Practice Free AI-900 Exam Online Questions

You need to provide customers with the ability to query the status of orders by using phones, social media, or digital assistants.

What should you use?

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DRAG DROP

Match the tasks to the appropriate machine learning models. To answer, drag the appropriate model from the column on the left to its scenario on the right. Each model may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.

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HOTSPOT

Select the answer that correctly completes the sentence

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HOTSPOT

You have the following apps:

• App1: Uses a set of images and photos to extract brand names

• App2: Enables touchless access control for buildings

Which Azure Al Vision service does each app use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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HOTSPOT

You have a database that contains a list of employees and their photos.

You are tagging new photos of the employees.

For each of the following statements select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

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DRAG DROP

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.

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DRAG DROP

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.

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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.

You are building a tool that will process images from retail stores and identify the products of competitors.

The solution will use a custom model.

Which Azure Cognitive Services service should you use?

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HOTSPOT

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE Each correct selection is worth one point

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Correct Answer: