Free Amazon MLS-C01 Exam Questions

Question 1

A bank wants to use a machine learning (ML) model to predict if users will default on credit card payments. The

training data consists of 30,000 labeled records and is evenly balanced between two categories. For the model,

an ML specialist selects the Amazon SageMaker built-in XGBoost algorithm and configures a SageMaker

automatic hyperparameter optimization job with the Bayesian method. The ML specialist uses the validation

accuracy as the objective metric.

When the bank implements the solution with this model, the prediction accuracy is 75%. The bank has given

the ML specialist 1 day to improve the model in production.

Which approach is the FASTEST way to improve the model's accuracy?

A : n a SageMaker incremental training based on the best candidate from the current model's tuning job. Monitor the same metric that was used as the objective metric in the previous tuning, and look for improvements

B : t the Area Under the ROC Curve (AUC) as the objective metric for a new SageMaker automatic hyperparameter tuning job. Use the same maximum training jobs parameter that was used in the previous tuning job.

C : n a SageMaker warm start hyperparameter tuning job based on the current model’s tuning job. Use the same objective metric that was used in the previous tuning.

D : t the F1 score as the objective metric for a new SageMaker automatic hyperparameter tuning job. Double the maximum training jobs parameter that was used in the previous tuning job.

Answer: A

Question 2

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 3

A geospatial analysis company processes thousands of new satellite images each day to produce vessel detection data for commercial shipping. The company stores the training data in Amazon S3. The training data incrementally increases in size with new images each day. The company has configured an Amazon SageMaker training job to use a single ml.p2.xlarge instance with File input mode to train the built-in Object Detection algorithm. The training process was successful last month but is now failing because of a lack of storage. Aside from the addition of training data, nothing has changed in the model training process. A machine learning (ML) specialist needs to change the training configuration to fix the problem. The solution must optimize performance and must minimize the cost of training. Which solution will meet these requirements?

A : dify the training configuration to use two ml.p2.xlarge instances

B : dify the training configuration to use Pipe input mode.

C : dify the training configuration to use a single ml.p3.2xlarge instance

D : dify the training configuration to use Amazon Elastic File System (Amazon EFS) instead of Amazon S3 to store the input training data.

Answer: C

Question 4

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training dataset is missing values for the target variable. The company stores the training dataset as JSON files.

The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset.

Which approach will meet these requirements with the LEAST development effort?

A : Impute the missing values by using the linear regression method. Use the entire dataset and the imputed values to train the DeepAR model.

B : Replace the missing values with not a number (NaN). Use the entire dataset and the encoded missing values to train the DeepAR model.

C : Impute the missing values by using a forward fill. Use the entire dataset and the imputed values to train the DeepAR model.

D : Impute the missing values by using the mean value. Use the entire dataset and the imputed values to train the DeepAR model.

Answer: D

Question 5

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model. Which solution will meet these requirements with the LEAST development effort?

A : SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

B : AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report

C : SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

D : Use custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.

Answer: A