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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Format: | Multiple response, Ordering, Multiple choice |
| Passing Score: | 700 (scaled score 100–1000) |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Available Languages: | Korean, Simplified Chinese, Japanese, English, Traditional Chinese |
| Certificate Validity Period: | 3 years |
| Exam Price: | 100 USD |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| Exam Registration: | AWS Certification Registration Pearson VUE Scheduling |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored or testing center delivery |
| Pre Condition: | No required prerequisites; recommended basic understanding of cloud computing and general IT concepts |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html |
私たちが提供するAWS Certified AI Practitioner準備トレントは、精巧にコンパイルされ、非常に効率的です。 AIF-C01試験トレントを練習するのに20〜30時間しかかからず、試験に参加できます。仕事などで忙しいほとんどのお客様。ただし、AIF-C01テスト準備を使用する場合、短時間で試験を準備して試験内容をマスターするのにそれほど時間は必要ありません。彼らがする必要があるのは、毎日学習して練習するのに1〜2時間を費やし、AIF-C01テスト準備で簡単に試験に合格することです。試験に合格するための時間と労力はほとんどかかりません。
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質問 # 286
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency
正解：
解説：
質問 # 287
Sated and order the steps from the following bat to correctly describe the ML Lifecycle for a new custom modal Select each step one time. (Select and order FOUR.)
* Define the business objective.
* Deploy the modal.
* Develop and tram the model.
* Process the data.
正解：
解説：
Step 1: Define the business objective.
Step 2: Process the data.
Step 3: Develop and train the model.
Step 4: Deploy the model.
The correct order represents the machine learning lifecycle as defined by AWS in the Amazon SageMaker documentation and AWS Certified Machine Learning Specialty Study Guide. The lifecycle describes the sequence of tasks required to build, train, and deploy a custom ML model effectively.
From AWS documentation:
" The machine learning process begins with defining the business problem, followed by collecting and processing data, developing and training models, and finally deploying them into production for inference. " Step 1 - Define the business objective:
This step involves clearly identifying the business problem to be solved and determining the measurable outcomes expected from the ML model. This ensures alignment between business goals and ML outputs.
Step 2 - Process the data:
Data is collected, cleaned, transformed, and prepared for training. This includes handling missing values, normalizing data, and performing feature engineering - a crucial phase that influences model performance.
Step 3 - Develop and train the model:
The model is built and trained on the processed data using algorithms appropriate to the problem (e.g., regression, classification, clustering). Hyperparameters are tuned to optimize model accuracy.
Step 4 - Deploy the model:
Once validated, the model is deployed to a production environment (e.g., Amazon SageMaker endpoint) to make predictions on new data. Continuous monitoring and retraining ensure the model remains effective.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Developer Guide - Machine Learning Lifecycle
AWS Certified Machine Learning Specialty Study Guide - Model Development Lifecycle AWS ML Best Practices Whitepaper - End-to-End ML Workflow
質問 # 288
A metal manufacturing company wants to capture images of finished products on its production line. The company wants to automatically detect subtle surface defects such as scratches, dents, and texture inconsistencies.
Which approach will meet these requirements?
正解：C
解説：
Computer vision is a field of artificial intelligence that enables machines to analyze and interpret visual information from images and videos.
In manufacturing, computer vision models can inspect product images to identify abnormalities, including scratches, dents, irregular surfaces, and other quality defects.
Deep learning approaches, including convolutional neural networks, can learn visual characteristics associated with acceptable and defective products.
AWS describes industrial computer vision architectures that use image analysis to automate manufacturing quality inspection and defect detection.
Natural language processing analyzes human language rather than visual surfaces.
Rule-based AI relies on explicitly defined conditions and can struggle with complex visual variations. Time- series forecasting models analyze sequential observations to predict future values.
Therefore, computer vision is the most appropriate approach for detecting subtle surface defects from manufacturing images.
質問 # 289
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?
正解：C
解説：
The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.
* Option C (Correct): "Use code that will calculate probability by using simple rules and computations": This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.
* Option A: "Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.
* Option B: "Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.
* Option D: "Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.
AWS AI Practitioner References:
* Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.
質問 # 290
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
正解：D
解説：
The correct answer is A because ensuring responsibility and fairness in ML begins with bias detection in the training data. Including a balanced representation of all demographics ensures the model learns fairly across different groups, which is critical in regulated industries like finance.
From AWS documentation:
"A key principle of responsible AI is building models that do not propagate or amplify bias. Fairness begins with training data. Reviewing and augmenting data for representation is essential." Explanation of other options:
B). The number of hidden layers doesn't inherently improve fairness or responsibility.
C). Keeping decisions opaque violates explainability principles in responsible AI.
D). A static dataset can become outdated and may not reflect real-world shifts, which limits fairness assessment over time.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon SageMaker Clarify Documentation - Bias Detection and Explainability
* AWS Responsible AI Guidelines
* AWS ML Specialty Study Guide - Fairness and Governance
質問 # 291
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AIF-C01日本語版: https://jp.fast2test.com/AIF-C01-premium-file.html
ちなみに、Fast2test AIF-C01の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1u38d1hZiMqNk_JmcAwmb11vPFMM-8YMM