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>> Authentic AIF-C01 Exam Questions <<
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NEW QUESTION # 419
A company has a large amount of unlabeled data. The company wants to group the data based on feature similarities.
Which algorithm will meet this requirement?
Answer: A
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS machine learning fundamentals classify K-means as an unsupervised learning algorithm used to group unlabeled data into clusters based on feature similarity and distance metrics. Because the data is unlabeled and the goal is grouping rather than prediction, K-means is the most appropriate choice.
Why the other options are incorrect:
* XGBoost is a supervised learning algorithm that requires labeled data.
* DeepAR forecasting is designed for time series forecasting.
* Linear learner is typically used for supervised regression or classification tasks.
AWS AI Study Guide References:
* AWS unsupervised learning concepts
* AWS clustering algorithms overview
NEW QUESTION # 420
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?
Answer: A
Explanation:
In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.
* Option B (Correct): "Hallucination": This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.
* Option A: "Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.
* Option C: "Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.
* Option D: "Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.
AWS AI Practitioner References:
* Large Language Models on AWS: AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.
NEW QUESTION # 421
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?
Answer: A
NEW QUESTION # 422
A company is building an AI application to automate business processes. The company uses a foundation model (FM) to support the application.
The company needs to select datasets to assess the quality of the AI model ' s behavior.
Which type of datasets will meet these requirements?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI and generative AI evaluation guidance emphasizes that assessing the quality and behavior of a foundation model requires representative and diverse evaluation datasets. These datasets should reflect real-world usage patterns, edge cases, and multiple business scenarios to properly evaluate how the model behaves in production.
Using diverse datasets that cover various use cases and usage scenarios allows organizations to:
Evaluate robustness and generalization of the FM
Identify failure modes, bias, and unsafe behavior across different inputs Validate that the model performs consistently across business workflows Why the other options are incorrect:
A may remove meaningful real-world patterns and does not reflect realistic usage.
B risks reinforcing model biases and does not provide independent evaluation.
D does not reflect real or meaningful business scenarios and can distort evaluation results.
AWS AI Study Guide References:
AWS Responsible AI evaluation practices
AWS guidance on foundation model testing and validation
NEW QUESTION # 423
A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world.
Which principle of responsible Al does the company demonstrate in this scenario?
Answer: B
Explanation:
The company is training an image classification model for diagnosing insect bites using a diverse dataset that includes photos from different genders, ethnicities, and geographic locations. This approach demonstrates the principle of fairness in responsible AI, as it aims to reduce bias and ensure the model performs equitably across diverse populations.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Fairness in AI involves ensuring that models do not exhibit bias against certain groups and perform equitably across diverse populations. This can be achieved by training models on diverse datasets that represent various demographics, such as gender, ethnicity, and geographic location." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Option A: FairnessThis is the correct answer. By using a diverse dataset, the company ensures the model is less likely to be biased against specific groups, promoting fairness in its predictions and treatments for insect bites.
Option B: ExplainabilityExplainability refers to making the model's decisions understandable to users, such as byproviding insights into how predictions are made. The scenario focuses on dataset diversity, not explainability.
Option C: GovernanceGovernance involves establishing policies and processes to manage AI systems, such as compliance and oversight. The scenario does not describe governance mechanisms.
Option D: TransparencyTransparency involves disclosing how a model works, its limitations, and its data sources. While transparency is important, the scenario specifically highlights the diversity of the dataset, which aligns more directly with fairness.
Reference:
AWS AI Practitioner Learning Path: Module on Responsible AI
AWS Documentation: Responsible AI Principles (https://aws.amazon.com/machine-learning/responsible-ai/) Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-bias.html)
NEW QUESTION # 424
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