QuickSight items.
  D. Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions.
  B. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign group permissions by using these folders.

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  Explanation/Reference:

  https://docs.aws.amazon.com/quicksight/latest/user/folders.html

• Question 163:

  A startup company runs its data processing and machine learning workloads on Amazon EMR. To increase productivity, the company granted permissions to engineers and analysts to create EMR clusters. A recent security review showed that some EMR clusters had ports open to the public internet.

  A data analytics specialist must make changes so that any new EMR clusters have public access blocked when they are created.

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

  A. Create an AWS Lambda function that checks whether the Amazon EMR security group is open to the public internet. Invoke a Lambda function when an EMR cluster is created. Integrate the function with Amazon Simple Notification Service (Amazon SNS) to send notification email.
  B. Turn on the block public access feature by using the Amazon EMR console.
  C. Use AWS Config to track the security on the EMR cluster. Use Amazon EventBridge to send notifications when an open cluster is detected. Create an AWS Lambda function that the notification invokes to block public access.
  D. Update security groups to remove inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.
  A. Create an AWS Lambda function that checks whether the Amazon EMR security group is open to the public internet. Invoke a Lambda function when an EMR cluster is created. Integrate the function with Amazon Simple Notification Service (Amazon SNS) to send notification email.

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  Explanation/Reference:

• Question 164:

  An insurance company has raw data in JSON format that is sent without a predefined schedule through an Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to provide access to the most up-to-date data.

  Which solution meets these requirements?

  A. Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.
  B. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
  C. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
  D. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
  D. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.

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  Explanation/Reference:

  Data analysts analyze the data using Apache Spark SQL on Amazon EMR for the data stored on S3 in JSON format. Input JSON file landing in S3 triggers a Lambda which invokes Glue Crawler.

• Question 165:

  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 ETL 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? (Choose two.)

  A. Place 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 ETL 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 files before running the AWS Glue ETL jobs.
  D. Use the groupFiles setting in the AWS Glue ETL job to merge small S3 files and rerun AWS Glue ETL jobs. Most Voted
  E. Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.
  D. Use the groupFiles setting in the AWS Glue ETL job to merge small S3 files and rerun AWS Glue ETL jobs. Most Voted
  E. Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.

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  Explanation/Reference:

  Grouping files together reduces the memory footprint on the Spark driver as well as simplifying file split orchestration. Buffer size increase avoids creating small size files Reference: https://docs.aws.amazon.com/glue/latest/dg/grouping-input-files.html https://docs.aws.amazon.com/glue/latest/dg/grouping-input-files.html

• Question 166:

  A large retailer has successfully migrated to an Amazon S3 data lake architecture. The company's marketing team is using Amazon Redshift and Amazon QuickSight to analyze data, and derive and visualize insights. To ensure the marketing

  team has the most up-to-date actionable information, a data analyst implements nightly refreshes of Amazon Redshift using terabytes of updates from the previous day.

  After the first nightly refresh, users report that half of the most popular dashboards that had been running correctly before the refresh are now running much slower. Amazon CloudWatch does not show any alerts.

  What is the MOST likely cause for the performance degradation?

  A. The dashboards are suffering from inefficient SQL queries.
  B. The cluster is undersized for the queries being run by the dashboards.
  C. The nightly data refreshes are causing a lingering transaction that cannot be automatically closed by Amazon Redshift due to ongoing user workloads.
  D. The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.
  D. The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.

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  Explanation/Reference:

  Correct answer is D as the reason for the drop in query performance might because of the long nightly data refreshes operation which blocked the vacuum operation and left it in the need of the vacuum operation.

  Amazon Redshift can automatically sort and perform a VACUUM DELETE operation on tables in the background. To clean up tables after a load or a series of incremental updates, you can also run the VACUUM command, either against the entire database or against individual tables.

  A vacuum operation might not be able to start if a load or insert operation is already in progress. Vacuum operations temporarily require exclusive access to tables in order to start. This exclusive access is required briefly, so vacuum operations don't block concurrent loads and inserts for any significant period of time.

• Question 167:

  A company wants to use a data lake that is hosted on Amazon S3 to provide analytics services for historical data. The data lake consists of 800 tables but is expected to grow to thousands of tables. More than 50 departments use the tables, and each department has hundreds 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 IAM role for each department. Use AWS Lake Formation based access control to grant each IAM role access to specific tables and columns. Use Amazon Athena to analyze the data.
  B. Create an Amazon Redshift cluster for each department. Use AWS Glue to ingest into the 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's Redshift cluster. Use Amazon Redshift to analyze the data.
  C. Create an IAM role for each department. Use AWS Lake Formation tag-based access control to grant each IAM role access to only the relevant resources. Create LF-tags that are attached to tables and columns. Use Amazon Athena to analyze the data.
  D. Create an Amazon EMR cluster for each department. Configure an IAM service role for each EMR cluster to access relevant S3 files. For each department's users, create an IAM role that provides access to the relevant EMR cluster. Use Amazon EMR to analyze the data.
  C. Create an IAM role for each department. Use AWS Lake Formation tag-based access control to grant each IAM role access to only the relevant resources. Create LF-tags that are attached to tables and columns. Use Amazon Athena to analyze the data.

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  Explanation/Reference:

• Question 168:

  A company uses Amazon Redshift for data analysis. The data is not encrypted at rest. A data analytics specialist must implement a solution to encrypt the data at 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 private information columns in the Amazon Redshift tables to use LZO encoding.
  B. Export data from the existing Amazon Redshift cluster to Amazon S3 by using the UNLOAD command with the ENCRYPTED option. Create a new Amazon Redshift cluster with encryption enabled. Load data into the new cluster by using the COPY command.
  C. Create a manual snapshot of the existing Amazon Redshift cluster. Restore the snapshot into a new Amazon Redshift cluster with encryption enabled.
  D. Modify the existing Amazon Redshift cluster to use AWS Key Management Service (AWS KMS) encryption. Wait for the cluster to finish resizing.
  C. Create a manual snapshot of the existing Amazon Redshift cluster. Restore the snapshot into a new Amazon Redshift cluster with encryption enabled.

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  Explanation/Reference:

• Question 169:

  An invoice tracking application stores invoice images within an Amazon S3 bucket. After invoice images are uploaded, they are accessed often by applications users for 30 days. After 30 days, the invoice images are rarely accessed. The application guarantees uploaded images will never be deleted and will be immediately available upon request by users. The application has 1 million users and 20,000 read requests each second during peak usage.

  Which combination of storage solutions MOST cost-effectively meet these requirements? (Choose two.)

  A. Store the invoice images by using the S3 Standard storage class. Apply a lifecycle policy to transition the images to the S3 Standard-Infrequent Access (S3 Standard-IA) storage class 30 days after upload.
  B. Create one S3 key prefix for each user in the S3 bucket and store the invoice images under the user-specific prefix.
  C. Store the invoice images by using the S3 Standard storage class. Apply a lifecycle policy to transition the images to the S3 Glacier Instant Retrieval storage class 30 days after upload.
  D. Store the invoice images by using the S3 Standard storage class. Apply a lifecycle policy to transition the images to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class 30 days after upload.
  E. Create one S3 key prefix for each day in the S3 bucket and store the invoice images under the upload date-specific prefix.
  B. Create one S3 key prefix for each user in the S3 bucket and store the invoice images under the user-specific prefix.
  D. Store the invoice images by using the S3 Standard storage class. Apply a lifecycle policy to transition the images to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class 30 days after upload.

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  Explanation/Reference:

• Question 170:

  An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool.

  The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: "Creating a connection to your data source timed out."

  How should the data analytics specialist resolve this error?

  A. Grant the SELECT permission on Amazon Redshift tables.
  B. Add the QuickSight IP address range into the Amazon Redshift security group.
  C. Create an IAM role for QuickSight to access Amazon Redshift.
  D. Use a QuickSight admin user for creating the dataset.
  B. Add the QuickSight IP address range into the Amazon Redshift security group.

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  Explanation/Reference:

  Correct answer is B as the error is time out and not permissions denied, the most likely reason is the Redshift Security Group does not allow QuickSight IP address range.

  For Amazon QuickSight to connect to an Amazon Redshift instance, you must create a new security group for that instance. This security group contains an inbound rule authorizing access from the appropriate IP address range for the

  Amazon QuickSight servers in that AWS Region.

  Options A, C and D are wrong as the error is a timeout and not access denied.

  https://docs.aws.amazon.com/quicksight/latest/user/enabling-access-redshift.html