Practice MLS-C01 Exam Online

Question 1

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A : The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%

B : The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%

C : The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).

D : The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: B

Question 2

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A : Redeploy the model as a batch transform job on an M5 instance.

B : Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C : Redeploy the model on a P3dn instance.

D : Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

Question 3

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

A : Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

B : Drop the features with low correlation scores by using a Jupyter notebook.

C : Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.

D : Concatenate the features with high correlation scores by using a Jupyter notebook.

Answer: C

Question 4

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?curacy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68?curacy on the training set

67?curacy 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.

Answer: B

Question 5

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 nonemployees

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.

Answer: C

Name:	AWS Certified Machine Learning - Specialty
Exam Code:	MLS-C01
Certification:	AWS Certified Specialty
Vendor:	Amazon
Total Questions:	294
Last Updated:	Apr 23, 2024

Amazon MLS-C01