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1. "Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

A) Maximize accuracy and recall
B) Maximize specificity number of classes
C) Maximize precision and accuracy
D) Maximize recall and precision

2. Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

A) Check the input test data for potential sample bias.
B) Testing the data pipeline for any sources for algorithmic bias.
C) Test the model during model evaluation for data bias.
D) Testing the distribution shift in the training data for inappropriate bias.

3. The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION

A) Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
B) Individual bias at the neuron level, and weights assigned to the connections between theneurons.
C) Individual bias at the neuron level, and activation values of neurons in the previous layer.
D) Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

4. Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A) A comparison of the performance of an ML system on two different input datasets.
B) A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
C) A comparison of two different websites for the same company to observe from a user acceptance perspective.
D) A comparison of the performance of two different ML implementations on the same input data.

Solutions: