現在IT技術会社に通勤しているあなたは、MicrosoftのDP-100日本語講座試験認定を取得しましたか？DP-100日本語講座試験認定は給料の増加とジョブのプロモーションに役立ちます。短時間でDP-100日本語講座試験に一発合格したいなら、我々社のMicrosoftのDP-100日本語講座資料を参考しましょう。また、DP-100日本語講座問題集に疑問があると、メールで問い合わせてください。 だから、Microsoft DP-100日本語講座試験参考書を早く購入しましょう！多くの受験者にとって、DP-100日本語講座試験資格証明書を取得することは簡単ではないです。 人によって目標が違いますが、あなたにMicrosoft DP-100日本語講座試験に順調に合格できるのは我々の共同の目標です。
Microsoft Azure DP-100 弊社はあなた１００％合格率を保証いたします。
Microsoft Azure DP-100日本語講座 - Designing and Implementing a Data Science Solution on Azure Omgzlook を選択して１００％の合格率を確保することができて、もし試験に失敗したら、Omgzlookが全額で返金いたします。 Omgzlookが提供した資料は最も全面的で、しかも更新の最も速いです。Omgzlookはその近道を提供し、君の多くの時間と労力も節約します。
あなたはインターネットでMicrosoftのDP-100日本語講座認証試験の練習問題と解答の試用版を無料でダウンロードしてください。そうしたらあなたはOmgzlookが用意した問題集にもっと自信があります。早くOmgzlookの問題集を君の手に入れましょう。
Microsoft DP-100日本語講座 - きっと試験に合格しますよ。
Omgzlook のMicrosoftのDP-100日本語講座問題集はシラバスに従って、それにDP-100日本語講座認定試験の実際に従って、あなたがもっとも短い時間で最高かつ最新の情報をもらえるように、弊社はトレーニング資料を常にアップグレードしています。弊社のDP-100日本語講座のトレーニング資料を買ったら、一年間の無料更新サービスを差し上げます。もっと長い時間をもらって試験を準備したいのなら、あなたがいつでもサブスクリプションの期間を伸びることができます。
OmgzlookのMicrosoftのDP-100日本語講座勉強資料は問題と解答を含めています。それは実践の検査に合格したソフトですから、全ての関連するIT認証に満たすことができます。
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