QUESTION: 25

DRAG DROP (Drag and Drop is not supported)
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
Note: Each correct selection is worth one point.
Select and Place:

See Explanation section for answer.

Answer(s): A

Explanation:

Box 1: Knowledge mining
You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot.

Box 2: Computer vision
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis.

Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

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QUESTION: 26

DRAG DROP (Drag and Drop is not supported)
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
Note: Each correct selection is worth one point.
Select and Place:

See Explanation section for answer.

Answer(s): A

Explanation:

Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as
ROC, Precision/Recall, and Lift curves.

Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.

Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

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QUESTION: 27

HOTSPOT (Drag and Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Hot Area:

See Explanation section for answer.

Answer(s): A

Explanation:

Reference:

https://www.baeldung.com/cs/feature-vs-label https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/

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QUESTION: 28

You have the Predicted vs. True chart shown in the following exhibit.

Which type of model is the chart used to evaluate?

classification
regression
clustering

Answer(s): B

Explanation:

What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m

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Free AI-900 Exam Braindumps (page: 8)

QUESTION: 25

Explanation:

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QUESTION: 26

Explanation:

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QUESTION: 27

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QUESTION: 28

Explanation:

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