- (Exam Topic 3)
You plan to create a speech recognition deep learning model. The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?

1. A. Apache Drill
2. B. Tensorflow
3. C. Rattle
4. D. Weka

Correct Answer: B
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
References:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html

- (Exam Topic 3)
You retrain an existing model.
You need to register the new version of a model while keeping the current version of the model in the registry.
What should you do?

1. A. Register a model with a different name from the existing model and a custom property named versionwith the value 2.
2. B. Register the model with the same name as the existing model.
3. C. Save the new model in the default datastore with the same name as the existing mode
4. D. Do not register the new model.
5. E. Delete the existing model and register the new one with the same name.

Correct Answer: B
Model version: A version of a registered model. When a new model is added to the Model Registry, it is added as Version 1. Each model registered to the same model name increments the version number.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/model-registry

- (Exam Topic 3)
You create a binary classification model to predict whether a person has a disease. You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solution:
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative
A true negative is an outcome where the model correctly predicts the negative class. Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class. Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class. Note: Let's make the following definitions:
"Wolf" is a positive class. "No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.
What should the entry script do?

1. A. Start a node on the inference cluster where the web service is deployed.
2. B. Register the model with appropriate tags and properties.
3. C. Create a Conda environment for the web service compute and install the necessary Python packages.
4. D. Load the model and use it to predict labels from input data.
5. E. Specify the number of cores and the amount of memory required for the inference compute.

Correct Answer: D
The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.
The two things you need to accomplish in your entry script are: Loading your model (using a function called init())
Running your model on input data (using a function called run()) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

- (Exam Topic 3)
You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:

You need to classify variables by type.
Which variable should you add to each category? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Solution:
References: https://www.edureka.co/blog/classification-algorithms/

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A