MLA-C01 Practice Questions
AWS Certified Machine Learning Engineer - Associate
Last Update 4 days ago
Total Questions : 241
Dive into our fully updated and stable MLA-C01 practice test platform, featuring all the latest AWS Certified Associate exam questions added this week. Our preparation tool is more than just a Amazon Web Services study aid; it's a strategic advantage.
Our free AWS Certified Associate practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about MLA-C01. Use this test to pinpoint which areas you need to focus your study on.
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model ' s accuracy in the LEAST amount of time?
A company wants to improve its customer retention ML model. The current model has 85% accuracy and a new model shows 87% accuracy in testing. The company wants to validate the new model’s performance in production.
Which solution will meet these requirements?
A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.
Which solution will meet this requirement?
An ML engineer is training an XGBoost regression model in Amazon SageMaker AI. The ML engineer conducts several rounds of hyperparameter tuning with random grid search. After these rounds of tuning, the error rate on the test hold-out dataset is much larger than the error rate on the training dataset.
The ML engineer needs to make changes before running the hyperparameter grid search again.
Which changes will improve the model ' s performance? (Select TWO.)
An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time.
Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent.
Which solution will meet this requirement?
A company is using ML to predict the presence of a specific weed in a farmer ' s field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?
A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible.
Which SageMaker AI feature should the company use?
A company needs to update the model definition of an existing Amazon SageMaker Al endpoint.
Select and order the correct steps from the following list to update the model definition settings with the LEAST interruption of inferences. Select each step one time or not
at all. (Select and order THRE
E.
)Create a new endpoint configuration that uses the new model definition.
Create a new model definition with updated settings by using the CreateModel action in the SageMaker AI API.
Delete the endpoint that needs to be updated and recreate the endpoint with the new endpoint configuration.
Delete the IAM role and permissions for the ExecutionRoleArn parameter.
Update the endpoint with the new endpoint configuration.
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account В in the same Region.
Which solution will meet this requirement with the LEAST development effort?
An ML engineer wants to use Amazon SageMaker Data Wrangler to perform preprocessing on a dataset. The ML engineer wants to use the processed dataset to train a classification model. During preprocessing, the ML engineer notices that a text feature has a range of thousands of values that differ only by spelling errors. The ML engineer needs to apply an encoding method so that after preprocessing is complete, the text feature can be used to train the model.
Which solution will meet these requirements?
