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Amazon AWS Certified AI Practitioner Sample Questions (Q92-Q97):
NEW QUESTION # 92
An AI practitioner is developing a prompt for an Amazon Titan model. The model is hosted on Amazon Bedrock. The AI practitioner is using the model to solve numerical reasoning challenges. The AI practitioner adds the following phrase to the end of the prompt: "Ask the model to show its work by explaining its reasoning step by step." Which prompt engineering technique is the AI practitioner using?
Answer: A
Explanation:
Chain-of-thought prompting is a prompt engineering technique where you instruct the model to explain its reasoning step by step, which is particularly useful for tasks involving logic, math, or reasoning.
A is correct: Asking the model to "explain its reasoning step by step" directly invokes chain-of-thought prompting, as documented in AWS and generative AI literature.
B is unrelated (prompt injection is a security concern).
C (few-shot) provides examples, but doesn't specifically require step-by-step reasoning.
D (templating) is about structuring the prompt format.
"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks." (Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)
"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks." (Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)
NEW QUESTION # 93
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
Answer: C
NEW QUESTION # 94
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?
Answer: D
Explanation:
Inference is the correct answer because it is the AI process that occurs when a deployed model analyzes new data (such as an image) to make predictions or identify objects.
* Inference:
* In the context of machine learning, inference is the process of using a trained model to make predictions on new, unseen data.
* When the deep learning model is deployed to production and receives a new image for analysis, it uses the learned patterns from the training phase to identify objects in the image. This is known as inference.
* Why Option B is Correct:
* Inference Process: Involves applying the trained model to real-world data (the new image) to identify objects.
* Deployment Context: The model has already been trained, and the deployment to production indicates it is being used for inference.
* Why Other Options are Incorrect:
* A. Training: Refers to the process of teaching the model using historical data, not making predictions on new data.
* C. Model deployment: Refers to the process of making a trained model available for use in production.
* D. Bias correction: Is a process to adjust a model to minimize bias, not for analyzing new images.
NEW QUESTION # 95
Which phase of the ML lifecycle determines compliance and regulatory requirements?
Answer: C
Explanation:
The business goal identification phase of the ML lifecycle involves defining the objectives of the project and understanding the requirements, including compliance and regulatory considerations. This phase ensures the ML solution aligns with legal and organizational standards before proceeding to technical stages like data collection or model training.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The business goal identification phase involves defining the problem to be solved, identifying success metrics, and determining compliance and regulatory requirements to ensure the ML solution adheres to legal and organizational standards." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
* Option A: Feature engineeringFeature engineering involves creating or selecting features for model training, which occurs after compliance requirements are identified. It does not address regulatory concerns.
* Option B: Model trainingModel training focuses on building the ML model using data, not on determining compliance or regulatory requirements.
* Option C: Data collectionData collection involves gathering data for training, but compliance and regulatory requirements (e.g., data privacy laws) are defined earlier in the business goal identification phase.
* Option D: Business goal identificationThis is the correct answer. This phase ensures that compliance and regulatory requirements are considered at the outset, shaping the entire ML project.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: ML Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg
/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
NEW QUESTION # 96
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
Answer: A
Explanation:
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
* Option A (Correct): "Delete the custom model. Remove the confidential data from the training dataset.
Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
* Option B: "Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model being trained on confidential data.
Masking data in inference responses does not prevent the model from using confidential data it learned during training.
* Option C: "Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.
* Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training.
AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner References:
* Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
* Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.
NEW QUESTION # 97
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