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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning Engineer - Associate AWS Certified Data Engineer - Associate |
| Exam Format: | Multiple response, Multiple choice |
| Real Exam Qty: | 80 |
| Passing Score: | 700 / 1000 |
| Available Languages: | Korean, Japanese, English, Simplified Chinese, Portuguese (Brazil) |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Exam Price: | USD 100 |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 151
A company wants to learn about generative AI applications in an experimental environment. Which solution will meet this requirement MOST cost-effectively?
Answer: A
Explanation:
* Amazon Bedrock PartyRock is a free, no-code playground for experimenting with generative AI apps.
* SageMaker JumpStart is powerful but incurs costs.
* Q Developer and Q Business are enterprise tools, not experimental learning environments.
# Reference:
AWS Documentation - PartyRock
NEW QUESTION # 152
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?
Answer: A
Explanation:
To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and havefaster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.
Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices": This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.
Option B: "Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.
Option C: "Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.
Option D: "Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.
AWS AI Practitioner Reference:
Optimizing AI Models for Edge Devices on AWS: AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.
NEW QUESTION # 153
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?
Answer: D
Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 154
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Answer: C
Explanation:
The correct answer is A, as the most effective way to ensure fairness in AI systems is to audit and diversify training data. AWS Responsible AI documentation stresses that bias originates primarily from unbalanced or incomplete datasets. By including samples from all demographic groups, the model learns patterns that generalize fairly across populations. AWS SageMaker Clarify supports data bias detection and offers fairness metrics like demographic parity and equal opportunity difference. Deeper models (option B) or secrecy (option C) do not mitigate bias and may worsen transparency. Static test datasets (option D) fail to capture evolving data distributions. Therefore, ongoing bias reviews and diverse datasets form the foundation of responsible generative AI for financial use cases.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Whitepaper - Fairness and Bias Mitigation
Amazon SageMaker Clarify Documentation - Bias Detection
NEW QUESTION # 155
Which type of AI model makes numeric predictions?
Answer: D
Explanation:
The regression model is a fundamental type of supervised machine learning algorithm that is specifically designed to make numeric predictions. In regression tasks, the goal is to predict a continuous numerical value based on input features. This contrasts with classification, which predicts discrete labels.
According to AWS documentation:
"Regression models are used for predicting a continuous value. Examples include predicting house prices, stock market prices, or customer credit limits." (Reference: AWS Machine Learning Foundations: Regression, AWS AI Practitioner Study Guide) Option A (Diffusion) relates to generative models and is not primarily used for numeric prediction.
Option C (Transformer) is a neural network architecture, often used for sequence modeling tasks (e.g., NLP).
Option D (Multi-modal) describes a model handling multiple data types, not specifically numeric prediction.
Reference:
AWS AI/ML Learning Path - Regression Models
AWS Certified AI Practitioner Study Guide (Pearson)
NEW QUESTION # 156
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