Microsoft Updated DP-100 Exam Questions and Answers by layth

Step 1: Implement a K-Means Clustering model

Step 2: Use the cluster as a feature in a Decision jungle model.

Decision jungles are non-parametric models, which can represent non-linear decision boundaries.

Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.

Scenario:

Ad response rated declined.

Ad response models must be trained at the beginning of each event and applied during the sporting event.

Market segmentation models must optimize for similar ad response history.

Ad response models must support non-linear boundaries of features.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender

Scenario:

Experiments for local crowd sentiment models must combine local penalty detection data.

Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.

Note: Evaluate the changed in correlation between model error rate and centroid distance

In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.

References:

https://en.wikipedia.org/wiki/Nearest_centroid_classifier

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering

Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.

In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript."

Scenario:

Local penalty detection models must be written by using BrainScript.

References:

https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics

Scenario:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1 +/- 5%.