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Autonomous AI implementation engineer Microsoft Quiz Answers
Get Autonomous AI implementation engineer Microsoft Quiz Answers
In this path, you’ll learn to build the Machine Teaching for Autonomous AI brains that you learned to design in the “Autonomous AI Design Architect” learning path. Starting from the AI specification document of a use case, you’ll learn to build, train, and test brains using the Microsoft Project Bonsai platform. You’ll see firsthand the differentiated benefits of the Bonsai platform:
- a low-code platform with a graphical interface
- automatic selection and training of Deep Reinforcement Learning solutions that prevent unlearning
- automatic integration with simulations
- self-embedded scaling of up to 1000 parallel simulations
- flexible integrated deployment into production, through local or cloud integration.
In future expansions of this path, well also present Machine Teaching Experimentation, the iterative process of implementing, validating results, and modifying brain design, until the brain reaches expected performance. designs to diverse stakeholders.
At the end of this learning path, you’ll be able to:
- Build Autonomous AI brains in the Project Bonsai platform.
- Train Autonomous AI brains with monolithic and modular brain structures.
- Validate brain training and performance using the training graph and custom assessments, respectively.
- Visually understanding brain performance using embedded graph interfaces.
Produced in partnership with the University of Oxford – Ajit Jaokar Artificial Intelligence: Cloud and Edge Implementations course
Prerequisites:
None
Module 1: Teach AI like you would a human with Project Bonsai
Implementing a monolithic (one concept) brain. Set up your Bonsai workplace and train a brain. Learn how to read the assessment graphs that tell you how the brain is performing.
Learning objectives:
In this module, you’ll learn how to:
- Understand the Bonsai interface and the integration between brains and simulations
- Gain familiarity with the components and functionality of automatic assessments
- Translate an existing AI spec design into a trained Bonsai brain
- Evaluate the success of a Bonsai training session using the training data
“Produced in partnership with the University of Oxford – Ajit Jaokar Artificial Intelligence: Cloud and Edge Implementations course“
Prerequisites:
- Basic knowledge of Automated Intelligence
- Basic knowledge of Autonomous Intelligence
- Basic knowledge of Machine Teaching
- Basic knowledge of AI Specification document
- Basic knowledge of brain design patterns
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. What do you find in the selected area of the Microsoft Bonsai platform?
- A carousel with the Bonsai samples.
- All the brains that have been created in your workspace and its versions.
- All the simulators available in your workspace.
- Exported brains
Q2. The Moab sample is a:
- Modular brain
- Hybrid brain
- Monolithic brain
- None of the above
Q3. What would happen if we change the goal “Move to center” by “Avoid the center”:
- It’s an impossible task to learn, the brain won’t converge.
- The brain will constantly throw the ball off the plate.
- The brain will keep the ball within a donut area between the center and the periphery of the plate.
- The brain will keep the ball in the periphery of the plate.
Quiz 2: Knowledge check
Q1. Select the continuation of the following sentence. The brain training stops when…:
- It reaches 100% Goal Satisfaction.
- It reaches Goal Robustness of one.
- It finishes one episode.
- It reaches the No Progress Iteration Limit.
Q2. Each vertical group of dots in any brain assessment plot is:
- An objective’s performance value.
- A champion.
- A strategy.
- A goal objective
Q3. You should stop training the brain when:
- The goal satisfaction has not made any meaningful progress.
- At 200k iterations.
- It reaches 100% goal satisfaction.
- You don’t need to stop brain training.
Q4. What is Machine Teaching Experimentation?
- Planning a hypothesis to be tested on a brain training session.
- Running custom assessments on scenarios of interest.
- Comparing the brain against the benchmark.
- All of the above
Q5. Select the deployment option which is NOT currently supported by the Bonsai platform:
- Cloud
- IoT Edge
- Embedded devices
- Local deployment
Module 2: Build an industrial strength brain
Train a modular brain for real industrial processes. Decompose the tasks into skills, do Machine Teaching experimentation to get the orchestration and the training completed successfully. Evaluate brain performance using training graphs and custom assessments.
Learning objectives:
In this module, you’ll learn how to:
- Identify the benefits of “modular” brains against “monolithic” brains when tackling industrial use cases
- Explore the Machine Teaching training strategies required to build industrial strength brains
- Learn the benefits of using web visualizers and how to embed them into the Bonsai UI
- Understand how Autonomous control can have a positive impact in process performance
- Understand how industrial strength brains require more nuanced machine teaching
- Describe how assessing a modular brain is different than assessing a monolithic brain
“Produced in partnership with the University of Oxford – Ajit Jaokar Artificial Intelligence: Cloud and Edge Implementations course“
Prerequisites:
- Basic knowledge of Automated Intelligence
- Basic knowledge of Autonomous Intelligence
- Basic knowledge of Machine Teaching
- Basic knowledge of AI Specification document
- Basic knowledge of brain design patterns
- Basic knowledge of selecting use cases for Autonomous AI
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. How many sensors or environment states the CSTR sample has?
- One
- Three
- Four
- Five
Q2. What are the control actions of the CSTR sample?
- Cr
- Delta Tc(dTc)
- Cref
- Tc
Q3. Select the strategies of the CSTR sample?
- Select Strategy
- Cr, Tr, Cref, Tref, Tc
- Steady state and modify concentration
- Delta Tc(dTc)
Quiz 2: Knowledge check
Q1. Which is a goal objective of the skill/module ‘Steady state’?
- Randomized noise up to 5%.
- Minimize Concentration Reference.
- Episode Iteration Limit set to 90.
- Thermal runaway
Q2. The lesson to be learned by the “Steady State” strategy is:
- Practice transient conditions (concentration reference signal, Cref_signal, equal to 1).
- Practice with different thermal runaway temperatures.
- Practice steady state conditions (concentration reference signal, Cref_signal, equal to 5).
- Thermal temperature equal to 400 degrees Kelvin.
Conclusion:
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This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.
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