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NEW QUESTION: 1
Ein Benutzer erstellt eine Eingangszahlung für einen Kunden und wählt zwei zu zahlende Rechnungen aus. Wenn der Benutzer versucht, die Zahlung zu buchen, wird die im angehängten Screenshot angezeigte Systemmeldung angezeigt.
Was sind mögliche Gründe für diese Systemmeldung? Hinweis: Auf diese Frage gibt es 2 richtige Antworten.

A. Die Rechnungen sind noch NICHT fällig
B. Der Nutzer hat KEIN Zahlungsmittel ausgewählt
C. Der Betrag im Fenster Zahlungsmittel stimmt NICHT mit dem Gesamtbetrag der Rechnungen überein
D. Der Kunde verwendet eine Fremdwährung und der Wechselkurs ist NICHT eingerichtet
Answer: B,C

NEW QUESTION: 2
CSVファイルからテキストを前処理する予定です。 Azure Machine Learning Studioのデフォルトのストップワードリストをロードします。
次の要件を満たすように、テキストの前処理モジュールを構成する必要があります。
*単一の標準形式から複数の関連する単語を確認します。
*テキストからパイプ文字を削除します。
*情報検索を最適化するために単語を削除します。
どの3つのオプションを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
Topic 1, Case Study
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.

NEW QUESTION: 3
Sie entwickeln eine Datenspeicherlösung für eine Social-Networking-App.
Für die Lösung ist eine mobile App erforderlich, in der Benutzerinformationen mithilfe von Azure Table Storage gespeichert werden.
Sie müssen Code entwickeln, der mehrere Sätze von Benutzerinformationen einfügen kann.
Wie solltest du den Code vervollständigen? Wählen Sie zum Beantworten die entsprechenden Optionen im Antwortbereich aus.
HINWEIS: Jede richtige Auswahl ist einen Punkt wert.

Answer:
Explanation:

Explanation:
Box 1, Box 2: TableBatchOperation
Create the batch operation.
TableBatchOperation op = new TableBatchOperation();
Box 3: ExecuteBatch
/ Execute the batch operation.
table.ExecuteBatch(op);
Note: You can insert a batch of entities into a table in one write operation. Some other notes on batch operations:
You can perform updates, deletes, and inserts in the same single batch operation.
A single batch operation can include up to 100 entities.
All entities in a single batch operation must have the same partition key.
While it is possible to perform a query as a batch operation, it must be the only operation in the batch.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/table-storage-how-to-use-dotnet