AWS Certified Machine Learning Specialty (MLS-C01)

The AWS Certified Machine Learning Specialty (MLS-C01) were last updated on today.

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Viewing questions 36-40 out of 285 questions

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Topic 1 - Exam A

Question #36 Topic 1

A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS. How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

A Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
B ׀¡onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
C Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
D Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

Suggested Answer: D
NOTE: Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html

Question #37 Topic 1

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?

A Logistic regression
B Linear regression
C K-means
D Principal component analysis (PCA)

Suggested Answer: B
NOTE:

Question #38 Topic 1

A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices. Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

A Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
B Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
C Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
D Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

Suggested Answer: D
NOTE:

Question #39 Topic 1

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users. Which storage option provides the most processing flexibility and will allow access control with IAM?

A Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
B Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
C Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
D Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

Suggested Answer: C
NOTE:

Question #40 Topic 1

A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset. The company requires at least 85% accuracy to make use of the model. After an exhaustive grid search, the optimal hyperparameters produced the following: ✑ 68% accuracy on the training set ✑ 67% accuracy on the validation set What can the machine learning specialist do to improve the system's accuracy?

A Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
B Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
D Train a new model using the current neural network architecture.

Suggested Answer: B
NOTE: