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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AWS AI Services Overview | 26% | - Describe Amazon SageMaker capabilities
|
| Topic 2: AI Application Development | 30% | - Implement AI applications using AWS services
|
| Topic 3: AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| Topic 4: Responsible AI | 20% | - Understand governance and compliance requirements
|
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NEW QUESTION # 56
A financial company has offices in different countries worldwide. The company requires that all API calls between generative AI applications and foundation models (FM) must not travel across the public internet.
Which AWS service should the company use?
Answer: D
Explanation:
AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, ensuring traffic does not traverse the public internet.
* A is correct:
"AWS PrivateLink provides private connectivity to services across VPCs, keeping API traffic off the public internet." (Reference: AWS PrivateLink Overview)
* B (Amazon Q) is a generative AI assistant, not a network security/control tool.
* C (CloudFront) is a CDN, not for private API calls.
* D (CloudTrail) is for logging and monitoring, not secure connectivity.
NEW QUESTION # 57
Which functionality does Amazon SageMaker Clarify provide?
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 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 # 58
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
Answer: B
Explanation:
Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.
Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.
Option B: "Model customization" is a feature, not a pricing model.
Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.
Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.
AWS AI Practitioner Reference:
AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.
NEW QUESTION # 59
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.
How should the bank fix this issue MOST cost-effectively?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A . Include more diverse training data. Fine-tune the model again by using the new data:
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation)
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation) B (RAG) is unrelated to model fairness or bias mitigation; it's for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security-not for ML model bias detection or mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.
Reference:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
NEW QUESTION # 60
A company wants to implement a generative AI assistant to provide consistent responses to various phrasings of user questions.
Which advantages can generative AI provide in this use case?
Answer: B
Explanation:
The correct answer is B - Adaptability and responsiveness, which are core strengths of generative AI models such as the foundation models available in Amazon Bedrock. According to AWS documentation, generative AI systems excel at understanding natural language variations, meaning they can interpret different phrasings, synonyms, sentence structures, and conversational styles while still generating contextually consistent answers. This capability comes from pretraining on diverse natural language corpora, allowing models to generalize across multiple linguistic patterns. AWS highlights that generative AI models are designed to handle "flexible, dynamic, and conversational inputs" and provide responses grounded in understanding user intent rather than matching exact keywords. Options A and D describe infrastructure performance characteristics, not the reasoning ability required for this use case. Option C (deterministic outputs) is incorrect because LLMs are inherently probabilistic and not fixed unless using advanced constraints.
Therefore, generative AI's adaptability to varied user phrasing makes it ideal for assistants requiring consistent, intent-based responses.
Referenced AWS Documentation:
* Amazon Bedrock Developer Guide - Foundation Model Capabilities
* AWS Generative AI Best Practices - Natural Language Understanding
NEW QUESTION # 61
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