Exam DP-100 topic 4 question 9 discussion

The answer should be C. For FileDataset input, this field is the number of files user script can process in one run() call.

Answer is C, https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallel_run_config.parallelrunconfig?view=azure-ml-py

mini_batch_size: Number of files passed per scoring script run. https://learn.microsoft.com/en-us/training/modules/deploy-model-batch-endpoint/3-deploy-your-mlflow-model-batch-endpoint

ParallelRunStep means run parallel in single one node. Question keyword 'six input files each time the inferencing function is called'. Answer keyword: mini_batch_size="6" .

The ParallelRunStep step runs must process six input files each time the inferencing function is called, so the correct configuration setting to specify in the ParallelRunConfig object for the ParallelRunStep step is the mini_batch_size, which should be set to 6. Therefore, the correct answer is: C. mini_batch_size= "6"

as stated in Microsoft Documentation this should be mini_batch_size: https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig?view=azure-ml-py. @ExamTopic Can we please fix the answer.

I believe the answer is C can we please fix the mistake? We are referring on the number of files here so why should be A?

Node count is about number of compute instances, servers etc.,

I am sure C is the answer, number of nodes = how many computer inst, number of processes = how many parallel running processes on a node, min_batch_size = min number of files processed

Correct answer is C. Question notes that the type of dateset to be used is a file dataset. Microsoft documentation says mini_batch_size: For FileDataset input, this field is the number of files a user script can process in one run() call https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallel_run_config.parallelrunconfig?view=azure-ml-py

Answer is C

on 19Oct2021

the answer should be mini_batch_size mini_batch_size Union[str, int] For FileDataset input, this field is the number of files a user script can process in one run() call. For TabularDataset input, this field is the approximate size of data the user script can process in one run() call. Example values are 1024, 1024KB, 10MB, and 1GB. (optional, default value is 10 files for FileDataset and 1MB for TabularDataset.)

On exam 2021/7/10

This Q&A model set has quite a few wrong answers... #Examtopics, kindly fix the wrong answers.

Answer is C: Refer below code: def run(mini_batch): # This runs for each batch resultList = [] # process each file in the batch for f in mini_batch: # Read comma-delimited data into an array data = np.genfromtxt(f, delimiter=',') # Reshape into a 2-dimensional array for model input prediction = model.predict(data.reshape(1, -1)) # Append prediction to results resultList.append("{}: {}".format(os.path.basename(f), prediction[0])) return resultList

mini_batch_size is correct