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NEW QUESTION: 1
A customer is planning on managing as many of their Oracle Field Service Cloud (OFSC) configuration elements as possible via the Metadata REST API.
Which three objects can be both retrieved and created/modified via the Metadata API?
A. Applications
B. Capacity Areas
C. Properties
D. Organizations
E. Capacity Categories
Answer: A,B,E

NEW QUESTION: 2
Azure App Serviceアプリがあります。
アプリのトレースを実装する必要があります。トレース情報には、次のものが含まれている必要があります。
*使用傾向
* AJAX呼び出し応答
*ブラウザによるページ読み込み速度
*サーバーとブラウザの例外
あなたは何をするべきか？
A. Azure Log AnalyticsでIISログを構成します。
B. Azure Log Analyticsでカスタムログを構成します。
C. Azure Network Watcherで接続モニターを構成します。
D. Azure Application Insightsサイト拡張を有効にします。
Answer: D
Explanation:
For web pages, Application Insights JavaScript SDK automatically collects AJAX calls as dependencies.
Note: Some of the things you can track or collect are:
What are the most popular webpages in your application, at what time of day and where is that traffic coming from?
Dependency rates or response times and failure rates to find out if there's an external service that's causing performance issues on your app, maybe a user is using a portal to get through to your application and there are response time issues going through there for instance.
Exceptions for both server and browser information, as well as page views and load performance from the end users' side.
Reference:
https://azure.microsoft.com/en-us/blog/ajax-collection-in-application-insights/
https://blog.pragmaticworks.com/what-is-application-insights

NEW QUESTION: 3
You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:

The data is collected every 12 hours: noon and midnight.
You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.
You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.
You need to configure the automated machine learning run.
How should you complete the AutoMLConfig definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Answer:
Explanation:

Explanation:
Box 1: forcasting
Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.
Box 2: temperature
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).
Box 3: observation_time
time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.
Box 4: 7
"predicts temperature over the next seven days"
max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.
Box 5: 50
"For the initial round of training, you want to train a maximum of 50 different models." Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig