Exams > Amazon > AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01)
AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01)
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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.
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A

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
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B
Many developers want to implement the famous Amazon model that was used to power the ג€People who bought this also bought these itemsג€ 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.
Reference:
https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/

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.
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B

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.
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C
Reference:
https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/? ref=Welcome.AI

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.
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D

Question#6

An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

  • A. Listwise deletion
  • B. Last observation carried forward
  • C. Multiple imputation
  • D. Mean substitution
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C
Reference:
https://worldwidescience.org/topicpages/i/imputing+missing+values.html

Question#7

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

  • A. Create a NAT gateway within the corporate VPC.
  • B. Route Amazon SageMaker traffic through an on-premises network.
  • C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
  • D. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
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A
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf
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Question#8

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

  • A. Initialize the model with random weights in all layers including the last fully connected layer.
  • B. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
  • C. Initialize the model with random weights in all layers and replace the last fully connected layer.
  • D. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
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B

Question#9

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time
Which solution should the agency consider?

  • A. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
  • B. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.
  • C. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non-employees are detected.
  • D. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.
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D
Reference:
https://aws.amazon.com/blogs/machine-learning/video-analytics-in-the-cloud-and-at-the-edge-with-aws-deeplens-and-kinesis-video-streams/

Question#10

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
✑ Profiles for all past and existing customers
✑ Profiles for all past and existing insured pets
✑ Policy-level information
✑ Premiums received
✑ Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

  • A. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
  • B. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
  • C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
  • D. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
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C

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