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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation |
| Topic 2: Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Compliance requirements and regulations - Security controls for AI data and models |
| Topic 3: Fundamentals of Generative AI | 24% | - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Explain capabilities and use cases of generative AI |
| Topic 4: Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications |
| Topic 5: Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Describe ethical and societal impacts of AI - Define responsible AI principles: fairness, transparency, privacy, safety - Identify risks and mitigation strategies for AI systems |
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NEW QUESTION # 275
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning
Answer:
Explanation:
NEW QUESTION # 276
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?
Answer: C
Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case.
Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
References:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com
/bedrock/latest/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock
/latest/devguide/)
NEW QUESTION # 277
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
Answer: A
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner Reference:
Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
NEW QUESTION # 278
Which functionality does Amazon SageMaker Clarify provide?
Answer: B
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
NEW QUESTION # 279
An AI practitioner wants to perform a contextual grounding check in Amazon Bedrock.
Which three components must the AI practitioner use?
Answer: B
Explanation:
Amazon Bedrock Guardrails provides contextual grounding checks to determine whether foundation model responses are supported by reference information and relevant to user queries.
According to AWS documentation, contextual grounding checks require three components:
1. Grounding source: Reference information used to establish factual context.
2. User query: The question or request submitted by the user.
3. Model response: The generated content evaluated against the reference source and query.
These checks evaluate two important dimensions: grounding, which determines whether claims are supported by source information, and relevance, which determines whether responses address the user ' s question.
Option B includes API credentials, which are not a contextual grounding component. Option C describes development interfaces. Option D includes confidence scores and thresholds, which support evaluation but cannot replace the required source and response.
Therefore, option A is correct.
AWS Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-contextual-grounding- check.html
NEW QUESTION # 280
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