- (Exam Topic 3)
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU’s.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Solution:
Step 1: Configure a DataTransferStep() to fetch new image data…
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target. Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target. Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 1)
You need to select an environment that will meet the business and data requirements. Which environment should you use?

1. A. Azure HDInsight with Spark MLlib
2. B. Azure Cognitive Services
3. C. Azure Machine Learning Studio
4. D. Microsoft Machine Learning Server

Correct Answer: D

- (Exam Topic 3)
You are conducting feature engineering to prepuce data for further analysis. The data includes seasonal patterns on inventory requirements.
You need to select the appropriate method to conduct feature engineering on the data. Which method should you use?

1. A. Exponential Smoothing (ETS) function.
2. B. One Class Support Vector Machine module
3. C. Time Series Anomaly Detection module
4. D. Finite Impulse Response (FIR) Filter module.

Correct Answer: D

- (Exam Topic 3)
You create machine learning models by using Azure Machine Learning.
You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.
You need to select the appropriate compute types.
Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Solution:
Box 1: Attached compute

Box 2: Inference cluster Box 3: Training cluster Box 4: Attached compute

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You create a binary classification model. You need to evaluate the model performance.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

1. A. relative absolute error
2. B. precision
3. C. accuracy
4. D. mean absolute error
5. E. coefficient of determination

Correct Answer: BC
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: ‘Out of the individuals whom the model, how many were classified correctly (TP)?’
This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance