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| Section | Weight | Objectives |
|---|---|---|
| AWS AI Services Overview | 26% | - Describe AWS AI services for specific use cases
|
| AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| AI Application Development | 30% | - Understand foundational concepts for building AI applications
|
| Responsible AI | 20% | - Explain the principles of responsible AI
|
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NEW QUESTION # 448
A company wants to identify groups for its customers based on the customers' demographics and buying patterns.
Which algorithm should the company use to meet this requirement?
Answer: A
Explanation:
The correct answer is B because K-means is an unsupervised learning algorithm used for clustering data points into groups (clusters) based on feature similarity. It is ideal for customer segmentation use cases where the goal is to discover natural groupings based on buying behavior and demographics without pre-labeled data.
From AWS documentation:
"K-means is a clustering algorithm that assigns data points to one of K groups based on feature similarity. It is commonly used in marketing and customer segmentation to group users with similar characteristics." Explanation of other options:
A). K-nearest neighbors is a supervised classification algorithm, not intended for clustering.
C). Decision tree is also a supervised learning method used for classification or regression tasks.
D). Support vector machine is used for classification and regression, not unsupervised clustering.
Referenced AWS AI/ML Documents and Study Guides:
* AWS ML Specialty Guide - Unsupervised Learning and Clustering
* AWS ML Algorithm Selection Guide
* Amazon SageMaker Built-in Algorithms - K-means Clustering
NEW QUESTION # 449
An ecommerce company is developing an AI application that categorizes product images and extracts specifications. The application will use a high-quality labeled dataset to customize a foundation model (FM) to generate accurate responses.
Which ML technique will meet these requirements by using Amazon Bedrock?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The correct technique is fine-tuning, which is explicitly supported by Amazon Bedrock for customizing foundation models using high-quality labeled datasets.
Fine-tuning involves:
Starting with a pre-trained foundation model
Training it further using domain-specific, labeled data
Improving accuracy for specialized tasks, such as product classification, image-based understanding, and specification extraction In this use case:
The company has labeled data
They want to customize model behavior
They require high accuracy and domain adaptation
These conditions match the definition of fine-tuning, not prompt-only methods.
Why the other options are incorrect:
A). Continued pre-training typically requires massive unlabeled datasets and is not the standard customization method exposed in Amazon Bedrock.
B). Creating an agent orchestrates model interactions and tools but does not customize the model's learned parameters.
D). Prompt engineering improves responses through prompt design but does not modify the underlying model weights, making it insufficient for deep domain adaptation.
AWS AI document references (for exact extracts):
Amazon Bedrock Documentation - section on Model customization and fine-tuning AWS Generative AI Study Guide - comparison of prompt engineering vs fine-tuning Foundation Models on AWS - explanation of fine-tuning with labeled datasets
NEW QUESTION # 450
A company is using Amazon SageMaker to deploy a model that identifies if social media posts contain certain topics. The company needs to show how different input features influence model behavior.
Answer: C
Explanation:
* SageMaker Clarify provides model explainability, bias detection, and helps visualize how input features affect predictions.
* Canvas is for low-code ML building.
* Feature Store is for storing and managing ML features.
* Ground Truth is for data labeling.
# Reference:
AWS Documentation - Amazon SageMaker Clarify
NEW QUESTION # 451
A company needs a scalable method to compare two foundation models (FMs) for chat summarization based on correctness and completeness.
Which solution will meet these requirements?
Answer: C
Explanation:
The verified answer is C. Use the LLM-as-a-judge approach with a rubric across prompts to compare model outputs . The question asks for a scalable way to compare two foundation models for chat summarization based on correctness and completeness . AWS documentation for Amazon Bedrock model evaluation states that an evaluation job using a judge model uses an LLM to score another model's responses and provide an explanation of how it scored each prompt and response pair. This is specifically designed to compare model outputs across prompt datasets at scale, without requiring every output to be manually scored by humans.
AWS Bedrock documentation also lists built-in metrics for model evaluation jobs that use an LLM as a judge.
The valid built-in metrics include Correctness and Completeness , which directly match the evaluation criteria in the question. Therefore, using LLM-as-a-judge with a rubric across prompts is the best fit because it evaluates the actual summaries produced by each model against the desired quality dimensions.
Option A is partially valid as an evaluation method, but it is not the most scalable approach. Human reviewers can provide high-quality judgments, but manual review becomes expensive and slow when comparing many prompts, outputs, and models. Option B is incorrect because latency and throughput measure operational performance, not whether a summary is correct or complete. A fast model can still produce incomplete or inaccurate summaries. Option D is incorrect because external benchmark rankings are generic. They do not prove that a model performs best on the company's specific chat summarization workload.
The decisive terms are "scalable," "correctness," and "completeness." Amazon Bedrock LLM-as-a-judge evaluation directly supports those dimensions, so option C is correct.
NEW QUESTION # 452
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)
Answer: B,D
Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
A . Include Fairness Metrics for Model Evaluation:
Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
C . Modify the Training Data to Mitigate Bias:
Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
Why Other Options are Incorrect:
B . Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
D . Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
E . Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.
NEW QUESTION # 453
......
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