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NEW QUESTION: 1
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
NEW QUESTION: 2
A. Option D
B. Option A
C. Option B
D. Option C
Answer: B
NEW QUESTION: 3
Your network contains a System Center 2012 Configuration Manager environment.
Software Inventory and Hardware Inventory are enabled for all of the client computers.
All of the client computers have an Application named App1 installed. App1 saves files to the C:\Program Files\App1 folder. All of the files saved by App1 have a file name extension of .xyz.
You configure Software Inventory to inventory all of the files that have the .xyz extension and the .exe extension.
After six months, you discover that some of the client computers fad to inventory .xyz files. All of the client computers inventory .exe files.
You need to ensure that the .xyz files are inventoried.
What should you do?
A. Modify C:\Program Files\App1\Skpswi.dat.
B. Delete C:\NO_SMS_On_Drive.sms.
C. Delete C:\Program Files\App1\Skpswi.dat.
D. Modify C:\NO_SMS_On_Drive.sms.
Answer: C
Explanation:
Explanation/Reference:
To avoid the overhead of running software inventory on large disks, you can create a hidden file named Skpswi.dat and place it in the root folder of each disk drive that you want excluded from software inventory. Software inventory does not scan these drives unless the Skpswi.dat file is removed. You can also place a Skpswi.dat file in the folder that is at the top of the path of a software inventory collection rule.
For example, if you have a rule to inventory "\Program Files," that entire folder tree is skipped on any SMS client that has a Skpswi.dat file in the \Program Files folder.
Disks with a Skpswi.dat file are not scanned to find files that are to be collected.
So, when u delete the file C:\Program Files\App1\Skpswi.dat, the folder App1 is scanned.