Drag and Drop Question You need to define a modeling strategy for ad response. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to…

Question

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Options

• 1Implement a K-Means Clustering model.
• 2Use the raw score as a feature in a Score Matchbox Recommender model.
• 3Use the cluster as a feature in a Decision Jungle model.
• 4Use the raw score as a feature in a Logistic Regression model.
• 5Implement a Sweep Clustering model.

Why each option

This question tests knowledge of a multi-stage machine learning pipeline for ad response (click-through) prediction using a stacking/chaining strategy in Azure Machine Learning. The correct sequence builds progressively richer features by chaining unsupervised and supervised models.

Concept tested: Multi-stage / stacked machine learning pipeline design for ad response prediction in Azure ML - combining unsupervised clustering (K-Means) with ensemble tree models (Decision Jungle) and a final calibrated classifier (Logistic Regression) in the correct dependency order.

Source: Microsoft Azure Machine Learning documentation - 'Train and evaluate clustering models' and 'Train classification models'; also covered in DP-100: Designing and Implementing a Data Science Solution on Azure exam objectives.

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