Amazon Web Services AIF-C01 - AWS Certified AI Practitioner Exam

Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.

Adjust the Prompt:

Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.

A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish."

Why Option A is Correct:

Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.

Flexibility: Prompt engineering is a flexible approach to refining the model’s behavior without modifying the model itself.

Why Other Options are Incorrect:

B. Choose an LLM of a different size: The model size does not directly impact the response length or language.

C. Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.

D. Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.

Amazon CloudWatch is designed for real-time monitoring of applications and infrastructure. It supports metrics and logs for ML model performance and resource utilization. According to the AWS Certified AI Practitioner Study Guide:

“Amazon CloudWatch is a monitoring service that provides data and actionable insights to monitor your ML workloads and applications in real time, ensuring performance and scalability.”

An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.

Anomaly Detection:

Anomaly detection uses machine learning algorithms to identify deviations from normal behavior, such as unexpected traffic from a suspicious IP address.

This is a common approach for identifying potential threats or malicious activity in cybersecurity applications.

Why Option C is Correct:

Detects Suspicious Behavior: An anomaly detection system can monitor and detect IP addresses that exhibit unusual or suspicious patterns.

Real-time Monitoring: Provides continuous analysis of network traffic to identify potential security threats.

Why Other Options are Incorrect:

A. Speech recognition system: Is unrelated to detecting suspicious IP addresses.

B. NLP named entity recognition: Focuses on identifying entities in text, not IP address analysis.

D. Fraud forecasting system: Generally used for predicting fraud, not directly applicable to identifying suspicious IPs.

Thus, C is the correct answer for detecting suspicious IP addresses.

To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.

A. Include Fairness Metrics for Model Evaluation:

Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.

These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.

C. Modify the Training Data to Mitigate Bias:

Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.

Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.

Why Other Options are Incorrect:

B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.

D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.

E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.

Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.

Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.

Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.

Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

AWS AI Practitioner References:

Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

Amazon Bedrock Knowledge Bases enable Retrieval Augmented Generation (RAG) by letting you connect external company data sources to your foundation model in a serverless, cost-effective manner—without retraining or fine-tuning. This allows the model to answer questions and generate content grounded in your own documents or data.

A is correct:

“Knowledge Bases for Amazon Bedrock enable generative AI applications to retrieve and include your company’s information for more contextual responses, without the need for expensive retraining or custom models.”

(Reference: Amazon Bedrock Knowledge Bases)

B is not cost-effective or guaranteed to add your company’s context.

C (Agents) are for orchestrating workflows, not specifically RAG/context.

D (Custom model deployment) is costly and unnecessary for just adding context.

The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.

Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.

Amazon SageMaker Clarify:

It helps in identifying potential bias in the data and model, and also explains model behavior by generating feature attributions, providing insights into which features are most influential in the model's predictions.

These capabilities are critical in medical applications where regulatory compliance often mandates transparency and explainability to ensure that decisions made by the model can be trusted and audited.

Why Option B is Correct:

Transparency and Explainability: SageMaker Clarify is explicitly designed to provide insights into machine learning models' decision-making processes, helping meet regulatory requirements by explaining why a model made a particular prediction.

Compliance with Regulations: The tool is suitable for use in sensitive domains, such as healthcare, where there is a need for explainable AI.

Why Other Options are Incorrect:

A. Amazon Inspector: Focuses on security assessments, not on explainability or model transparency.

C. Amazon Macie: Provides data security by identifying and protecting sensitive data, but does not help in making models explainable.

D. Amazon Rekognition: Used for image and video analysis, not relevant to making models explainable.

Thus, B is the correct answer for meeting transparency and explainability requirements for the foundation model

The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.

Exact Extract from AWS AI Documents:

From the Amazon SageMaker Developer Guide:

"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes."

(Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics)

Detailed Explanation:

Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model’s ability to correctly identify both churners and non-churners.

Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.

Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.

Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model’s accuracy or effectiveness.

The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it easy for users to understand and modify these weights. This interpretability and adjustability make it suitable for the requirements.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Logistic regression is a supervised learning algorithm used for classification tasks. It is highly interpretable, as it assigns weights to each feature, allowing users to understand and adjust the importance of different variables based on domain expertise."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Algorithms)

Detailed Explanation:

Option A: Logistic regression modelThis is the correct answer. Logistic regression provides interpretable coefficients (weights) for each feature, enabling employees to view and adjust them based on domain knowledge, meeting the application’s requirements.

Option B: Deep learning model built on principal componentsDeep learning models, even when using principal components, are complex and lack interpretability. The weights in such models are not easily adjustable by users, making this option unsuitable.

Option C: K-nearest neighbors (k-NN) modelk-NN is a non-parametric model that does not assign explicit weights to features. It relieson distance metrics, which are not easily adjustable based on domain knowledge, so it does not meet the requirements.

Option D: Neural networkNeural networks are highly complex and lack interpretability, as their weights are not directly tied to input features in a human-understandable way. Adjusting weights based on domain knowledge is impractical, making this option incorrect.