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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Available Languages: | English, Traditional Chinese, Simplified Chinese, Japanese, Korean |
| Exam Format: | Ordering, Multiple response, Multiple choice |
| Passing Score: | 700 (scaled score 100–1000) |
| Related Certifications: | AWS Certified Machine Learning – Specialty AWS Certified Cloud Practitioner |
| 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: | 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 |
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NEW QUESTION # 63
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
Answer: B
Explanation:
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.
AWS AI Practitioner Reference:
According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.
The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.
The other options do not directly relate to building vector database applications:
A . Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.
B . Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.
D . Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.
NEW QUESTION # 64
A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes.
Answer: B
Explanation:
* AWS HealthScribe provides speech-to-text and medical documentation generation, specifically designed for healthcare applications.
* Amazon Polly is text-to-speech, not speech-to-text.
* Amazon Rekognition is computer vision.
* Amazon Q Developer is a generative AI assistant for developers, not healthcare.
# Reference:
AWS Documentation - AWS HealthScribe
NEW QUESTION # 65
A company is using an Amazon Nova Canvas model to generate images. The model generates images successfully. The company needs to prevent the model from including specific items in the generated images.
Which solution will meet this requirement?
Answer: B
Explanation:
The correct answer is C - Use a negative prompt. Negative prompts instruct a generative image model to avoid certain features, objects, or styles in the output. This technique is fully supported by models like Amazon Nova Canvas on Bedrock, which are based on diffusion or image generation architectures. According to AWS documentation, negative prompts refine output control by telling the model what not to include, thereby improving brand alignment, compliance, or creative direction. A higher temperature increases randomness, not control. A detailed prompt helps, but without exclusion instructions, the model may still include unwanted elements. Changing the model may yield better output but doesn't directly solve this control requirement. Negative prompts are purpose-built for this scenario.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation - Prompt Engineering for Image Models
AWS Generative AI Guide - Controlled Generation with Negative Prompts
NEW QUESTION # 66
A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.
Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)
Answer: C,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:
A . Generation of content embeddings:When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern) C . Creation of the search index:After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper) B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.
Reference:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation
NEW QUESTION # 67
A company deploys a custom ML model on Amazon SageMaker AI. The company uses the model to build a generative AI application for a healthcare recommendation system.
The company tests the application and finds a potential bias issue. The application consistently recommends different treatment approaches for patients who have identical medical conditions based on patient demographic information.
The company needs a solution to ensure that the application does not generate biased recommendations.
Which solution will meet this requirement?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI best practices emphasize that bias should be detected, measured, mitigated, and monitored throughout the ML lifecycle, especially for sensitive domains such as healthcare. When biased outcomes are observed, AWS guidance recommends addressing bias at the data and model level, not only at the output level.
Using Amazon SageMaker Clarify aligns directly with AWS Responsible AI principles because it is designed to:
Detect and quantify bias in datasets and model predictions across sensitive attributes such as demographic groups Provide pre-training and post-training bias metrics, allowing practitioners to identify where bias originates Support data-centric mitigation, including improving dataset balance and representativeness After identifying bias with SageMaker Clarify, collecting additional balanced training data and retraining the model helps ensure that:
The model learns from a more representative dataset
Disparate treatment recommendations based on demographics are reduced
Fairness is improved while maintaining clinical accuracy
Why the other options are not sufficient or aligned with AWS best practices:
B . Prompt engineering can influence outputs but does not address underlying data or model bias and is not sufficient for regulated, high-risk domains like healthcare.
C . Content filtering removes outputs after generation but does not prevent biased decision-making by the model itself.
D . Separate FM endpoints by demographic group increases the risk of reinforcing bias and violates fairness principles rather than mitigating them.
AWS AI Study Guide Reference:
AWS Responsible AI principles: Fairness and Governance
Amazon SageMaker Clarify: bias detection and mitigation
AWS best practices for ML in high-risk domains such as healthcare
NEW QUESTION # 68
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