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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - Recognize tools for building and deploying generative AI solutions - Explain integration of foundation models into applications - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
| Fundamentals of Generative AI | 24% | - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Explain capabilities and use cases of generative AI |
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning |
| Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems |
| Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Security controls for AI data and models |
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NEW QUESTION # 419
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 # 420
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?
Answer: C
Explanation:
Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.
* A is correct:
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview)
* B (Amazon Personalize) is for recommendations, not general ML or waste reduction.
* C (CloudWatch) is for monitoring, not ML training or deployment.
* D (Rekognition) is for image/video analysis.
NEW QUESTION # 421
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
Answer:
Explanation:
NEW QUESTION # 422
A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?
Answer: A
Explanation:
Amazon Comprehend toxicity detection is a managed NLP service that can analyze text for harmful or toxic language using pre-trained models-no need for labeled data or custom training.
B is correct: Comprehend's toxicity detection API is designed for this use case, works out-of-the-box, and requires no data labeling or model training.
A (Rekognition) is for image and video content moderation.
C would require labeled data for training.
D (Polly) is for text-to-speech, not content moderation.
"Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data." (Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)
"Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data." (Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)
NEW QUESTION # 423
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: B
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 # 424
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