AIF-C01

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The dumps for AIF-C01 exam was last updated on Jan 12,2026 .

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Question#1

A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books.
Why does the application fail to summarize some books?

A. The temperature is set too high.

B. The selected model does not support fine-tuning.

C. The Top P value is too high.

D. The input tokens exceed the model's context size.

Explanation:
Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure. Temperature (A) and Top P (C) control randomness, not input size. Fine-tuning (B) is irrelevant to input truncation failures.
Reference: AWS Documentation C Amazon Bedrock Model Parameters (context size limits)

Question#2

Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

A. Embeddings

B. Tokens

C. Models

D. Binaries

Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner
Reference: Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.

Question#3

A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

A. Number of tokens consumed

B. Temperature value

C. Amount of data used to train the LLM

D. Total training time

Explanation:
In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.
Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.
Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.
Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.
Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.
AWS AI Practitioner
Reference: Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

Question#4

Which metric measures the runtime efficiency of operating AI models?

A. Customer satisfaction score (CSAT)

B. Training time for each epoch

C. Average response time

D. Number of training instances

Explanation:
The average response time is the correct metric for measuring the runtime efficiency of operating AI models.
Average Response Time:
Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.
A lower average response time indicates a more efficient model that can handle queries quickly.
Why Option C is Correct:
Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.
Performance Indicator: Helps identify potential bottlenecks and optimize model performance.
Why Other Options are Incorrect:
A. Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.
B. Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.
D. Number of training instances: Refers to data used during training, not operational efficiency.

Question#5

A company has developed a generative text summarization application by using Amazon Bedrock.
The company will use Amazon Bedrock automatic model evaluation capabilities.
Which metric should the company use to evaluate the accuracy of the model?

A. Area Under the ROC Curve (AUC) score

B. F1 score

C. BERT Score

D. Real World Knowledge (RWK) score

Explanation:
The correct answer is C because BERTScore is a commonly used metric to evaluate the semantic similarity between generated text (like summaries) and reference text. It uses contextual embeddings from BERT to compare generated and reference sentences, making it highly suitable for evaluating generative tasks like summarization.
From AWS documentation:
"Amazon Bedrock supports BERTScore for evaluating generative text tasks, such as summarization or translation, by comparing the semantic similarity between the output and a reference."
Explanation of other options:
A. AUC is used for binary classification models, not generative text.
B. F1 score is also used for classification problems (precision/recall balance).
D. Real World Knowledge (RWK) score is not a standard or supported evaluation metric in Amazon Bedrock.
Referenced AWS AI/ML Documents and Study Guides: Amazon Bedrock Documentation C Model Evaluation Metrics AWS ML Specialty Guide C Evaluating Generative Models AWS Generative AI Developer Tools

Exam Code: AIF-C01 Q & A: 336 Q&As Updated: Jan 12,2026

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Exam Code: AIF-C01
Q & A: 336 Q&As
Updated: Jan 12,2026

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