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All MLS-C01 Test Inside Amazon Web Services Questions

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Total 322 questions

AWS Certified Machine Learning - Specialty Questions and Answers

Question 41

An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items

A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.

How should the data scientist meet these requirements MOST cost-effectively?

Options:

A.

Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:accuracy", "Type": "Maximize"}}

B.

Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.

C.

Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.

D.

Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Minimize"}).

Question 42

A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.

A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.

Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Select THREE.)

Options:

A.

Define the feature variables and target variable for the churn prediction model.

B.

Use the SQL EXPLAIN_MODEL function to run predictions.

C.

Write a CREATE MODEL SQL statement to create a model.

D.

Use Amazon Redshift Spectrum to train the model.

E.

Manually export the training data to Amazon S3.

F.

Use the SQL prediction function to run predictions,

Question 43

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data

Which type of data repository is the MOST cost-effective solution?

Options:

A.

An Amazon EBS-backed Amazon EC2 instance with hourly directories

B.

An Amazon RDS database with hourly table partitions

C.

An Amazon S3 data lake with hourly object prefixes

D.

An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

Question 44

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

Options:

A.

Linear regression is inappropriate. The residuals do not have constant variance.

B.

Linear regression is inappropriate. The underlying data has outliers.

C.

Linear regression is appropriate. The residuals have a zero mean.

D.

Linear regression is appropriate. The residuals have constant variance.

Page: 11 / 23
Total 322 questions