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HOTSPOT You create an Azure Machine Learning dataset containing automobile price data The dataset includes 10,000 rows and 10 columns You use Azure Machine Learning Designer to transform the dataset by using an Execute Python Script component and custom code. The code must combine three columns to create a new column. You need to configure the code function. Which configurations should you use? lo answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
DRAG DROP You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription. You must manage the Synapse Spark pool from the Azure Machine Learning workspace. You need to attach the Synapse Spark pool in Azure Machine Learning by usinq the Python SDK v2. 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.
You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed. Which statistic should you use?
You register a model that you plan to use in a batch inference pipeline. The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called. You need to configure the pipeline. Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
HOTSPOT You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements: * Provide near real-time inferencing. * Enable endpoint and deployment level cost estimates. * Support logging to Azure Log Analytics. You need to configure the deployment solution. What should you configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
You train and publish a machine teaming model. You need to run a pipeline that retrains the model based on a trigger from an external system. What should you configure?
You create a multi-class image classification deep learning model. You train the model by using PyTorch version 1.2. You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed. What should you do?
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?
DRAG DROP You are analyzing a raw dataset that requires cleaning. You must perform transformations and manipulations by using Azure Machine Learning Studio. You need to identify the correct modules to perform the transformations. Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module 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.
You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size. You have the following requirements: Models must be built using Caffe2 or Chainer frameworks. Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments. Personal devices must support updating machine learning pipelines when connected to a network. You need to select a data science environment. Which environment should you use?

Question 101

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DRAG DROP

You are analyzing a raw dataset that requires cleaning.

You must perform transformations and manipulations by using Azure Machine Learning Studio.

You need to identify the correct modules to perform the transformations.

Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module 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.


Question 101
Correct answer: Question 101

Explanation:

Box 1: Clean Missing Data

Box 2: SMOTE

Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

Box 3: Convert to Indicator Values

Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.

Box 4: Remove Duplicate Rows

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values

Question 102

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You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.


Question 102
Correct answer: Question 102

Explanation:

Box 1: No

If a training cluster already exists it will be used.

Box 2: Yes

The wait_for_completion method waits for the current provisioning operation to finish on the cluster.

Box 3: Yes

Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.

Box 4: No

Need to use training_compute.delete() to deprovision and delete the AmlCompute target.

Reference:

https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

Question 103

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You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column. The text column represents a product's category. The product category will always be one of the following:

Bikes

Cars

Vans

Boats

You are building a regression model using the scikit-learn Python package.

You need to transform the text data to be compatible with the scikit-learn Python package.

How should you complete the code segment? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 103
Correct answer: Question 103

Explanation:

Box 1: pandas as df

Pandas takes data (like a CSV or TSV file, or a SQL database) and creates a Python object with rows and columns called data frame that looks very similar to table in a statistical software (think Excel or SPSS for example.

Box 2: transpose[ProductCategoryMapping]

Reshape the data from the pandas Series to columns.

Reference:

https://datascienceplus.com/linear-regression-in-python/

Question 104

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You are evaluating a Python NumPy array that contains six data points defined as follows:

data = [10, 20, 30, 40, 50, 60]

You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:

train: [10 40 50 60], test: [20 30]

train: [20 30 40 60], test: [10 50]

train: [10 20 30 50], test: [40 60]

You need to implement a cross-validation to generate the output.

How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.


Question 104
Correct answer: Question 104

Explanation:

Box 1: k-fold

Box 2: 3

K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.

Box 3: data

Example: Example:

>>>

>>> from sklearn.model_selection import KFold

>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])

>>> y = np.array([1, 2, 3, 4])

>>> kf = KFold(n_splits=2)

>>> kf.get_n_splits(X)

2

>>> print(kf)

KFold(n_splits=2, random_state=None, shuffle=False)

>>> for train_index, test_index in kf.split(X):

... print("TRAIN:", train_index, "TEST:", test_index)

... X_train, X_test = X[train_index], X[test_index]

... y_train, y_test = y[train_index], y[test_index]

TRAIN: [2 3] TEST: [0 1]

TRAIN: [0 1] TEST: [2 3]

References:

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

Question 105

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You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.

You must submit an experiment that runs this script in the Azure Machine Learning workspace.

The following compute resources are available:

a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.

Which resources should the data scientist use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 105
Correct answer: Question 105

Explanation:

Box 1: the ds-workstation notebook VM

Box 2: the gpu-compute target

Just as GPUs revolutionized deep learning through unprecedented training and inferencing performance, RAPIDS enables traditional machine learning practitioners to unlock game-changing performance with GPUs. With RAPIDS on Azure Machine Learning service, users can accelerate the entire machine learning pipeline, including data processing, training and inferencing, with GPUs from the NC_v3, NC_v2, ND or ND_v2 families. Users can unlock performance gains of more than 20X (with 4 GPUs), slashing training times from hours to minutes and dramatically reducing time-to-insight.

Reference:

https://azure.microsoft.com/sv-se/blog/azure-machine-learning-service-now-supports-nvidia-s-rapids/

Question 106

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You are performing a classification task in Azure Machine Learning Studio.

You must prepare balanced testing and training samples based on a provided data set.

You need to split the data with a 0.75:0.25 ratio.

Which value should you use for each parameter? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 106
Correct answer: Question 106

Explanation:

Box 1: Split rows

Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.

You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.

Box 2: 0.75

If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.

Box 3: Yes

To ensure splits are balanced.

Box 4: No

If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

Question 107

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You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.

You need to configure the module. You must meet the following requirements:

Divide the data into subsets

Assign the rows into folds using a round-robin method

Allow rows in the dataset to be reused

How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.


Question 107
Correct answer: Question 107

Explanation:

Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

1. Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.

2. For Partition or sample mode, select Assign to Folds.

3. Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.

4. If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.

5. Randomized split: Select this option if you want rows to be randomly assigned to folds.

If you do not select this option, rows are assigned to folds using the round-robin method.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

Question 108

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You create an Azure Machine Learning workspace and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.

You must optimize computation speed for training runs.

You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.

Which values should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 108
Correct answer: Question 108

Explanation:

Box 1: Tensorflow

TensorFlow represents an estimator for training in TensorFlow experiments.

Box 2: 12 vCPU, 112 GB memory..,2 GPU,..

Use GPUs for the deep neural network.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn

Question 109

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You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.

You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.

You need to configure store1.

What should you do? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 109
Correct answer: Question 109

Explanation:

Box 1: Regenerate the keys of account1.

Azure Blob Storage support authentication through Account key or SAS token.

To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal

Box 2: Update the authentication for store1.

For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks, use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.

Note: Some of the studio's features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.

The following operations are disabled by default in a virtual network:

Preview data in the studio.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

Question 110

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You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the regression model.

Which two metrics can you use? Each correct answer presents a complete solution?

NOTE: Each correct selection is worth one point.

A.
a Root Mean Square Error value that is low
A.
a Root Mean Square Error value that is low
Answers
B.
an R-Squared value close to 0
B.
an R-Squared value close to 0
Answers
C.
an F1 score that is low
C.
an F1 score that is low
Answers
D.
an R-Squared value close to 1
D.
an R-Squared value close to 1
Answers
E.
an F1 score that is high
E.
an F1 score that is high
Answers
F.
a Root Mean Square Error value that is high
F.
a Root Mean Square Error value that is high
Answers
Suggested answer: A, D

Explanation:

RMSE and R2 are both metrics for regression models.

A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

Incorrect Answers:

C, E: F-score is used for classification models, not for regression models.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

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