Amazon AWS Certified AI Practitioner AIF-C01 Exam
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Free Amazon AWS Certified AI Practitioner AIF-C01 Exam Questions
Domain: Fundamentals of Gen AI
You are an AI Engineer tasked with building and deploying a Natural Language Processing (NLP) application using a pre-trained foundation model (FM) on AWS. Which stage involves adapting the pre-trained model to your specific task by training it on a smaller, task-specific dataset
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Model Selection
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Pre-training
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Fine-tuning
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Evaluation
Explanation
Correct Answer C. Fine-tuning
Explanation of Correct Answer:
C. Fine-tuning: Fine-tuning is the process of taking a pre-trained foundation model and training it further on a smaller, task-specific dataset. This allows the model to adapt to the nuances and requirements of a particular application while retaining the general knowledge it learned during pre-training. Fine-tuning is especially useful in NLP tasks like sentiment analysis, summarization, or question answering, where task-specific performance is critical.
Domain: Security, Compliance, and Governance for AI Solutions
You are a data scientist working for an e-commerce company. You've built multiple models using Amazon SageMaker to predict customer churn. Which metric is useful for evaluating the trade-off between true positive rate and false positive rate in a classification model
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Accuracy
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AUC (Area Under the ROC Curve)
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F1 score
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Mean Absolute Error (MAE)
Explanation
Correct Answer B. AUC (Area Under the ROC Curve)
Explanation of the Correct Answer:
B. AUC (Area Under the ROC Curve): AUC is a performance measurement for classification models at various threshold settings. It evaluates how well the model distinguishes between classes by plotting the true positive rate (sensitivity) against the false positive rate. AUC provides a single number summarizing the model's ability to balance false positives and true positives across thresholds, making it ideal for customer churn prediction.
Domain: Fundamentals of Gen AI
You have trained a generative AI model on Amazon SageMaker to summarize customer support conversations. You need to evaluate the model's performance and ensure the generated summaries are accurate and informative. Which evaluation metric is commonly used for assessing the quality of text summarization tasks
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Accuracy
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RMSE (Root Mean Square Error)
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ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
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BLEU (Bilingual Evaluation Understudy)
Explanation
Correct Answer C. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
Explanation:
ROUGE is a set of metrics specifically designed to evaluate text generation tasks like summarization. It compares the overlap between n-grams, word sequences, or word pairs in the generated text and one or more reference texts. ROUGE emphasizes recall, which is important in summarization to ensure key content is retained from the source. It helps quantify how much of the important information was captured in the generated summary.
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.What can Amazon Q Developer do to help the company meet these requirements?
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Create software snippets, reference tracking, and open source license tracking.
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Run an application without provisioning or managing servers.
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Enable voice commands for coding and providing natural language search.
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Convert audio files to text documents by using ML models.
Explanation
The correct Answer is: A. Create software snippets, reference tracking, and open source license tracking. because Amazon Q Developer is a generative AI-powered assistant that helps developers by generating code snippets, tracking references in documentation, and managing open source license usage. This directly boosts productivity and streamlines software development tasks, aligning with the company's goals.
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
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On-Demand
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Model customization
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Provisioned Throughput
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Spot Instance
Explanation
The Correct Answer is: A. On-Demand because The On-Demand pricing model in Amazon Bedrock is ideal for companies with a limited budget and a need for flexibility. It allows you to pay only for what you use without requiring long-term commitments or upfront costs, making it a cost-effective option for experimentation and variable workloads.
Which deployment model of Amazon SageMaker is the right fit for persistent and real-time endpoints that make one prediction at a time?
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Real-time hosting services
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Asynchronous Inference
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Batch transform
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Serverless Inference
Explanation
The Correct Answer is: A) Real-time hosting services because Real-time hosting services in Amazon SageMaker are ideal for applications that need persistent endpoints and respond to individual inference requests with low latency. This is the best fit when predictions must be made immediately, such as in fraud detection or recommendation systems.
Domain: Applications of Foundation Models
You are fine-tuning the Amazon Titan Text Premier model on Amazon Bedrock for a specific task. You want to ensure you follow best practices and understand the impact of different hyperparameters. Which metrics are recommended for determining the optimal number of epochs for fine-tuning?
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Validation output accuracy
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Training loss
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Validation loss
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Learning rate
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Batch Size
Explanation
Correct Answers:
B. Training loss
C. Validation loss
Explanation:
B. Training loss
Training loss measures how well the model is fitting the training data during each epoch. Monitoring this metric helps determine whether the model is learning effectively. A consistently decreasing training loss generally indicates that the model is improving on the training set.
C. Validation loss
Validation loss indicates how well the model is generalizing to unseen data. It is the most important metric for determining when to stop training. If validation loss starts to increase while training loss continues to decrease, it’s a sign of overfitting—indicating that you've likely passed the optimal number of epochs.
Together, training loss and validation loss are key metrics for identifying when a model has learned sufficiently without overfitting, helping you determine the optimal number of epochs during fine-tuning.
Domain: Guidelines for Responsible AI
You are a data scientist leading a project to develop an AI-powered loan approval system. You are committed to ensuring that the system is trustworthy and adheres to responsible AI principles. Which of the following factors are crucial for AI technologies to be considered trustworthy
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Fairness
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Explainability
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Reproducibility
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Cost-effectiveness
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Scalability
Explanation
Correct Answers:
A. Fairness
B. Explainability
C. Reproducibility
Explanation:
A. Fairness
Fairness ensures that the AI system treats all individuals and groups equitably, without discrimination or bias—particularly critical in sensitive applications like loan approvals where biased outcomes can lead to real-world harm.
B. Explainability
Explainability refers to the ability to understand and interpret how the model makes decisions. This is vital in regulated industries like finance, where decisions must be transparent and justifiable to stakeholders and regulators.
C. Reproducibility
Reproducibility ensures that the AI system produces consistent results when given the same inputs under the same conditions. This is a cornerstone of scientific integrity and essential for building trust in the system’s reliability.
Which option is a use case for generative AI models?
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Improving network security by using intrusion detection systems
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Creating photorealistic images from text descriptions for digital marketing
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Enhancing database performance by using optimized indexing
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Analyzing financial data to forecast stock market trends
Explanation
The Correct Answer is: B. Creating photorealistic images from text descriptions for digital marketing because Generative AI models are designed to create new content, such as images, text, or audio. Generating photorealistic images from text is a typical use case—especially useful in areas like digital marketing, where custom visuals are needed to engage audiences.
Domain: Guidelines for Responsible Al
Case Study:
You're deploying a text summarization solution on AWS Bedrock, leveraging a foundation model (FM). You're concerned about the potential for prompt leaking, Prompt Injection, and Jailbreaking attacks.
Which of the following scenarios demonstrates a prompt injection attack
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A user submits a prompt requesting a summary of a sensitive document they are not authorized to access
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A user includes instructions within their prompt to bypass safety guidelines and generate harmful content
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A user repeatedly queries the FM to try and deduce its internal parameters
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A user provides a prompt that is intentionally vague and ambiguous to confuse the model
Explanation
Correct Answer B. A user includes instructions within their prompt to bypass safety guidelines and generate harmful content
Explanation of Correct Answer:
B. A user includes instructions within their prompt to bypass safety guidelines and generate harmful content
This scenario is a classic example of a prompt injection attack. The user attempts to override or manipulate the system's original prompt instructions (e.g., safety rules or ethical boundaries) by embedding their own malicious instructions within the input prompt. The goal is to make the model behave in unintended ways, such as generating harmful or prohibited content. Prompt injection targets the LLM’s context window to subvert expected behavior.
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