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NEW QUESTION: 1
特性を左側から右側に記載されているルーティングプロトコルにドラッグアンドドロップします。

Answer:
Explanation:

Explanation

NEW QUESTION: 2
-----
A customer has a head office with a number of branch offices that have no local servers. The customer requires a file-serving solution that provides these benefits:
High availability
Low-cost and low-overhead
No additional hardware or software required at branch offices
Reduced WAN usage with caching of frequently-used files
Clients access data at LAN speed
Which solution should you propose to meet the customer requirements?
A. HP StoreEasy 1000 Storage
B. HP StoreAll 9000 Storage
C. HP StoreEasy 5000 Storage
D. HP StoreSure 2000 Storage
Answer: A

NEW QUESTION: 3
You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?
A. run.tag('row_count', rows)
B. run.log_table('row_count', rows)
C. run.log('row_count', rows)
D. run.upload_file('row_count', './data.csv')
E. run.log_row('row_count', rows)
Answer: C
Explanation:
Log a numerical or string value to the run with the given name using log(name, value, description=''). Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Incorrect Answers:
E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.
Example: run.log_row("Y over X", x=1, y=0.4)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run