DP-100受験対策書、DP-100対応受験 - Microsoft DP-100認定テキスト - Omgzlook

OmgzlookのMicrosoftのDP-100受験対策書「Designing and Implementing a Data Science Solution on Azure」試験問題集はあなたが成功へのショートカットを与えます。IT 職員はほとんど行動しましたから、あなたはまだ何を待っているのですか。ためらわずにOmgzlookのMicrosoftのDP-100受験対策書試験トレーニング資料を購入しましょう。 このような素晴らしい資料をぜひ見逃さないでください。IT技術の急速な発展につれて、IT認証試験の問題は常に変更されています。 このサイトはIT認定試験を受けた受験生から広く好評されました。

Microsoft Azure DP-100 自分の幸せは自分で作るものだと思われます。

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Microsoft DP-100受験対策書 - これはあなたの能力を認めます。

多分、DP-100受験対策書テスト質問の数が伝統的な問題の数倍である。Microsoft DP-100受験対策書試験参考書は全ての知識を含めて、全面的です。そして、DP-100受験対策書試験参考書の問題は本当の試験問題とだいたい同じことであるとわかります。DP-100受験対策書試験参考書があれば,ほかの試験参考書を勉強する必要がないです。

この時代にはIT資格認証を取得するは重要になります。それでは、DP-100受験対策書試験に参加しよう人々は弊社OmgzlookのDP-100受験対策書問題集を選らんで勉強して、一発合格して、MicrosoftIT資格証明書を受け取れます。

DP-100 PDF DEMO:

QUESTION NO: 1
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Step 1: Define a cross-entropy function activation
When using a neural network to perform classification and prediction, it is usually better to use cross- entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.
Step 2: Add cost functions for each target state.
Step 3: Evaluated the distance error metric.
References:
https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization- techniques/

QUESTION NO: 2
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 the answer area and arrange them in the correct order.
Answer:
Explanation:
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

QUESTION NO: 3
You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:

QUESTION NO: 4
You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?
A. Test Hypothesis Using t-Test
B. Group Data into Bins
C. Assign Data to Clusters
D. Partition and Sample
Answer: D
Explanation:
Partition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and- sample

QUESTION NO: 5
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply an Equal Width with Custom Start and Stop binning mode.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Use the Entropy MDL binning mode which has a target column.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data- into-bins

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Updated: May 28, 2022