我々Goldmile-Infobizはお客様の立場でお客様に最高のサービスを提供します。全日でのオンライン係員、MicrosoftのDP-100専門知識訓練試験資料のデモ、豊富なバーション、MicrosoftのDP-100専門知識訓練試験資料を購入した後の無料更新、試験に失敗した後の全額の返金…これら全部は我々Goldmile-Infobizが信頼される理由です。あなたは我々のソフトを通してMicrosoftのDP-100専門知識訓練試験に順調に合格したら、私たちの共同の努力を覚えられると希望します。 でも、試験に合格するために大量な時間とエネルギーを費やすことはなく、Goldmile-InfobizのMicrosoftのDP-100専門知識訓練試験トレーニング資料を選んだらいいです。Goldmile-Infobizのトレーニング資料はIT認証試験に受かるために特別に研究されたものですから、この資料を手に入れたら難しいMicrosoftのDP-100専門知識訓練認定試験に気楽に合格することができるようになります。 Goldmile-Infobiz DP-100専門知識訓練問題集を使って試験に合格しない場合に、当社は全額返金できます。
Microsoft Azure DP-100 でも大丈夫です。
弊社のDP-100 - Designing and Implementing a Data Science Solution on Azure専門知識訓練試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後DP-100 - Designing and Implementing a Data Science Solution on Azure専門知識訓練試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。 この問題集には実際の試験に出る可能性のあるすべての問題が含まれています。従って、この問題集を真面目に学ぶ限り、DP-100 認証試験認定試験に合格するのは難しいことではありません。
もしあなたはDP-100専門知識訓練試験に合格しなかったら、全額返金のことを承諾します。我々Goldmile-Infobizは一番行き届いたアフタサービスを提供します。Microsoft DP-100専門知識訓練試験問題集を購買してから、一年間の無料更新を楽しみにしています。
その他、Microsoft DP-100専門知識訓練問題集の更新版を無料に提供します。
従来の試験によってGoldmile-Infobiz が今年のMicrosoftのDP-100専門知識訓練認定試験を予測してもっとも真実に近い問題集を研究し続けます。Goldmile-Infobizは100%でMicrosoftのDP-100専門知識訓練「Designing and Implementing a Data Science Solution on Azure」認定試験に合格するのを保証いたします。
弊社のMicrosoftのDP-100専門知識訓練練習問題を利用したら、あなたは気楽に勉強するだけではなく、順調に試験に合格します。多くの人々はMicrosoftのDP-100専門知識訓練試験に合格できるのは難しいことであると思っています。
DP-100 PDF DEMO:
QUESTION NO: 1
You are developing a hands-on workshop to introduce Docker for Windows to attendees.
You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Windows 10 64-bit Professional
B. Kitematic
C. BIOS-enabled virtualization
D. VirtualBox
E. Microsoft Hardware-Assisted Virtualization Detection Tool
Answer: A,C
Explanation:
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:
E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
References:
https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use- on-windows-10/
QUESTION NO: 2
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
A. Preprocess Text
B. Edit Metadata
C. Execute Python Script
D. Latent Dirichlet Allocation
Answer: B
Explanation:
Typical metadata changes might include marking columns as features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata
QUESTION NO: 3
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
QUESTION NO: 4
You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
* iterate all possible combinations of hyperparameters
* minimize computing resources required to perform the sweep
* You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
A. Sweep clustering
B. Entire grid
C. Random grid
D. Random seed
E. Random sweep
Answer: C
Explanation:
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Incorrect Answers:
B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.
E: If you choose a random sweep, you can specify how many times the model should be trained, using a random combination of parameter values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model- hyperparameters
QUESTION NO: 5
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
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Updated: May 28, 2022