Free Online Amazon MLS-C01 Practice Test

Prepare Your Amazon MLS-C01 Exam Questions with Free online MLS-C01 Practice Test. Get Brilliant AWS Certified Machine Learning - Specialty Exam Results with Valid MLS C01 Exam Dumps.

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Total 367 Questions | Updated On: May 02, 2024

Question 1

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.

Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

A : The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

B : The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

C : The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

D : The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Answer: C

Question 2

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

Question 3

A machine learning (ML) specialist wants to bring a custom training algorithm to Amazon SageMaker. The ML specialist implements the algorithm in a Docker container that is supported by SageMaker. How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?

A : Specify the server argument in the ENTRYPOINT instruction in the Dockerfile

B : Specify the training program in the ENTRYPOINT instruction in the Dockerfile

C : Include the path to the training data in the docker build command when packaging the container.

D : Use a COPY instruction in the Dockerfile to copy the training program to the /opt/ml/train directory

Answer: B

Question 4

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

A : Drop all records from the dataset where age has been set to 0.

B : Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

C : Drop the age feature from the dataset and train the model using the rest of the features.

D : Use k-means clustering to handle missing features.

Answer: B

Question 5

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

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Total 367 Questions | Updated On: May 02, 2024