DP-100関連問題資料 & DP-100復習過去問
P.S. JPNTestがGoogle Driveで共有している無料かつ新しいDP-100ダンプ：https://drive.google.com/open?id=1wVqz_yTZBdoBVP4wDzd0867H2LdcA0VH
Microsoft DP-100試験のAPPテストエンジンは、ほとんどの認定候補者がファッションであり、この新しい学習方法に簡単に適応できるため、少なくとも60％の受験者に人気があります。 DP-100試験のAPPテストエンジンは、いつでもどこでも使用できると考える人がいます。 また、候補者の一部は、このバージョンでは実際のテストで実際のシーンをシミュレートできると考えています。 ブラウザを開くことができれば、学ぶことができます。 また、オフラインで学習したい場合は、DP-100試験のAPPテストエンジンをダウンロードしてインストールした後、キャッシュをクリアしないでください。
あなたのIT領域での能力を証明したいのですか。もっと多くの認可と就職機会を貰いたいのですか。MicrosoftのDP-100試験はあなたの必要のある証明です。IT業界でのほとんどの人はMicrosoftのDP-100試験の重要性を知っています。だれでもエネルギーは限られていますから、短い時間でMicrosoftのDP-100試験に合格したいなら、我々JPNTestの提供するソフトはあなたを助けることができます。豊富な問題と分析で作るソフトであなたはMicrosoftのDP-100試験に合格することができます。
認定するMicrosoft DP-100関連問題資料 & 合格スムーズDP-100復習過去問 | 有効的なDP-100関連日本語版問題集
Microsoftはコンテンツだけでなくディスプレイでも、DP-100テスト準備の設計に最新のテクノロジーを適用しました。 結果として、あなたは変化する世界に歩調を合わせ、DP-100トレーニング資料であなたの利点を維持することができます。 また、DP-100試験の重要な知識を個人的に統合し、カスタマイズされた学習スケジュールやDesigning and Implementing a Data Science Solution on Azureリストを毎日設計できます。 最後になりましたが、アフターサービスは、DP-100ガイド急流で最も魅力的なプロジェクトになる可能性があります。
Microsoft Designing and Implementing a Data Science Solution on Azure 認定 DP-100 試験問題 (Q162-Q167):
質問 # 162
You create a batch inference pipeline by using the Azure ML SDK.
You configure the pipeline parameters by executing the following code:
You need to obtain the output from the pipeline execution.
Where will you find the output?
正解：A
解説：
output_action (str): How the output is to be organized. Currently supported values are
'append_row' and 'summary_only'.
'append_row' ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig
質問 # 163
You have a Jupyter Notebook that contains Python code that is used to train a model.
You must create a Python script for the production deployment. The solution must minimize code maintenance.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：C、D
解説：
Reference:
https://www.guru99.com/learn-python-main-function-with-examples-understand-main.html
https://towardsdatascience.com/from-jupyter-notebook-to-deployment-a-straightforward-example-
1838c203a437
質問 # 164
You create an Azure Machine Learning workspace.
You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment.
The logged image must be available from the images tab in Azure Machine Learning Studio.
You need to complete the script.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
質問 # 165
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
質問 # 166
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.
正解：
解説：
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-recommende
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
*Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
*Access a user's tendency to respond to an advertisement.
*Customize styles of ads served on mobile devices.
*Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
*Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1
+/-5%.
* The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:
Penalty detection and sentiment
Findings
*Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
*Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
*Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
*Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
*Global penalty detection models must be trained by using dynamic runtime graph computation during training.
*Local penalty detection models must be written by using BrainScript.
* 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.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
*Ad response rates declined.
*Drops were not consistent across ad styles.
*The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
*Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
*All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
*Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
*The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.
質問 # 167
......
DP-100試験の認証資格を取得したら、あなたは利益を得られます。あなたは試験に参加したいなら、我々のDP-100問題集はあなたの最高の復習方法です。この問題集で、あなたは気楽でDP-100試験に合格することができます。我々の資料があったら、あなたは試験の復習を心配する必要がありません。
DP-100復習過去問: https://www.jpntest.com/shiken/DP-100-mondaishu
Microsoft DP-100関連問題資料 テストバンクには、実際の試験に含まれる可能性のあるすべての質問と回答、および過去の試験問題の本質と要約が含まれています、Microsoft DP-100関連問題資料 そして、10年以上にわたってこのキャリアでプロフェッショナルであったため、あなたの成功を確実にすることができます、Microsoft DP-100関連問題資料 ですから、躊躇しないではやく試験を申し込みましょう、これは品質の問題だけではなく、もっと大切なのは、JPNTestのMicrosoftのDP-100試験資料は全てのIT認証試験に適用するもので、ITの各領域で使用できます、MicrosoftのDP-100試験に合格するのに、私たちは最も早い時間で合格するのを追求します。
闇に白い息が浮かびつづけ、その内頬を地面にうねらせ熱い唇にふさがれ、今さらキDP-100スに麻薬の錠剤が含まれていたのだと気付いた、ちなみに、他のアウトドア系の媒体は軒並みこぞって参加しており、誰ひとり参加しなかったのはうちだけだったそうだ。
検証するDP-100関連問題資料 & 合格スムーズDP-100復習過去問 | 100％合格率のDP-100関連日本語版問題集 Designing and Implementing a Data Science Solution on Azure
テストバンクには、実際の試験に含まれる可能性のあるすべての質問と回答、および過DP-100日本語版試験解答去の試験問題の本質と要約が含まれています、そして、10年以上にわたってこのキャリアでプロフェッショナルであったため、あなたの成功を確実にすることができます。
ですから、躊躇しないではやく試験を申し込みましょう、これは品質の問題だけではなく、もっと大切なのは、JPNTestのMicrosoftのDP-100試験資料は全てのIT認証試験に適用するもので、ITの各領域で使用できます。
MicrosoftのDP-100試験に合格するのに、私たちは最も早い時間で合格するのを追求します。
さらに、JPNTest DP-100ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1wVqz_yTZBdoBVP4wDzd0867H2LdcA0VH