Amazon Web Services Updated AIF-C01 Exam Questions and Answers by jace

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Prompt engineering is the primary method for controlling tone, style, and behavior of foundation model responses.

AWS generative AI guidance explains that:

Prompts can define tone, voice, and response structure

Iterative refinement ensures consistent outputs

Prompt refinement requires no model retraining

Why the other options are incorrect:

Token limits (A) affect length, not tone.

Batch inferencing (B) affects processing mode, not response style.

Higher temperature (D) increases randomness, reducing consistency.

AWS AI document references:

Prompt Engineering Best Practices

Controlling Model Output Tone

Using Prompts with Foundation Models on AWS

Comprehensive and Detailed Explanation (AWS AI documents):

According to AWS Generative AI and Foundation Model guidance, foundation models are pre-trained on very large and diverse datasets, enabling them to perform a wide range of tasks such as classification, summarization, labeling, and content generation without task-specific training.

For labeling online news articles, a foundation model can be used out of the box or with minimal prompt engineering, whereas a conventional ML model would typically require:

Task-specific labeled training data

Model training and validation cycles

Ongoing retraining as content evolves

Why the other options are incorrect:

B. Smaller and faster – Foundation models are generally larger, not smaller, than conventional ML models.

C. More transparent – FMs are often less transparent due to their size and complexity.

D. Not biased – Foundation models can still inherit biases from training data; bias mitigation is required.

AWS AI Study Guide References:

AWS Generative AI fundamentals

AWS Foundation Model characteristics and benefits

Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and predictionquality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.

Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.

Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.

Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.

Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.

AWS AI Practitioner References:

Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.