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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Available Languages: | Korean, Japanese, English, Spanish, Portuguese (Brazil), Simplified Chinese |
| Real Exam Qty: | 65 |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700/1000 |
| Exam Price: | $100 USD |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Exam Format: | Multiple response, Multiple choice |
| Recommended Training: | AWS Certified AI Practitioner Official Exam Guide AWS Skill Builder - AI Practitioner Learning Plan |
| Exam Registration: | AWS Training and Certification Registration AWS Certification Portal |
| 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 # 210
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?
Answer: A
Explanation:
Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.
Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms": This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.
Option A: "Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.
Option C: "Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.
Option D: "Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.
AWS AI Practitioner Reference:
Domain Adaptation in Amazon Bedrock: AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.
NEW QUESTION # 211
A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and labeling the classes.
Which metric should the company use?
Answer: B
Explanation:
The correct answer is D - F1 score, which is the harmonic mean of precision and recall. AWS documentation states that the F1 score is the preferred metric when evaluating ML models trained on imbalanced datasets, where one class appears significantly more often than others. Accuracy becomes misleading in these situations because a model can achieve high accuracy simply by predicting the majority class. Precision alone measures correctness of positive predictions, while recall measures completeness-neither fully captures class balancing performance. The F1 score provides a combined measurement that reflects how well the model identifies minority classes while avoiding excessive false positives. AWS's ML Specialty Guide recommends using F1 for classification tasks where "class distribution is uneven or where both false negatives and false positives matter." This metric provides a fairer assessment of overall model performance across all class categories.
Referenced AWS Documentation:
* AWS Certified Machine Learning Specialty Guide - Metrics for Imbalanced Classes
* Amazon SageMaker Developer Guide - Evaluating Classification Models
NEW QUESTION # 212
A company is building an educational application to teach various topics. The company needs a pre-trained model to supply generic content for the topics.
Which solution will meet these requirements?
Answer: C
Explanation:
An Amazon Bedrock foundation model is the appropriate solution because the requirement is specifically for a pre-trained general-purpose model that can generate generic educational content across multiple topics.
AWS defines a foundation model as a model with many parameters that is "trained on a massive amount of diverse data" and capable of generating responses for a wide range of use cases. AWS also explains that foundation models are pre-trained and can generalize across domains and tasks with little or no additional training.
Amazon Bedrock provides managed access to foundation models from AWS and other model providers. An application can send natural-language prompts to an appropriate FM and receive generated explanations, summaries, questions, examples, or instructional content. Because the requested material is generic rather than highly specialized proprietary knowledge, the company can initially use the model's pre-trained knowledge without building and training a new model.
Option A requires the organization to collect internet data and operate its own compute environment. This adds development, data-governance, crawling, storage, and infrastructure-management overhead without addressing the requirement efficiently.
Option B provides serverless compute and object storage but does not itself provide a pre-trained content-generation model.
Option D, building a custom model with SageMaker AI, can be appropriate when an organization requires specialized ML training or complete control over its model lifecycle. However, developing a custom model is unnecessary when a suitable pre-trained FM already provides the required generic content-generation capabilities.
AWS emphasizes that organizations can often use an existing pre-trained FM, customize it through prompts, or later fine-tune it if the business requires greater domain specialization. This avoids the enormous cost of building a foundation model from scratch.
Therefore, the most direct, operationally efficient, and AWS-aligned answer is C. Use an Amazon Bedrock foundation model to build the educational application.
NEW QUESTION # 213
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)
Answer: D,E
Explanation:
I'll continue with more questions. Stay tuned!
NEW QUESTION # 214
A company has trained a custom foundation model (FM). The company wants to evaluate the toxicity of the FM ' s outputs by using human reviewers. The company has a team of internal reviewers. The company also wants to include external teams of reviewers to scale operations.
Which AWS service or feature will meet these requirements?
Answer: A
NEW QUESTION # 215
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