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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Related Certifications: | AWS Certified Machine Learning Engineer - Associate AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate |
| Exam Price: | USD 100 |
| Exam Duration: | 90 minutes |
| Available Languages: | English, Korean, Japanese, Simplified Chinese, Portuguese (Brazil) |
| Exam Format: | Multiple choice, Multiple response |
| Passing Score: | 700 / 1000 |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 80 |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 84
A company wants to learn about generative AI applications in an experimental environment.
Which solution will meet this requirement MOST cost-effectively?
Answer: B
Explanation:
The correct answer is Amazon Bedrock PartyRock, a playground for building and experimenting with generative AI apps in a low-cost, no-code environment. PartyRock is designed for innovation and learning. It enables users to try out prompts, LLM apps, and templates using Amazon Bedrock under a free-tier friendly setup. According to AWS, PartyRock abstracts infrastructure and allows rapid prototyping using models from Bedrock providers. This makes it ideal for early experimentation, especially for non-developers or those not ready to invest in full production pipelines. In contrast, Amazon Q Developer is for software engineering tasks, SageMaker JumpStart focuses on deploying ML models, and Q Business targets enterprise knowledge workers. None of those are as cost-effective and experimental-focused as PartyRock.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation - PartyRock Overview
AWS Generative AI Learning Path - Getting Started Tools
NEW QUESTION # 85
A financial company is using ML to help with some of the company's tasks.
Which option is a use of generative AI models?
Answer: A
Explanation:
Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text-like customer complaints-is a classic application of generative AI.
* A is correct:
"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text." (Reference: AWS Generative AI Use Cases)
* B and C are standard ML classification/segmentation tasks.
* D is a regression/prediction task, not generative.
NEW QUESTION # 86
A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages. Which evaluation metric will meet these requirements?
Answer: C
Explanation:
BLEU (Bilingual Evaluation Understudy) is the standard metric for evaluating machine translation quality across multiple languages.
ROUGE is for summarization quality (not translation).
AUC is for classification model performance.
Precision is a general metric but not specific for evaluating translations.
Reference:
AWS Documentation - Evaluation Metrics for NLP
NEW QUESTION # 87
A company is building a conversational AI assistant by using Amazon Bedrock AgentCore. The assistant must maintain context across multiple user interactions without requiring the company to manage infrastructure.
Which AgentCore feature meets these requirements?
Answer: D
Explanation:
The verified answer is C. Memory . In Amazon Bedrock AgentCore, Memory is the feature designed to preserve conversational context and make an assistant remember relevant information across interactions.
AWS documentation states that AgentCore Memory supports both short-term and long-term memory. Short- term memory stores raw interactions that help the agent maintain context within a single session, while long- term memory automatically extracts and stores key insights from conversations across multiple sessions, including user preferences, important facts, and session summaries. This directly matches the requirement because the assistant must maintain context across multiple user interactions without the company building and operating its own memory infrastructure.
Option A. Gateway is incorrect because AgentCore Gateway is used to connect agents securely to tools, APIs, and enterprise services. It helps expose external capabilities to agents, but it does not provide persistent conversational context. Option B. Browser Tool is incorrect because the Browser Tool enables an agent to interact with websites or web-based workflows. It is useful for browser automation and web interaction, not memory retention. Option D. Code Interpreter is incorrect because Code Interpreter allows the agent to execute code for tasks such as calculations, file analysis, and data manipulation. It does not solve the requirement of remembering prior conversation details.
The key phrase in the question is "maintain context across multiple user interactions." That is exactly the purpose of AgentCore Memory. AWS also explains that long-term memory provides persistent storage for session-specific context, enabling agents to maintain continuity and personalization across interactions.
Therefore, when a conversational assistant needs continuity, personalization, and context retention without custom infrastructure management, the correct AgentCore feature is Memory .
NEW QUESTION # 88
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?
Answer: A
Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.
NEW QUESTION # 89
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