DP-100 Free Practice Test

- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named
aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed. You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy
metrics.
Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.

1. A. Yes
2. B. No

Correct Answer: A
Use the VM as a compute target.
Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

- (Exam Topic 3)
You are creating a new Azure Machine Learning pipeline using the designer.
The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.
You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.
Which module should you add to the pipeline in Designer?

1. A. Convert to CSV
2. B. Enter Data Manually D
3. C. Import Data
4. D. Dataset

Correct Answer: D
The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web URL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.
Example:
from azureml.core import Dataset
iris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')]) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline

- (Exam Topic 3)
You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.
The model will be retrained each month as new data is available. You must register the model for use in a batch inference pipeline.
You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

1. A. Specify a different name for the model each time you register it.
2. B. Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline.
3. C. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.
4. D. Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of thecurrently registered model.
5. E. Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currentlyregistered mode

Correct Answer: CE
E: Using tags, you can track useful information such as the name and version of the machine learning library
used to train the model. Note that tags must be alphanumeric.
Reference:
https://notebooks.azure.com/xavierheriat/projects/azureml-getting-started/html/how-to-use-azureml/deployment/

- (Exam Topic 3)
You are a data scientist building a deep convolutional neural network (CNN) for image classification. The CNN model you built shows signs of overfitting.
You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

1. A. Reduce the amount of training data.
2. B. Add an additional dense layer with 64 input units
3. C. Add L1/L2 regularization.
4. D. Use training data augmentation
5. E. Add an additional dense layer with 512 input units.

Correct Answer: AC
References:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/ https://en.wikipedia.org/wiki/Convolutional_neural_network

- (Exam Topic 3)
You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images. You must meet the following requirements:
• Reduce the number of training epochs.
• Reduce the size of the neural network.
• Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area. NOTE: Each correct selection is worth one point.

Solution:


Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A