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             A  W   S   C  e r tified Machine Learning - Specialty
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Question: 1 
   
A large mobile network operating company is building a machine learning model to predict 
customers who are likely to unsubscribe from the service. The company plans to offer an incentive 
for these customers as the cost of churn is far greater than the cost of the incentive. 
The model produces the following confusion matrix after evaluating on a test dataset of 100 
customers: 
 
Based on the model evaluation results, why is this a viable model for production? 
 
A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is 
less than the false positives. 
B. The precision of the model is 86%, which is less than the accuracy of the model. 
C. The model is 86% accurate and the cost incurred by the company as a result of false positives is 
less than the false negatives. 
D. The precision of the model is 86%, which is greater than the accuracy of the model. 
 
Answer: B     
 
Question: 2 
   
A Machine Learning Specialist is designing a system for improving sales for a company. The objective 
is to use the large amount of information the company has on users’ behavior and product 
preferences to predict which products users would like based on the users’ similarity to other users. 
What should the Specialist do to meet this objective? 
 
A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR 
B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR. 
C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR 
D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR 
 
Answer: B     
 
Explanation: 
Many developers want to implement the famous Amazon model that was used to power the “People 
who bought this also bought these items” feature on Amazon.com. This model is based on a method 
called Collaborative Filtering. It takes items such as movies, books, and products that were rated 
highly by a set of users and recommending them to other users who also gave them high ratings. This 
method works well in domains where explicit ratings or implicit user actions can be gathered and 
analyzed. 
 
Question: 3 
   
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's 
operations using Amazon Athena and Amazon S3. 
The source systems send data in .CSV format in real time. The Data Engineering team wants to 
transform the data to the Apache Parquet format before storing it on Amazon S3. 
Which solution takes the LEAST effort to implement? 
 
A. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 
to serialize data as Parquet. 
B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into 
Parquet. 
C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use 
Apache Spark to convert data into Parquet. 
D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to 
convert data into Parquet. 
 
Answer: C     
 
Question: 4 
   
A city wants to monitor its air quality to address the consequences of air pollution. A Machine 
Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 
days in the city. As this is a prototype, only daily data from the last year is available. 
Which model is MOST likely to provide the best results in Amazon SageMaker? 
 
A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series 
consisting of the full year of data with a predictor_type of regressor. 
B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full 
year of data. 
C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the 
full year of data with a predictor_type of regressor. 
D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the 
full year of data with a predictor_type of classifier. 
 
Answer: C     
 
Question: 5 
   
A Data Engineer needs to build a model using a dataset containing customer credit card information. 
How can the Data Engineer ensure the data remains encrypted and the credit card information is 
secure? 
 
 
A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon 
SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card 
numbers. 
B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to 
automatically discard credit card numbers and insert fake credit card numbers. 
C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the 
SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to 
reduce the length of the credit card numbers. 
D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit 
card numbers from the customer data with AWS Glue. 
 
Answer: C     
 
Question: 6 
   
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet 
of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook 
instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML 
Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 
instance within the VPC. 
Why is the ML Specialist not seeing the instance visible in the VPC? 
 
A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer 
account, but they run outside of VPCs. 
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer 
accounts. 
C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service 
accounts. 
D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS 
service accounts. 
 
Answer: C