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Microsoft Azure DP-100 私の夢は最高のIT専門家になることです。

Microsoft Azure DP-100復習解答例 - Designing and Implementing a Data Science Solution on Azure そうであれば、あなたは夢がある人だと思います。 きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。

我々ShobhadoshiのITエリートと我々のMicrosoftのDP-100復習解答例試験のソフトに満足するお客様は我々に自信を持たせます。あなたのMicrosoftのDP-100復習解答例試験を準備する圧力を減少するのは我々の責任で、あなたにMicrosoftのDP-100復習解答例試験に合格させるのは我々の目標です。我々はほぼ100％の通過率であなたに安心させます。

Microsoft DP-100復習解答例 - あなたは最高のトレーニング資料を手に入れました。

近年、IT領域で競争がますます激しくなります。IT認証は同業種の欠くことができないものになりました。あなたはキャリアで良い昇進のチャンスを持ちたいのなら、ShobhadoshiのMicrosoftのDP-100復習解答例「Designing and Implementing a Data Science Solution on Azure」試験トレーニング資料を利用してMicrosoftの認証の証明書を取ることは良い方法です。現在、MicrosoftのDP-100復習解答例認定試験に受かりたいIT専門人員がたくさんいます。Shobhadoshiの試験トレーニング資料はMicrosoftのDP-100復習解答例認定試験の１００パーセントの合格率を保証します。

ITを通して自分の実力を証明したいのですか。IT業界に従事したいなら、IT認定試験を受験して認証資格を取得することは必要になります。

DP-100 PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 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