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AWS-Big-Data-Specialty PDF DEMO:

QUESTION NO: 1
What does Amazon ELB stand for?
A. Elastic Linux Box.
B. Elastic Load Balancing.
C. Encrypted Load Balancing.
D. Encrypted Linux Box.
Answer: B

QUESTION NO: 2
Are you able to integrate a multi-factor token service with the AWS Platform?
A. Yes, you can integrate private multi-factor token devices to authenticate users to the AWS platform.
B. Yes, using the AWS multi-factor token devices to authenticate users on the AWS platform.
C. No, you cannot integrate multi-factor token devices with the AWS platform.
Answer: B

QUESTION NO: 3
A sys admin is planning to subscribe to the RDS event notifications. For which of the below mentioned source categories the subscription cannot be configured?
A. DB security group
B. DB parameter group
C. DB snapshot
D. DB options group
Answer: D

QUESTION NO: 4
An organization is setting up a data catalog and metadata management environment for their numerous data stores currently running on AWS. The data catalog will be used to determine the structure and other attributes of data in the data stores. The data stores are composed of Amazon
RDS databases, Amazon Redshift, and CSV files residing on Amazon S3. The catalog should be populated on a scheduled basis, and minimal administration is required to manage the catalog.
How can this be accomplished?
A. Use AWS Glue Data Catalog as the data catalog and schedule crawlers that connect to data sources to populate the database.
B. Set up Amazon DynamoDB as the data catalog and run a scheduled AWS Lambda function that connects to data sources to populate the database.
C. Use an Amazon database as the data catalog and run a scheduled AWS Lambda function that connects to data sources to populate the database.
D. Set up Apache Hive metastore on an Amazon EC2 instance and run a scheduled bash script that connects to data sources to populate the metastore.
Answer: A
Explanation
https://docs.aws.amazon.com/glue/latest/dg/populate-data-catalog.html

QUESTION NO: 5
A telecommunications company needs to predict customer churn (i.e. customers eho decide to switch a computer). The company has historic records of each customer, including monthly consumption patterns, calls to customer service, and whether the customer ultimately quit th eservice. All of this data is stored in Amazon S3. The company needs to know which customers are likely going to churn soon so that they can win back their loyalty.
What is the optimal approach to meet these requirements?
A. Use the Amazon Machine Learning service to build the binary classification model based on the dataset stored in Amazon S3. The model will be used regularly to predict churn attribute for existing customers
B. Use EMR to run the Hive queries to build a profile of a churning customer. Apply the profile to existing customers to determine the likelihood of churn
C. Use AWS QuickSight to connect it to data stored in Amazon S3 to obtain the necessary business insight. Plot the churn trend graph to extrapolate churn likelihood for existing customer
D. Use a Redshift cluster to COPY the data from Amazon S3. Create a user Define Function in Redshift that computers the likelihood of churn
Answer: A
Explanation
https://aws.amazon.com/blogs/machine-learning/predicting-customer-churn-with-amazon-machine- learning/

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Updated: May 28, 2022

by RHC.

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