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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) - Foundation models and prompt engineering basics |
| Applications of Foundation Models | - Use cases for generative AI in business - AI-powered assistants and automation - Content generation and summarization |
| Fundamentals of Artificial Intelligence and Machine Learning | - Data fundamentals for AI/ML - Core AI and ML concepts - Common ML workflows and lifecycle |
| Responsible AI and Security | - Bias, fairness, and explainability - Security, privacy, and governance in AI systems - AI ethics and responsible use |
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NEW QUESTION # 77
An AI practitioner is developing a new ML model. After training the model, the AI practitioner evaluates the accuracy of the model's predictions. The model's accuracy is low when the model uses both the training dataset and the test dataset.
Which scenario is the MOST likely cause of this problem?
Answer: C
Explanation:
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. AWS documentation explains that an underfit model performs poorly on both training and test datasets, which directly matches the scenario described.
In this case, the model shows low accuracy during training and evaluation, indicating that it has not learned sufficient relationships from the data. AWS identifies common causes of underfitting as insufficient model complexity, inadequate feature representation, overly aggressive regularization, or insufficient training time.
Underfitting is different from overfitting. Overfitting occurs when a model performs well on training data but poorly on test data, which is not the situation here. Hallucination applies to generative AI outputs, not supervised ML model accuracy. Cross-validation is a model evaluation technique, not a cause of poor performance.
AWS emphasizes the importance of diagnosing underfitting early in the model development lifecycle.
Remedies include increasing model complexity, adding relevant features, reducing regularization, or selecting a more expressive algorithm. These steps allow the model to better learn from the data and improve accuracy across both training and test sets.
AWS machine learning best practices clearly associate low performance on both datasets with underfitting, making this the most likely cause of the problem described.
NEW QUESTION # 78
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
NEW QUESTION # 79
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
Answer: D
Explanation:
Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.
Option C (Correct): "Amazon Q in Amazon QuickSight": This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.
Option A, B, and D: These options are incorrect:
A . Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.
B . Amazon Q Developer: This is not an existing AWS service or feature.
D . Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.
AWS AI Practitioner Reference:
Amazon QuickSight Q is designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.
Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.
NEW QUESTION # 80
A company is using Amazon Bedrock Agents to build an application to automate business workflows.
Answer: D
NEW QUESTION # 81
An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.
Which model type meets these requirements?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
A multimodal large language model (LLM) can:
* Accept both text and image inputs
* Understand visual and textual context
* Generate coherent written explanations
AWS generative AI guidance positions multimodal LLMs as the best choice for applications requiring cross- modal understanding and text generation.
Why the other options are incorrect:
* Computer vision (A) does not generate text explanations.
* Diffusion models (C) generate images.
* Text-to-speech (D) converts text to audio.
AWS AI document references:
* Multimodal Foundation Models on AWS
* Building AI Tutors with Generative Models
* Text and Image Understanding with LLMs
NEW QUESTION # 82
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