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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Passing Score: | 700 (scaled score 100β1000) |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning β Specialty |
| Available Languages: | Simplified Chinese, Japanese, Traditional Chinese, Korean, English |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Format: | Multiple response, Ordering, Multiple choice |
| Exam Price: | 100 USD |
| Exam Duration: | 90 minutes |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| Exam Registration: | Pearson VUE Scheduling AWS Certification Registration |
| 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 |
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NEW QUESTION # 369
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
Answer: A
Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
* Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
* Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
* Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real- time inference.
* Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
* Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
NEW QUESTION # 370
A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.
What are the key benefits of using Amazon Bedrock agents that could help this retailer?
Answer: C
Explanation:
Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.
* Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows":
This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.
* Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.
* Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.
* Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.
AWS AI Practitioner References:
* Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.
NEW QUESTION # 371
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
Answer: B
Explanation:
The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).
B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.
A and C require manual effort, increasing operational overhead.
D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
NEW QUESTION # 372
A company stores millions of PDF documents in an Amazon S3 bucket. The company needs to extract the text from the PDFs, generate summaries of the text, and index the summaries for fast searching.
Which combination of AWS services will meet these requirements? (Select TWO.)
Answer: A,C
Explanation:
Amazon Textract (E) automatically extracts text and structured data from scanned documents, such as PDFs.
Amazon Bedrock (B) offers access to LLMs (such as Amazon Titan or Anthropic Claude) for tasks like summarization and generating embeddings for search.
Workflow:
Amazon Textract extracts text from PDFs in S3.
Amazon Bedrock LLMs summarize the extracted text.
(Optional: Summaries can be indexed using Amazon OpenSearch or another search solution.) A (Translate) is for language translation, not extraction or summarization.
C (Transcribe) is for audio to text, not PDFs.
D (Polly) is for text-to-speech.
"Amazon Textract extracts text, forms, and tables from scanned documents... Bedrock provides generative AI models to perform summarization and other text generation tasks." (Reference: Amazon Textract, Amazon Bedrock, AWS GenAI RAG Reference)
NEW QUESTION # 373
A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.
Which combination of AWS service and storage class meets these requirements? (Select TWO.)
Answer: D,E
NEW QUESTION # 374
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