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A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.
A company's ML engineer is creating a classification model. The ML engineer explores the dataset and notices a column named day_of_week. The column contains the following values: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday.
Which technique should the ML engineer use to convert this column's data to binary values?
The day_of_week feature is a categorical variable with a small, fixed number of unique values and no inherent ordinal relationship. AWS machine learning best practices strongly recommend one-hot encoding for this type of categorical data when preparing features for classification models.
One-hot encoding converts each unique category into a separate binary feature (0 or 1). For example, ''Monday'' becomes a column where Monday = 1 and all other days = 0. This ensures that the ML model does not incorrectly assume a numeric or ordered relationship between categories.
Option B (label encoding) assigns integer values to categories (e.g., Monday = 1, Tuesday = 2). AWS documentation cautions against this approach for nominal data because models may incorrectly infer ordinal meaning, leading to biased or inaccurate predictions.
Option A (binary encoding) is typically used for high-cardinality categorical features to reduce dimensionality. With only seven categories, AWS recommends one-hot encoding for clarity and interpretability.
Option D (tokenization) is used for text processing, such as NLP tasks, and is not appropriate for structured categorical features.
AWS SageMaker feature engineering guidelines emphasize that one-hot encoding is the preferred method for low-cardinality categorical variables in classification models, especially when using algorithms such as logistic regression, neural networks, and tree-based models.
Therefore, Option C is the correct and AWS-aligned choice.
A company has an ML model that is deployed to an Amazon SageMaker AI 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?
AWS recommends shadow testing to evaluate a new model against a production model with minimal operational overhead. Using production variants on a single SageMaker endpoint allows traffic to be routed to multiple models without managing additional endpoints.
With a shadow variant, the new model receives a copy of live traffic but does not affect production responses. Performance metrics such as latency, accuracy, and error rates can be compared directly against the current model using Amazon CloudWatch metrics. This approach is natively supported by Amazon SageMaker Endpoints.
Options A, B, and D introduce unnecessary complexity by requiring additional endpoints, traffic routing infrastructure, or custom code.
Therefore, deploying the new model as a shadow variant on the same endpoint is the most efficient solution.
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?
Amazon SageMaker batch transform is ideal for obtaining inferences from large datasets in an asynchronous manner, as it processes data in batches rather than requiring real-time inputs.
SageMaker Model Monitor allows scheduled monitoring of data quality, detecting shifts in input data characteristics, and generating alerts when changes in data quality occur.
This solution provides a fully managed, efficient way to handle both asynchronous inference and data quality monitoring with minimal operational overhead.
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 ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?
Why Linear Learner?
SageMaker's Linear Learner algorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options for weight balancing across classes.
Linear Learner can capture patterns in the data while being computationally efficient.
Key Features of Linear Learner:
Automatically weights minority and majority classes.
Supports both classification and regression tasks.
Handles interdependencies among features effectively through gradient optimization.
Steps to Implement:
Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
Configure the hyperparameters to enable balanced class weights.
Train the model with the balanced dataset created using SageMaker Data Wrangler.
241 questions covering all exam domains, starting from $20
Exam domains verified against: Official Amazon MLA-C01 exam guide, last checked August 2026.
Ingest and store data using AWS data sources like S3 and streaming services, then transform and engineer features through cleaning, encoding, and scaling techniques. Use SageMaker Data Wrangler and AWS Glue for data exploration and ensure data integrity by addressing class imbalance and applying anonymization and compliance controls.
Sample question from this domain above: Q2
Select appropriate algorithms and AWS AI services for business problems, then train and refine models using techniques like distributed training, early stopping, and hyperparameter tuning. Analyze model performance using metrics such as confusion matrices, F1 scores, ROC curves, and identify overfitting and convergence issues.
Sample question from this domain above: Q5
Select deployment infrastructure by choosing between real-time, batch, and serverless endpoints, then create and script infrastructure using SageMaker, CloudFormation, or AWS CDK. Set up CI/CD pipelines with AWS CodePipeline and CodeBuild to automate model deployment with versioning and rollback strategies.
Monitor model inference for drift using techniques to track data quality and model performance, then optimize infrastructure costs using CloudWatch and X-Ray. Secure ML resources through IAM roles and policies, apply SageMaker security features, and implement network access controls and CI/CD pipeline security.
Sample question from this domain above: Q1
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