An ML engineer is training a random forest model. The current model has 100 trees with a max depth of 10. The model is overfitting the training data. The engineer wants to reduce overfitting while maintaining good performance. Which hyperparameter change is most likely to help?

Decrease the number of trees to 10.

Increase the number of trees to 500.

Decrease the max depth of the trees.

What is the purpose of SageMaker Automatic Model Tuning (AMT)?

To automatically label training data.

To automatically deploy the best model to an endpoint.

To automatically search for the best hyperparameters for a model.

To automatically clean and preprocess data.

AWSCertified Machine Learning Engineer - Associate

Domain 2Objective 2

Task 2.2: Train and Refine Models MLA-C01 Practice Questions (Page 5)

Part of the Content Domain 2: ML Model Development domain, which accounts for 26% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~13–22 in this domain), expect 4–7 from this objective — we provide 43 practice questions to prepare you well beyond it. (estimate)

43questions here

9free pages

16concepts

26%of the exam

16 Concepts in This Objective

Training process elements
Reducing training time
Factors influencing model size
Improving model performance
Regularization techniques
Hyperparameter tuning techniques
Model hyperparameters and effects
Integrating external models into SageMaker
SageMaker built-in algorithms and libraries
SageMaker script mode
Fine-tuning pre-trained models
SageMaker automatic model tuning
Preventing overfitting and underfitting
Ensembling and model combination
Reducing model size
Model version management

Questions 21–25

21application · medium
An ML engineer is training a random forest model. The current model has 100 trees with a max depth of 10. The model is overfitting the training data. The engineer wants to reduce overfitting while maintaining good performance. Which hyperparameter change is most likely to help?
Select an answer first
22foundation · easy
Which hyperparameter tuning technique is generally more efficient when the evaluation of each hyperparameter combination is expensive?
Select an answer first
23foundation · easy
What is the purpose of SageMaker Automatic Model Tuning (AMT)?
Select an answer first
24foundation · easy
Which ensemble technique trains a sequence of models where each new model focuses on the mistakes of the previous ones?
Select an answer first
25foundation · easy
Which technique reduces model size by converting weights to lower-precision data types like float16 or int8?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to MLA-C01

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by AWS. “MLA-C01” is a trademark of its owner, used for identification only.

Task 2.2: Train and Refine Models MLA-C01 Practice Questions (Page 5)

Questions 21–25

An ML engineer is training a random forest model. The current model has 100 trees with a max depth of 10. The model is overfitting the training data. The engineer wants to reduce overfitting while maintaining good performance. Which hyperparameter change is most likely to help?

Which hyperparameter tuning technique is generally more efficient when the evaluation of each hyperparameter combination is expensive?

What is the purpose of SageMaker Automatic Model Tuning (AMT)?

Which ensemble technique trains a sequence of models where each new model focuses on the mistakes of the previous ones?

Which technique reduces model size by converting weights to lower-precision data types like float16 or int8?

Keep studying — Content Domain 2: ML Model Development