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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models |
| Topic 2: Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Security controls for AI data and models |
| Topic 3: Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Recognize key concepts: data, models, training, inference, evaluation |
| Topic 4: Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI |
| Topic 5: Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications |
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NEW QUESTION # 311
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?
Answer: A
Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
A . Measurement bias: Involves errors in data collection or measurement, not sampling.
C . Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.
D . Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.
NEW QUESTION # 312
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.)
* SageMaker Clarify
* SageMaker Model Registry
* SageMaker Serverless Inference
Answer:
Explanation:
Explanation:
SageMaker Model Registry, SageMaker Serverless interference
This question requires selecting the appropriate Amazon SageMaker feature for two distinct steps in the AI model lifecycle. Let's break down each step and evaluate the options:
Step 1: Managing different versions of the model
The goal here is to identify a SageMaker feature that supports version control and management of machine learning models. Let's analyze the options:
* SageMaker Clarify: This feature is used to detect bias in models and explain model predictions, helping with fairness and interpretability. It does not provide functionality for managing model versions.
* SageMaker Model Registry: This is a centralized repository in Amazon SageMaker that allows users to catalog, manage, and track different versions of machine learning models. It supports model versioning, approval workflows, and deployment tracking, making it ideal for managing different versions of a model.
* SageMaker Serverless Inference: This feature enables users to deploy models for inference without managing servers, automatically scaling based on demand. It is focused on inference (predictions), not on managing model versions.
Conclusion for Step 1: The SageMaker Model Registry is the correct choice for managing different versions of the model.
Exact Extract Reference: According to the AWS SageMaker documentation, "The SageMaker Model Registry allows you to catalog models for production, manage model versions, associate metadata, and manage approval status for deployment." (Source: AWS SageMaker Documentation - Model Registry,
https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html).
Step 2: Using the current model to make predictions
The goal here is to identify a SageMaker feature that facilitates making predictions (inference) with a deployed model. Let's evaluate the options:
* SageMaker Clarify: As mentioned, this feature focuses on bias detection and explainability, not on performing inference or making predictions.
* SageMaker Model Registry: While the Model Registry helps manage and catalog models, it is not used directly for making predictions. It can store models, but the actual inference process requires a deployment mechanism.
* SageMaker Serverless Inference: This feature allows users to deploy models for inference without managing infrastructure. It automatically scales based on traffic and is specifically designed for making predictions in a cost-efficient, serverless manner.
Conclusion for Step 2: SageMaker Serverless Inference is the correct choice for using the current model to make predictions.
Exact Extract Reference: The AWS documentation states, "SageMaker Serverless Inference is a deployment option that allows you to deploy machine learning models for inference without configuring or managing servers. It automatically scales to handle inference requests, making it ideal for workloads with intermittent or unpredictable traffic." (Source: AWS SageMaker Documentation - Serverless Inference, https://docs.aws.
amazon.com/sagemaker/latest/dg/serverless-inference.html).
Why Not Use the Same Feature Twice?
The question specifies that each SageMaker feature or resource should be selected one time or not at all. Since SageMaker Model Registry is used for version management and SageMaker Serverless Inference is used for predictions, each feature is selected exactly once. SageMaker Clarify is not applicable to either step, so it is not selected at all, fulfilling the question's requirements.
:
AWS SageMaker Documentation: Model Registry (https://docs.aws.amazon.com/sagemaker/latest/dg/model- registry.html) AWS SageMaker Documentation: Serverless Inference (https://docs.aws.amazon.com/sagemaker/latest/dg
/serverless-inference.html)
AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference) Let's format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications.
NEW QUESTION # 313
An online learning company with large volumes of education materials wants to use enterprise search.
Answer: D
NEW QUESTION # 314
A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.
Which combination of AWS service and storage class meets these requirements? (Select TWO.)
Answer: A,D
Explanation:
* AWS CloudTrail: Logs all API calls to Amazon Bedrock.
* Amazon S3 Intelligent-Tiering: Optimizes storage costs for long-term retention with automatic tiering.
According to Amazon Bedrock Logging Documentation:
"CloudTrail records API activity and events, and logs can be stored in S3. For cost optimization, use S3 Intelligent-Tiering to retain logs long-term."
NEW QUESTION # 315
A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.
What can the company do to secure the chatbot with the LEAST implementation effort?
Answer: D
NEW QUESTION # 316
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