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| Section | Weight | Objectives |
|---|---|---|
| ML Solution Monitoring, Maintenance, and Security | 24% | - Optimize and maintain workloads
|
| Deployment and Orchestration of ML Workflows | 22% | - Configure deployment for scalability and availability
|
| Data Preparation for Machine Learning | 28% | - Ensure data integrity and prepare for modeling
|
| ML Model Development | 26% | - Evaluate and analyze model performance
|
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NEW QUESTION # 100
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?
Answer: B
NEW QUESTION # 101
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use.
The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?
Answer: B
NEW QUESTION # 102
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?
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 # 103
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real- time endpoints from the application.
Which action will meet this requirement?
Answer: C
Explanation:
Monitoring bias drift in deployed machine learning models is crucial to ensure fairness and accuracy over time. Amazon SageMaker Clarify provides tools to detect bias in ML models, both during training and after deployment. To monitor bias drift for models deployed to real-time endpoints, an effective approach involves orchestrating SageMaker Clarify jobs using AWS Lambda functions.
Implementation Steps:
* Set Up Data Capture:
* Enable data capture on the SageMaker endpoint to record input data and model predictions. This captured data serves as the basis for bias analysis.
* Develop a Lambda Function:
* Create an AWS Lambda function configured to initiate a SageMaker Clarify job. This function will process the captured data to assess bias metrics.
* Schedule or Trigger the Lambda Function:
* Configure the Lambda function to run on-demand or at scheduled intervals using Amazon CloudWatch Events or EventBridge. This setup allows for regular bias monitoring as per the application's requirements.
* Analyze and Respond to Results:
* After each Clarify job completes, review the generated bias reports. If bias drift is detected, take appropriate actions, such as retraining the model or adjusting data preprocessing steps.
Advantages of This Approach:
* Automation: Utilizing AWS Lambda for orchestrating Clarify jobs enables automated and scalable bias monitoring without manual intervention.
* Cost-Effectiveness: AWS Lambda's serverless nature ensures that you only pay for the compute time consumed during the execution of the function, optimizing resource usage.
* Flexibility: The solution can be tailored to specific monitoring needs, allowing for adjustments in monitoring frequency and analysis parameters.
By implementing this solution, the company can effectively monitor bias drift in real-time, ensuring that the AI application maintains fairness and accuracy throughout its lifecycle.
References:
Bias drift for models in production - Amazon SageMaker
Schedule Bias Drift Monitoring Jobs - Amazon SageMaker
NEW QUESTION # 104
An ML engineer wants to deploy an Amazon SageMaker AI model for inference. The payload sizes are less than 3 MB. Processing time does not exceed 45 seconds. The traffic patterns will be irregular or unpredictable.
Which inference option will meet these requirements MOST cost-effectively?
Answer: A
Explanation:
Amazon SageMaker Serverless Inference is designed for irregular or unpredictable traffic patterns. It automatically provisions and scales compute resources based on request volume and scales down to zero when idle, making it the most cost-effective option.
Serverless inference supports payloads up to 6 MB and request durations up to 60 seconds, which comfortably meets the stated constraints. Customers are billed only for actual compute usage during inference execution, not for idle capacity.
Asynchronous inference is intended for long-running jobs (up to 1 hour) and large payloads (up to 1 GB).
Real-time inference requires always-on instances, increasing cost during idle periods. Batch transform is designed for offline processing.
Therefore, serverless inference is the optimal choice.
NEW QUESTION # 105
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