Practice MLS-C01 Exam Online

Question 1

Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3

The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3

Which approach will provide the information required for further analysis?

A : Use Amazon Comprehend with the transcribed files to build the key topics

B : Use Amazon Translate with the transcribed files to train and build a model for the key topics

C :

Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics

D : Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

Answer: B

Question 2

A company’s data scientist has trained a new machine learning model that performs better on test data than the company’s existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data. The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data. Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

A : Create a new endpoint configuration that includes a production variant for each of the two models.

B : Create a new endpoint configuration that includes two target variants that point to different endpoints.

C : Deploy the new model to the existing endpoint

D : Update the existing endpoint to activate the new model.

E : Update the existing endpoint to use the new endpoint configuration.

Answer: A,C

Question 3

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat

a. The company is worried about data egress and wants an ML engineer to secure the environment.

Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A : Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.

B : Use SCPs to restrict access to SageMaker.

C : Disable root access on the SageMaker notebook instances.

D : Enable network isolation for training jobs and models.

E : Restrict notebook presigned URLs to specific IPs used by the company.

F : Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer: A,B,D

Question 4

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 5

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

A : Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

B : Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

C : Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

D : Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer: C

Name:	AWS Certified Machine Learning - Specialty
Exam Code:	MLS-C01
Certification:	AWS Certified Specialty
Vendor:	Amazon
Total Questions:	367
Last Updated:	May 02, 2024

Amazon MLS-C01