HOTSPOT - (Topic 1)
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Solution:
Box 1: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Does this meet the goal?
Correct Answer:
A
- (Topic 4)
In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Correct Answer:
BD
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features
DRAG DROP - (Topic 5)
Match the Azure Cognitive Services to the appropriate Al workloads.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Solution:
Does this meet the goal?
Correct Answer:
A
- (Topic 5)
You have an Azure Machine Learning pipeline that contains a Split Data module. The Split Data module outputs to a Train Model module and a Score Model module. What is the function of the Split Data module?
Correct Answer:
A
HOTSPOT - (Topic 5)
brectly completes the sentence.
Solution:
Does this meet the goal?
Correct Answer:
A