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AIF-C01の有効な学習ガイド資料は、何十年にもわたる専門家や教授の骨の折れる努力により、世界市場で主導的な地位を占めていることがわかっています。当社のAIF-C01学習練習問題のAIF-C01試験の準備をしている多くの人々が重い負担を軽減するのを助けるために、AIF-C01学習教材には多くの特別な機能があります。散発的な時間の使用。 AIF-C01試験の質問を購入する必要がある場合、AIF-C01試験に簡単に合格できます。
質問 # 327
Which inference parameter can control the creativity of an ML model?
正解：B
解説：
Temperature is an inference parameter that directly influences the randomness and creativity of content generated by a foundation model.
During text generation, a language model assigns probabilities to potential next tokens. Temperature adjusts this probability distribution, affecting how predictably the model selects subsequent tokens.
A lower temperature generally produces more deterministic, consistent responses, making it useful for factual question answering and structured business applications.
A higher temperature increases the likelihood of selecting less probable tokens, potentially producing more diverse and creative responses.
Token length controls output size, while an end token sequence determines when generation stops.
Top K also affects output diversity by restricting the number of candidate tokens. However, temperature is the standard answer because it directly regulates sampling randomness and creativity.
AWS documents both temperature and Top K as generation-control parameters.
質問 # 328
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
正解：D
解説：
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Option A: Use data from only customers who match the demography of the company's overall customer base.Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
Reference:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-bias.html) AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
質問 # 329
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
正解：C
解説：
Comprehensive and Detailed
Explainable AI (XAI) techniques such as SHAP (SHapley values) or feature attribution provide transparency by showing which input factors influenced decisions.
A is not scalable for complex use cases.
C does not guarantee real interpretability.
D ignores the regulatory need for explainability.
Reference:
AWS SageMaker Clarify - Explainable AI
質問 # 330
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
正解：D
解説：
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
* Option A: Use data from only customers who match the demography of the company's overall customer base.Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
* Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
* Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
* Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
質問 # 331
A company needs to perform several ML tasks. The company wants to select the appropriate metric for each task.
Select the correct metric from the following list for each task. Select each metric one time or not at all. (Select THREE.)
正解：A、C、D
解説：
The three tasks require different evaluation metrics because they assess different characteristics of model output.
For machine translation, the correct metric is BLEU . AWS describes BLEU as a metric that "Measures n- gram overlap, focusing on precision." AWS identifies machine translation as its typical use case. BLEU compares sequences of words or tokens in a candidate translation with one or more reference translations.
Higher agreement of relevant N-grams generally produces a better score. Therefore, the first task maps to B .
For semantic similarity, BERTScore is appropriate. AWS explains that BERTScore uses a BERT-family model to create sentence embeddings and compares them using cosine similarity. Unlike simple lexical overlap, this allows semantically related phrases to receive similarity credit even when the exact words differ.
Thus, measuring semantic similarity between a generated response and a reference response maps to A .
For a binary classifier, Precision evaluates the reliability of positive predictions. AWS defines it as the fraction of actual positive instances among the examples predicted as positive. In formula form:
Precision = TP / (TP + FP)
A high precision score means that when the classifier predicts the positive class, that prediction is frequently correct. Therefore, the third task maps to C .
R² is not used for any of these tasks. R², or the coefficient of determination, is fundamentally a regression metric used to measure how much variation in a numerical target is explained by a regression model.
The verified mapping is therefore:
Machine translation using N-gram overlap # B. BLEU
Semantic similarity # A. BERTScore
Positive-class prediction quality # C. Precision
質問 # 332
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