Free Amazon MLS-C01 Exam Questions

Become Amazon Certified with updated MLS-C01 exam questions and correct answers

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Total 392 Questions | Updated On: Feb 19, 2026

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Question 1

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

A : rform one-hot encoding on highly correlated features

B : matrix multiplication on highly correlated features.

C : eate a new feature space using principal component analysis (PCA)

D : ply the Pearson correlation coefficient

Answer: C

Question 2

A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.

The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome categories from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.

The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.

Which solution will meet these requirements?

A : Perform classification every month by using supervised learning of the 200 outcome categories based on claim contents.

B : Perform reinforcement learning by using claim IDs and dates. Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month.

C : Perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.

D : Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.

Answer: C

Question 3

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?

A : eate one-hot word encoding vectors.

B : oduce a set of synonyms for every word using Amazon Mechanical Turk.

C : eate word embedding factors that store edit distance with every other word.

D : wnload word embedding's pre-trained on a large corpus.

Answer: D

Question 4

A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

A : nfigure the endpoint to use Amazon Elastic Inference (EI) accelerators.

B : eate a new endpoint configuration with two production variants.

C : nfigure the endpoint to automatically scale with the InvocationsPerInstance metric.

D : ploy a second instance pool to support a blue/green deployment of models.

E : configure the endpoint to use burstable instances.

Answer: B,D

Question 5

While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

A : mensionality reduction

B : ta normalization

C : del regulanzation

D : ta augmentation for the minority class

Answer: D

Page: 1 / 79

Total 392 Questions | Updated On: Feb 19, 2026

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