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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Price: | $100 USD |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 65 |
| Available Languages: | Korean, Simplified Chinese, English, Spanish, Japanese, Portuguese (Brazil) |
| Passing Score: | 700/1000 |
| Exam Format: | Multiple response, Multiple choice |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Recommended: basic understanding of cloud computing and general AI/ML concepts. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 264
Which functionality does Amazon SageMaker Clarify provide?
Answer: D
Explanation:
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 References:
* 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.
NEW QUESTION # 265
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?
Answer: B
Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
* Fine-Tuning Strategy:
* To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
* This allows the model to learn the desired behavior or response style based on the provided examples.
* Why Option A is Correct:
* Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
* Customization: Ensures that the model is fine-tuned to the specific requirements of the company' s data and desired outputs.
* Why Other Options are Incorrect:
* B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
* C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
* D. Train on journals and textbooks: Lacks the specific format and labeling required for fine- tuning.
NEW QUESTION # 266
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
Answer: D
Explanation:
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 References:
* 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.
NEW QUESTION # 267
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
Answer: D
Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
* Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
* Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
* Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
* Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner References:
* Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
NEW QUESTION # 268
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements?
Answer: B
NEW QUESTION # 269
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AIF-C01 Passing Score: https://www.examdiscuss.com/Amazon/exam/AIF-C01/
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