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NEW QUESTION: 1
시맨틱 레이어 뷰에서 사용되는 차원의 예는 다음 중 두 필드입니까? 이 질문에 대한 두 가지 정답이 있습니다.
A. 포스트 데일
B. 문서 총계
C. 상품 코드
D. 품목 수량
Answer: A,B

NEW QUESTION: 2
あなたはProfessional Edition組織と協力しています。彼らは、4つのカスタムタブ、3つのカスタムオブジェクト、および1つのカスタムアプリを使用する必要がある経費トラッカーをインストールしたいと考えています。会社がすでに使用している場合
4つのアプリケーション、36のカスタムオブジェクト、7のカスタムタブで、Expense Trackerをインストールしようとするとどうなりますか？
A. インストールは成功しますが、レポートのみ、
B. カスタムタブの最大数に達したため、インストールプロセスを完了できません。
C. インストールは成功します
D. カスタムオブジェクトの最大数に達したため、インストールプロセスを完了できません。
Answer: B

NEW QUESTION: 3
Identity three transactions that can be entered as a pre-approved expenditure batch:
A. Miscellaneous
B. Supplier Invoices
C. Usages
D. Burden
E. Expense Reports
Answer: A,D,E
Explanation:
Pre-approved expenditures are timecards, expense reports(D), usage logs, miscellaneous transactions(E), burden transactions(A), inventory transactions, and work in process transactions usually completed on paper and approved by a supervisor. You enter preapprovedexpenditures into Oracle Projects in batch mode, generally on a weekly basis. After you enter the batches into the system, you submit them for review and then release them for cost distribution.
Reference:Pre-Approved Expenditures

NEW QUESTION: 4
パフォーマンスカーブの図に示されているように、広告応答モデルの新しいコストファクターシナリオを実装する必要があります。
どのテクニックを使用する必要がありますか？
A. Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.
B. Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.
C. Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.
D. Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.
Answer: D
Explanation:
Explanation
Scenario:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1
+/- 5%.
Topic 2, Case Study 2
Case study
Overview
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Datasets
There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:

The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
Dataset issues
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column.
The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.