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| Section | Weight | Objectives |
|---|---|---|
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Data protection and privacy in AI workflows |
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems |
| Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation |
| Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications |
>> AIF-C01 Relevant Questions <<
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NEW QUESTION # 105
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: A
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 # 106
Which functionality does Amazon SageMaker Clarify provide?
Answer: A
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner Reference:
Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
NEW QUESTION # 107
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Answer: B
Explanation:
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
* Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
* Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
* Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
* Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner References:
* Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
NEW QUESTION # 108
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
Answer: A
NEW QUESTION # 109
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
Answer: B
NEW QUESTION # 110
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