Designing and Implementing a Data Science Solution on Azure (beta) v1.0
Question 1
You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.
Which of the following is an algorithm that reduces the variances between actual and predicted values?
- A. Fast Forest Quantile Regression
- B. Poisson Regression
- C. Boosted Decision Tree Regression
- D. Linear Regression
Answer : C
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/boosted-decision-tree-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
Question 2
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Convolutional Neural Networks (CNNs).
Will the requirements be satisfied?
- A. Yes
- B. No
Answer : B
Question 3
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Generative Adversarial Networks (GANs).
Will the requirements be satisfied?
- A. Yes
- B. No
Answer : B
Question 4
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Recurrent Neural Networks (RNNs).
Will the requirements be satisfied?
- A. Yes
- B. No
Answer : A
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. Theyג€™re called recurrent because the networkג€™s hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory.
It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
Reference:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571
Question 5
DRAG DROP -
You have been tasked with evaluating the performance of a binary classification model that you created.
You need to choose evaluation metrics to achieve your goal.
Which of the following are the metrics you would choose? Answer by dragging the correct options from the list to the answer area.
Select and Place:
Answer : 
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: ג€˜Out of the individuals whom the model, how many were classified correctly (TP)?ג€™
This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
Question 6
DRAG DROP -
You build a binary classification model using the Azure Machine Learning Studio Two-Class Neural Network module.
You are preparing to configure the Tune Model Hyperparameters module for the purpose of tuning accuracy for the model.
Which of the following are valid parameters for the Two-Class Neural Network module? Answer by dragging the correct options from the list to the answer area.
Select and Place:
Answer : 
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network
Question 7
You make use of Azure Machine Learning Studio to create a binary classification model.
You are preparing to carry out a parameter sweep of the model to tune hyperparameters. You have to make sure that the sweep allows for every possible combination of hyperparameters to be iterated. Also, the computing resources needed to carry out the sweep must be reduced.
Which of the following actions should you take?
- A. You should consider making use of the Selective grid sweep mode.
- B. You should consider making use of the Measured grid sweep mode.
- C. You should consider making use of the Entire grid sweep mode.
- D. You should consider making use of the Random grid sweep mode.
Answer : D
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters
Question 8
You are in the process of constructing a deep convolutional neural network (CNN). The CNN will be used for image classification.
You notice that the CNN model you constructed displays hints of overfitting.
You want to make sure that overfitting is minimized, and that the model is converged to an optimal fit.
Which of the following is TRUE with regards to achieving your goal?
- A. You have to add an additional dense layer with 512 input units, and reduce the amount of training data.
- B. You have to add L1/L2 regularization, and reduce the amount of training data.
- C. You have to reduce the amount of training data and make use of training data augmentation.
- D. You have to add L1/L2 regularization, and make use of training data augmentation.
- E. You have to add an additional dense layer with 512 input units, and add L1/L2 regularization.
Answer : B
B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.
Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.
Three different regularizer instances are provided; they are:
✑ L1: Sum of the absolute weights.
✑ L2: Sum of the squared weights.
✑ L1L2: Sum of the absolute and the squared weights.
Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.
By avoiding training all nodes on all training data, dropout decreases overfitting.
Reference:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/ https://en.wikipedia.org/wiki/Convolutional_neural_network
Question 9
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Attached compute.
Will the requirements be satisfied?
- A. Yes
- B. No
Answer : B
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
Question 10
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Inference cluster.
Will the requirements be satisfied?
- A. Yes
- B. No
Answer : B
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
Question 11
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Compute cluster.
Will the requirements be satisfied?
- A. Yes
- B. No
Answer : A
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
Question 12
You are making use of the Azure Machine Learning to designer construct an experiment.
After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree.
You are preparing to ascertain the Area Under the Curve (AUC).
Which of the following is a sequential combination of the models required to achieve your goal?
- A. Train, Score, Evaluate.
- B. Score, Evaluate, Train.
- C. Evaluate, Export Data, Train.
- D. Train, Score, Export Data.
Answer : A
Question 13
DRAG DROP
-
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the computer resources.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer : 
Question 14
You are developing a hands-on workshop to introduce Docker for Windows to attendees.
You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Microsoft Hardware-Assisted Virtualization Detection Tool
- B. Kitematic
- C. BIOS-enabled virtualization
- D. VirtualBox
- E. Windows 10 64-bit Professional
Answer : CE
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:
E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
Reference:
https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/
Question 15
Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
✑ support Python and Scala
✑ compose data storage, movement, and processing services into automated data pipelines
✑ the same tool should be used for the orchestration of both data engineering and data science
✑ support workload isolation and interactive workloads
✑ enable scaling across a cluster of machines
You need to create the environment.
What should you do?
- A. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
- B. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
- C. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
- D. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Answer : B
In Azure Databricks, we can create two different types of clusters.
✑ Standard, these are the default clusters and can be used with Python, R, Scala and SQL
✑ High-concurrency
Azure Databricks is fully integrated with Azure Data Factory.
Incorrect Answers:
D: Azure Container Instances is good for development or testing. Not suitable for production workloads.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning