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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

A. Apply random data splitting on the dataset.
B. Apply random oversampling on the dataset.
C. Reduce the size of the dataset.
D. Transform some of the images in the dataset.

Answer: B

NEW QUESTION # 28
A company has an ML model that is deployed to an Amazon SageMaker endpoint for real-time inference. The company needs to deploy a new model. The company must compare the new model's performance to the currently deployed model's performance before shifting all traffic to the new model. Which solution will meet these requirements with the LEAST operational effort?

A. Deploy the new model to a separate endpoint. Manually split traffic between the two endpoints.
B. Deploy the new model as a shadow variant on the same endpoint as the current model. Route a portion of live traffic to the shadow model for evaluation.
C. Deploy the new model to a separate endpoint. Use Amazon CloudFront to distribute traffic between the two endpoints.
D. Use AWS Lambda functions with custom logic to route traffic between the current model and the new model.

Answer: B

Explanation:
SageMaker supports shadow variant deployments, which allow a new model to run alongside the current one on the same endpoint. A portion of live traffic is mirrored to the shadow model for evaluation, while only the current model's output is returned to users. This provides the required comparison with minimal operational effort, avoiding the need for custom traffic-splitting solutions.

NEW QUESTION # 29
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::Pipeline
B. AWS::SageMaker::NotebookInstance
C. AWS::SageMaker::Model
D. AWS::SageMaker::Endpoint

Answer: C

NEW QUESTION # 30
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

A. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
B. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
C. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.
D. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.

Answer: B

Explanation:
The requirement is to quickly determine whether the target variable is predictable, with minimal development effort. Amazon SageMaker Autopilot is specifically designed for this purpose.
SageMaker Autopilot automatically handles data preprocessing, feature engineering, algorithm selection, model training, and evaluation. It generates multiple candidate models and provides detailed performance metrics, allowing teams to quickly assess predictability without writing custom code.
Option B requires significant manual development. Option C is incorrect because Amazon Macie is a data security and classification service, not an ML modeling service. Option D is unsuitable because Amazon Bedrock models are not intended for structured tabular prediction tasks.
Therefore, SageMaker Autopilot provides the fastest and least effort solution.

NEW QUESTION # 31
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?

A. Use AWS Glue to transform the numerical data into categorical data.
B. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
C. Use AWS Glue to transform the categorical data into numerical data.
D. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

Answer: D

Explanation:
Preparing a training dataset that includes both categorical and numerical data is essential for maximizing the accuracy of a machine learning model. Transforming categorical data into numerical format is a critical step, as most ML algorithms require numerical input.
Why Transform Categorical Data into Numerical Data?
* Model Compatibility: Many ML algorithms cannot process categorical data directly and require numerical representations.
* Improved Performance: Proper encoding of categorical variables can enhance model accuracy and convergence speed.
Why Use Amazon SageMaker Data Wrangler?
Amazon SageMaker Data Wrangler offers a visual interface with over 300 built-in data transformations, including tools for encoding categorical variables.
Implementation Steps:
* Import Data:
* Load the dataset into SageMaker Data Wrangler from sources like Amazon S3 or on-premises databases.
* Identify Categorical Features:
* Use Data Wrangler's data type inference to detect categorical columns.
* Apply Categorical Encoding:
* Choose appropriate encoding techniques (e.g., one-hot encoding or ordinal encoding) from Data Wrangler's transformation options.
* Apply the selected transformation to convert categorical features into numerical format.
* Validate Transformations:
* Review the transformed dataset to ensure accuracy and completeness.
Advantages of Using SageMaker Data Wrangler:
* Ease of Use: Provides a user-friendly interface for data transformation without extensive coding.
* Operational Efficiency: Integrates data preparation steps, reducing the need for multiple tools and minimizing operational overhead.
* Flexibility: Supports various data sources and transformation techniques, accommodating diverse datasets.
By utilizing SageMaker Data Wrangler to transform categorical data into numerical format, the ML engineer can efficiently prepare the dataset, thereby enhancing the model's accuracy with minimal operational overhead.
References:
* Transform Data - Amazon SageMaker
* Prepare ML Data with Amazon SageMaker Data Wrangler

NEW QUESTION # 32
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