Free Amazon MLS-C01 Exam Questions

Question 1

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C

Question 2

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C

Question 3

A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.

How should the Specialist address this issue and what is the reason behind it?

A : learning rate should be increased because the optimization process was trapped at a local minimum.

B : dropout rate at the flatten layer should be increased because the model is not generalized enough.

C : dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

D : epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer: D

Question 4

A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification. Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then "Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product." The team is using Amazon Comprehend to train the model and must complete the project as soon as possible. Which functionality of Amazon Comprehend should the team use to meet these requirements?

A : tom classification with multi-class mode

B : tom classification with multi-label mode

C : tom entity recognition

D : ilt-in models

Answer: A

Question 5

A Data Scientist needs to analyze employment dat

a. The dataset contains approximately 10 million

observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices

that income and age distributions are not normal. While income levels shows a right skew as expected, with

fewer individuals having a higher income, the age distribution also show a right skew, with fewer older

individuals participating in the workforce.

Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

A : oss-validation

B : merical value binning

C : gh-degree polynomial transformation

D : garithmic transformation

E : hot encoding

Answer: A,B