Free Amazon MLS-C01 Exam Questions

Question 1

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

A : plement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

B : AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

C : plement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

D : AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Answer: B

Question 2

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

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

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

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

Answer: C

Question 3

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

A : model is 86?curate and the cost incurred by the company as a result of false negatives is less than the false positives.

B : precision of the model is 86%, which is less than the accuracy of the model.

C : model is 86?curate and the cost incurred by the company as a result of false positives is less than the false negatives.

D : precision of the model is 86%, which is greater than the accuracy of the model.

Answer: A

Question 4

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 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