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| Section | Weight | Objectives |
|---|---|---|
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI |
| Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models |
| Applications of Foundation Models | 28% | - Explain integration of foundation models into applications - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Recognize tools for building and deploying generative AI solutions |
| Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation |
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Compliance requirements and regulations - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows |
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NEW QUESTION # 55
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
Answer:
Explanation:
Explanation:
The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
* Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
* Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
* K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
* Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and
"Unsupervised learning." The correct selections are:
* Binary classification: Supervised learning
* Multi-class classification: Supervised learning
* K-means clustering: Unsupervised learning
* Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine- learning/)
NEW QUESTION # 56
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
Answer: B
Explanation:
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide) A (JumpStart) provides prebuilt solutions and models, not monitoring.
B (HyperPod) is for large-scale training, not model monitoring.
C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.
NEW QUESTION # 57
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
NEW QUESTION # 58
A company uses foundation models (FMs) to automate daily tasks. An AI practitioner is creating system instructions that include context relevant to the tasks. The AI practitioner wants to save and reuse the instructions in daily interactions with FMs in Amazon Bedrock.
Which Amazon Bedrock solution will meet these requirements?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Amazon Bedrock Prompt Management is designed to allow practitioners to create, version, store, and reuse prompts and system instructions when working with foundation models. It enables consistent reuse of contextual instructions across repeated interactions and applications.
Why the other options are incorrect:
Knowledge Bases store and retrieve external data, not reusable system instructions.
Guardrails enforce safety and policy controls, not prompt reuse.
Playgrounds are for experimentation and testing, not long-term prompt storage.
AWS AI Study Guide Reference:
Amazon Bedrock prompt management capabilities
AWS best practices for prompt engineering and reuse
NEW QUESTION # 59
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more dat a. Which type of learning should the company use to train the model?
Answer: B
Explanation:
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. Reference: AWS Machine Learning Best Practices.
NEW QUESTION # 60
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