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MLS-C01 PDF + Testing Engine



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  • Exam Name: AWS Certified Machine Learning - Specialty
  • Last Update: 22-Jun-2024
  • Questions and Answers: 281
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MLS-C01 Questions and Answers

Question # 1

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?


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.


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.


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.


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.

Question # 2

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?


A naive Bayesian model, since the features are all conditionally independent.


A full Bayesian network, since the features are all conditionally independent.


A naive Bayesian model, since some of the features are statistically dependent.


A full Bayesian network, since some of the features are statistically dependent.

Question # 3

A company uses camera images of the tops of items displayed on store shelves to determine which items

were removed and which ones still remain. After several hours of data labeling, the company has a total of

1,000 hand-labeled images covering 10 distinct items. The training results were poor.

Which machine learning approach fulfills the company’s long-term needs?


Convert the images to grayscale and retrain the model


Reduce the number of distinct items from 10 to 2, build the model, and iterate


Attach different colored labels to each item, take the images again, and build the model


Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

Question # 4

A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.

A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.

Which solution will meet these requirements?


Apply anomaly detection to remove outliers from the training dataset before training.


Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.


Apply normalization to the features of the training dataset before training.


Apply undersampling to the training dataset before training.

Question # 5

A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.

Which action will provide the MOST secure protection?


Remove Amazon S3 access permissions from the SageMaker execution role.


Encrypt the weights of the CNN model.


Encrypt the training and validation dataset.


Enable network isolation for training jobs.

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MLS-C01 Testimonials

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mx  Sarah
posted on 11-Jun-2022
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