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QUESTION NO: 1
An organization's data warehouse contains sales data for reporting purposes. data governance policies prohibit staff from accessing the customers' credit card numbers.
How can these policies be adhered to and still allow a Data Scientist to group transactions that use the same credit card number?
A. Store a cryptographic hash of the credit card number.
B. Mask the credit card numbers to only show the last four digits of the credit card number.
C. Encrypt the credit card number with a symmetric encryption key, and give the key only to the authorized Data Scientist.
D. Encrypt the credit card number with an asymmetric encryption key and give the decryption key only to the authorized Data Scientist.
Answer: A
QUESTION NO: 2
True or False: When you add a rule to a DB security group, you do not need to specify port number or protocol.
A. TRUE
B. FALSE
C. Depends on the RDMS used
Answer: A
QUESTION NO: 3
To help you manage your Amazon EC2 instances, images, and other Amazon EC2 resources, you can assign your own metadata to each resource in the form of____________
A. functions
B. special filters
C. wildcards
D. tags
Answer: D
QUESTION NO: 4
Does Amazon RDS allow direct host access via Telnet, Secure Shell (SSH), or Windows Remote
Desktop Connection?
A. No
B. Yes
C. Depends on if it is in VPC or not
Answer: A
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