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WorksheetsMS Champ Month of Jan
Total questions: 15
Worksheet time: 8mins
What is Responsible AI?
a) An approach to developing AI safely and ethically
b) A new machine-learning algorithm
c) A type of neural network architecture
d) A dashboard for analyzing models
Which of these is not one of the six principles of Microsoft's Responsible AI Standard?
a) Fairness
b) Reliability & Safety
c) Accuracy
d) Inclusiveness
What does the Responsible AI Dashboard provide?
a) Access Azure Open AI Services
b) Automated model training
c) Data labelling
d) A central place to access Responsible AI tools
What are some components of the Responsible AI Dashboard?
a) Model debugging and business decision-making tools
b) Data visualization and database management
c) Automated data labelling and augmentation
d) Neural architecture search and hyperparameter tuning
How can you find model performance inconsistencies?
a) Compare metrics across cohorts
b) Analyze probability distributions
c) Visualize performance scores
d) All of these
What are some reasons to use the Responsible AI Dashboard?
a) Centralize access to Responsible AI tools
b) Streamline workflows
c) Improve tool integration
d) All of these
What do most of the traditional ML metrics focus on?
a) Ratio of correct vs incorrect predictions
b) Training time
c) Loss function values
d) Number of parameters
What metrics can show model performance for a cohort?
a) Accuracy, Precision, Recall, MAE, RMSE
b) Learning rate, batch size, epochs
c) Loss, gradient norm, step size
d) Flops, latency, throughput
What can probability distributions show?
a) Uncertainty estimates
b) The distribution of a cohort's predicted outcomes
c) Decision Boundaries
d) Feature importance
Model explainability can help with:
a) Compliance and auditing
b) Identifying biases
c) Increasing transparency
d) All of these
The Responsible AI Dashboard provides:
a) Unified access to Responsible AI tools
b) Automated pipeline creation
c) Hyperparameter optimization
d) Neural architecture search
When should you create a Responsible AI Dashboard?
a) Before training a model
b) During data collection
c) After developing a model
d) Before deployment
What are common issues that the Dashboard can uncover?
a) Biases, errors, unintended correlations
b) Underfitting, vanishing gradients
c) Class imbalance, label noise
d) High bias, high variance
The Dashboard tools can help:
a) Improve model safety and reliability
b) Speed up training
c) Simplify deployment
d) Reduce inference latency
Who can benefit from using the Dashboard?
a) Data scientists
b) Developers
c) Business decision-makers
d) All of these
