Amazon MLS-C01 Exam Questions - Navigate Your Path to Success

The Amazon AWS Certified Machine Learning - Specialty (MLS-C01) exam is a good choice for Data Scientists Machine Learning Developers and if the candidate manages to pass Amazon AWS Certified Machine Learning - Specialty exam, he/she will earn Amazon Specialty, Amazon AWS Certified Machine Learning Certifications. Below are some essential facts for Amazon MLS-C01 exam candidates:

In actual Amazon AWS Certified Machine Learning - Specialty (MLS-C01) exam, a candidate can expect 65 Questions and the officially allowed time is expected to be around 180 Minutes.
TrendyCerts offers 307 Questions that are based on actual Amazon MLS-C01 syllabus.
Our Amazon MLS-C01 Exam Practice Questions were last updated on: Mar 11, 2025

Sample Questions for Amazon MLS-C01 Exam Preparation

Question 1

A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.

What should the engineer do to improve the validation accuracy of the model?

APerform stratified sampling on the original dataset.

BAcquire additional data about the majority classes in the original dataset.

CUse a smaller, randomly sampled version of the training dataset.

DPerform systematic sampling on the original dataset.

Correct : A

Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability. Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.

The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.

References:

* Stratified Sampling for Imbalanced Datasets

* Imbalanced Data

* Tour of Data Sampling Methods for Imbalanced Classification

Options Selected by Other Users:

Question 2

A data scientist is trying to improve the accuracy of a neural network classification model. The data scientist wants to run a large hyperparameter tuning job in Amazon SageMaker.

However, previous smaller tuning jobs on the same model often ran for several weeks. The ML specialist wants to reduce the computation time required to run the tuning job.

Which actions will MOST reduce the computation time for the hyperparameter tuning job? (Select TWO.)

AUse the Hyperband tuning strategy.

BIncrease the number of hyperparameters.

CSet a lower value for the MaxNumberOfTrainingJobs parameter.

DUse the grid search tuning strategy

ESet a lower value for the MaxParallelTrainingJobs parameter.

Correct : A, C

The Hyperband tuning strategy is a multi-fidelity based tuning strategy that dynamically reallocates resources to the most promising hyperparameter configurations. Hyperband uses both intermediate and final results of training jobs to stop under-performing jobs and reallocate epochs to well-utilized hyperparameter configurations. Hyperband can provide up to three times faster hyperparameter tuning compared to other strategies1. Setting a lower value for the MaxNumberOfTrainingJobs parameter can also reduce the computation time for the hyperparameter tuning job by limiting the number of training jobs that the tuning job can launch. This can help avoid unnecessary or redundant training jobs that do not improve the objective metric.

The other options are not effective ways to reduce the computation time for the hyperparameter tuning job. Increasing the number of hyperparameters will increase the complexity and dimensionality of the search space, which can result in longer computation time and lower performance. Using the grid search tuning strategy will also increase the computation time, as grid search methodically searches through every combination of hyperparameter values, which can be very expensive and inefficient for large search spaces. Setting a lower value for the MaxParallelTrainingJobs parameter will reduce the number of training jobs that can run in parallel, which can slow down the tuning process and increase the waiting time.

References:

* How Hyperparameter Tuning Works

* Best Practices for Hyperparameter Tuning

* HyperparameterTuner

* Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband