Amazon DAS-C01 Exam Questions Instant Access

Question 1

A company collects data from parking garages. Analysts have requested the ability to run reports 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 Amazon Redshift cluster. The solution must alert operations personnel when the number of vehicles in a particular garage exceeds a specific threshold. The alerting query will use garage threshold values as a static reference. The threshold values are stored in

Amazon S3.

What is the MOST operationally efficient solution that meets these requirements?

AUse an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

BUse an Amazon Kinesis data stream to collect the data. Use an Amazon Kinesis Data Firehose delivery stream to deliver the data to Amazon Redshift. Create another Kinesis data stream to temporarily store the threshold values from Amazon S3. Send the delivery stream and the second data stream to Amazon Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an C. Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

CUse an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Automatically initiate an AWS Lambda function that queries the data in Amazon Redshift. Configure the Lambda function to compare the number of vehicles in a particular garage to the corresponding threshold 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.

DUse an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Use Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value that is stored in a table as an in-application stream. Configure an AWS Lambda function as an output for the application to publish an Amazon Simple Queue Service (Amazon SQS) notification if the number of vehicles exceeds the threshold.

Answer : A

This solution meets the requirements because:

It uses Amazon Kinesis Data Firehose to collect and deliver data to Amazon Redshift in near real time, without requiring any coding or server management1.

It uses Amazon Kinesis Data Analytics to process and analyze streaming data using SQL queries or Apache Flink applications2.It can also create a reference data source that allows joining streaming data with static data stored in Amazon S33. This way, it can compare the number of vehicles in each garage with the corresponding threshold value from the reference data source.

It uses AWS Lambda to create a serverless function that can be triggered by Kinesis Data Analytics as an output destination4.The Lambda function can then publish an Amazon SNS notification to alert operations personnel when the number of vehicles exceeds the threshold5.

Question 2

A manufacturing company is storing data from its operational systems in Amazon S3. The company's business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athen

a. The company needs to access the Athena service from the on-premises network by using a JDBC connection. The company has created a VPC. Security policies mandate that requests to AWS services cannot traverse the internet.

Which combination of steps should a data analytics specialist take to meet these requirements? (Select TWO.)

AEstablish an AWS Direct Connect connection between the on-premises network and the VPC.

BConfigure the JDBC connection to connect to Athena through Amazon API Gateway.

CConfigure the JDBC connection to use a gateway VPC endpoint for Amazon S3.

DConfigure the JDBC connection to use an interface VPC endpoint for Athena.

EDeploy Athena within a private subnet.

Answer : A, D

AWS Direct Connect is a service that establishes a dedicated network connection between your on-premises network and AWS1.It can help you reduce network costs, increase bandwidth throughput, and provide a more consistent network experience than internet-based connections1. It can also help you meet the security policy that requires requests to AWS services not to traverse the internet.

An interface VPC endpoint is a type of VPC endpoint that enables you to privately connect your VPC to supported AWS services and VPC endpoint services powered by AWS PrivateLink2.It is represented by one or more Elastic Network Interfaces (ENIs) with private IP addresses in your VPC subnets2. It can also help you meet the security policy that requires requests to AWS services not to traverse the internet.

Amazon Athena now provides an interface VPC endpoint that allows you to connect directly to Athena through an interface VPC endpoint in your VPC3.You can create an interface VPC endpoint to connect to Athena using the AWS console or AWS CLI commands4.You can also configure the JDBC connection to use the interface VPC endpoint for Athena by specifying the endpoint URL as the JDBC URL5.

Question 3

An online retailer is rebuilding its inventory management system and inventory reordering system to automatically reorder products by using Amazon Kinesis Data Streams. The inventory management system uses the Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Kinesis Client Library (KCL) to consume data from the stream. The stream has been configured to scale as needed. Just before production deployment, the retailer discovers that the inventory reordering system is receiving duplicated data.

Which factors could be causing the duplicated data? (Choose two.)

AThe producer has a network-related timeout.

BThe stream's value for the IteratorAgeMilliseconds metric is too high.

CThere was a change in the number of shards, record processors, or both.

DThe AggregationEnabled configuration property was set to true.

EThe max_records configuration property was set to a number that is too high.

Answer : B, D

Question 4

A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.

Which solution meets these requirements?

AUse an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

BCreate a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.

CCreate an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.

DCreate an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Answer : A

https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-quicksight/

Question 5

A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.

The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.

Which solution will meet these requirements?

AUse an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.

BUse Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.

CUse a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.

DUse calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

Answer : A

Question 6

A business intelligence (Bl) engineer must create a dashboard to visualize how often certain 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 Amazon Redshift table. The table displays the keywords and the count corresponding

to each keyword.

The Bl engineer needs to display the top keywords with more emphasis on the most frequently used keywords.

Which visual type in Amazon QuickSight meets these requirements?

ABar charts

BWord clouds

CCircle packing

DHeat maps

Answer : B

Question 7

A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (Pll). Only authorized users should be able to see

plaintext PII data.

What is the MOST operationally efficient solution that meets these requirements?

ADefine a bucket policy for each S3 bucket of the data lake to allow access to users who have authorization to see PII data. Catalog the data by using AWS Glue. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role.

BRegister the S3 locations with AWS Lake Formation. Create two IAM roles. Use Lake Formation 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.

CRegister the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an E TL workflow that removes the Pll columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register the new S3 locations with Lake Formation. Grant users the permissions to each data lake data based on whether the users are authorized to see PII data.

DRegister the S3 locations with AWS Lake Formation. Create two IAM roles. Attach a permissions policy with access to Pll columns to one role. Attach a policy without these permissions to the other role. For each downstream analytics service, use its native security functionality and the IAM roles to secure the Pll data.

Answer : B

This solution meets the requirements because:

AWS Lake Formation is a fully managed service that allows you to build, secure, and manage data lakes on AWS1.You can use Lake Formation to register your S3 locations as data sources and catalog your data using AWS Glue1.

AWS Lake Formation provides fine-grained data permissions that enable you to control access to your data at the column or row level1.You can use Lake Formation to create two IAM roles and grant them different Select permissions based on the PII status of the columns1.

AWS Lake Formation integrates with various analytics services from AWS, such as Amazon Athena, Amazon Redshift, Amazon EMR, and Amazon QuickSight1.You can use these services to query and visualize your data in S3 using the IAM roles and permissions defined by Lake Formation1.

Amazon AWS Certified Data Analytics - Specialty DAS-C01 Exam Questions