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Amazon AIF-C01인증시험은 현재IT인사들 중 아주 인기 잇는 인증시험입니다.Amazon AIF-C01시험패스는 여러분의 하시는 일과 생활에서 많은 도움을 줄뿐만 아니라 중요한 건 여러분의IT업계에서의 자기만의 자리를 지키실 수 잇습니다.이렇게 좋은 시험이니 많은 분들이 응시하려고 합니다,하지만 패스 율은 아주 낮습니다.
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질문 # 116
Which functionality does Amazon SageMaker Clarify provide?
정답：D
설명：
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 Reference:
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.
질문 # 117
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?
정답：D
설명：
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.
* Prompt Templates for Security:
* A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.
* This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.
* Why Option A is Correct:
* Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.
* Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.
* Why Other Options are Incorrect:
* B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.
* C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.
* D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.
질문 # 118
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
정답：D
설명：
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.
질문 # 119
Which task represents a practical use case to apply a regression model?
정답：C
설명：
* Regression predicts continuous numerical values (e.g., stock prices, temperatures).
* A is classification (genre selection).
* B is clustering.
* D is generative AI/computer vision.
# Reference:
AWS ML Glossary - Regression
질문 # 120
A financial company is building an ML model to classify fraudulent transactions based on customer data and transaction patterns. The company wants to evaluate the model's performance. The company must ensure that the model makes correct predictions and minimizes false positives.
Which evaluation metric will meet these requirements?
정답：A
설명：
The verified answer is C. F1 score. This is a classification problem: the model classifies transactions as fraudulent or non-fraudulent. AWS machine learning documentation identifies accuracy, precision, recall, and F1 score as classification evaluation metrics. F1 score is especially appropriate when the evaluation must consider both correct positive predictions and the cost of incorrect positive predictions. AWS defines F1 as the harmonic mean of precision and recall, which means it balances how many positive predictions are correct with how many actual positive cases are found.
The key issue in the question is minimizing false positives. In fraud detection, a false positive means a legitimate transaction is incorrectly classified as fraudulent. AWS documentation explains that precision evaluates how well the model avoids false positives by measuring the percentage of positive predictions that were actually correct. Because F1 includes precision, it penalizes models that generate too many false positives while also considering recall.
Accuracy is not the best answer because it only measures the overall fraction of correct predictions. In fraud detection, the dataset is often imbalanced, so a model can appear accurate while still performing poorly on fraud-related predictions. R-squared and RMSE are regression metrics, not classification metrics. R-squared measures how well a regression model explains variance, and RMSE measures prediction error magnitude for continuous numeric outputs.
Because the company needs a classification metric that reflects correct predictions and penalizes false- positive behavior, F1 score is the strongest option among the choices.
질문 # 121
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IT업계의 치열한 경쟁속에 살아 남으려면 자신의 능력을 증명하여야 합니다. 국제승인을 받는 IT인증자격증을 많이 취득하시면 취직이든 승진이든 이직이든 모든 면에서 이득을 볼수 있습니다. 최근 Amazon인증 AIF-C01시험에 도전하는 분이 많은데 ExamPassdump에서 Amazon인증 AIF-C01시험에 대비한 가장 최신버전 덤프공부가이드를 제공해드립니다.
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