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DP-100 exam

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Here is the latest updated DP-100 exam dumps questions

QUESTION 1

You have a Python data frame named sales data in the following format:

q1

You need to use pandas.melt() function in Python to perform the transformation.
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.

Hot Area:

q1-2

Correct Answer:

q1-3

Box 1: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name=\\’value\\’,
col_level=None)[source]
Where frame is a DataFrame

Box 2: shop
Paramter id_vars id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.

Box 3: [\\’2017\\’,\\’2018\\’]
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, use all columns that are not set as id_vars.
Example:
df = pd.DataFrame({\\’A\\’: {0: \\’a\\’, 1: \\’b\\’, 2: \\’c\\’},
… \\’B\\’: {0: 1, 1: 3, 2: 5},
… \\’C\\’: {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=[\\’A\\’], value_vars=[\\’B\\’, \\’C\\’]) A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6

References: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

QUESTION 2

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 on the review screen.

You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

q2

Does the solution meet the goal?

A. Yes
B. No
Correct Answer: A

The two steps are present: process_step and train_step
Data_input correctly references the data in the data store.

Note:
Data used in the pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and input of one or more subsequent steps.

PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step
requires the output of another step as input, use a PipelineData object in the constructor of both steps.

For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData(“processed_data”, datastore=datastore) process_step =
PythonScriptStep(script_name=”process.py”, arguments=[“–data_for_train”, process_step_output],
outputs=[process_step_output],
compute_target=aml_compute,
source_directory=process_directory)
train_step = PythonScriptStep(script_name=”train.py”,
arguments=[“–data_for_train”, process_step_output],
inputs=[process_step_output],
compute_target=aml_compute,
source_directory=train_directory)
pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata? view=azure-mlpy

QUESTION 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.

You create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
1. /data/2018/Q1.csv
2. /data/2018/Q2.csv
3. /data/2018/Q3.csv
4. /data/2018/Q4.csv
5. /data/2019/Q1.csv

All files store data in the following format:
id,f1,f2,I 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1
You run the following code:

q3

Does the solution meet the goal?

A. Yes
B. No
Correct Answer: B

Define paths with two file paths instead.
Use Dataset.Tabular_from_delimeted as the data isn\\’t cleansed.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets

QUESTION 4

You are solving a classification task.
You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k
parameter as the number of splits.
You need to configure the k parameter for the cross-validation.
Which value should you use?

A. k=1
B. k=10
C. k=0.5
D. k=0.9
Correct Answer: B

Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one-out cross-validation (LOO), a special
case of the K-fold approach.

LOO CV is sometimes useful but typically doesn\’t shake up the data enough. The estimates from each fold are highly
correlated and hence their average can have high variance. This is why the usual choice is K=5 or 10. It provides a
good compromise for the bias-variance tradeoff.

QUESTION 5

You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?

A. Split Data
B. Load Trained Model
C. Assign Data to Clusters
D. Group Data into Bins
Correct Answer: D

The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are
set and how values are apportioned into the bins.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

QUESTION 6

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.

You train a classification model by using a logistic regression algorithm.
You must be able to explain the model\’s predictions by calculating the importance of each feature, both as an overall
global relative importance value and as a measure of local importance for a specific set of predictions.

You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer.
Does the solution meet the goal?

A. Yes
B. No
Correct Answer: B

Instead, use Permutation Feature Importance Explainer (PFI). Note 1:

q6

Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to
explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature
at a time for the entire dataset and calculating how much the performance metric of interest changes.

The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability

QUESTION 7

You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best-performing model based on a metric named accuracy.

You submit the experiment with the following code:

q7

You need to create Python code that returns the best model that is generated by the automated machine learning task.
Which code segment should you use?

A. best_model = automl_run.get_details()
B. best_model = automl_run.get_metrics()
C. best_model = automl_run.get_file_names()[1]
D. best_model = automl_run.get_output()[1]
Correct Answer: D

The get_output method returns the best run and the fitted model.

Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine learning/classification/auto-ml-classification.Reyna

QUESTION 8

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 minimum administrative effort.
Which module should you add to the pipeline in Designer?

A. Convert to CSV
B. Enter Data Manually
C. Import Data
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

QUESTION 9

You create a deep learning model for image recognition on Azure Machine Learning service using GPU- based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.

Which compute type should you use?

A. Azure Container Instance
B. Azure Kubernetes Service
C. Field Programmable Gate Array
D. Machine Learning Compute
Correct Answer: B

You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure
Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for
inference.

Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of
CPUs offer performance advantages on highly parallelizable computation.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus

QUESTION 10

Use the drop-down menus to select the answer choice that answers each question based on the information presented
in the image. NOTE: Each correct selection is worth one point.
Hot Area:

q10

Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is
better.

Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.

References:
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 11

DRAG-DROP
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM)
binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of
feature importance scores for the dataset.

In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area
and arrange them in the correct order.

Select and Place:

q11

Correct Answer:

q11-2

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test datasets.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification – Accuracy and then run the experiment.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance

QUESTION 12

You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a
real-time web service.

You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an
the error occurs when the service runs the entry script that is associated with the model deployment.

You need to debug the error by iteratively modifying the code and reloading the service, without requiring redeployment of the service for each code update.

What should you do?

A. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
B. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
C. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
D. Create a local web service deployment configuration and deploy the model to a local Docker container.
E. Register a new version of the model and update the entry script to load the new version of the model from its
registered path.
Correct Answer: B

How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure
Kubernetes Service (AKS) using Azure Machine Learning.

The recommended and the most up-to-date approach for model deployment is via the Model.deploy() API using an
Environment object as an input parameter. In this case, our service will create a base docker image for you during
the deployment stage and mount the required models all in one call.

The basic deployment tasks are:
1. Register the model in the workspace model registry.

2. Define Inference Configuration:
a. Create an Environment object based on the dependencies you specify in the environment YAML file or use one of our procured environments.
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.

3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).

QUESTION 13


You are performing sentiment analysis using a CSV file that includes 12.0O0 customer reviews written in a short
sentence format.

You add the CSV file to Azure Machine Learning Studio and Configure it as the starting point dataset of an experiment.
You add the Extract N-Gram Features from the Text module to the experiment to extract key phrases from the customer
review column in the dataset.

You must create a new n-gram text dictionary from the customer review text and set the maximum n-gram size to
trigrams.
You need to configure the Extract N-Gram Features from the Text module.

What should you select? To answer, select the appropriate options in the answer area;
NOTE: Each correct selection is worth one point.

Hot Area:

q13

Correct Answer:

q13-2
QUESTION 14

HOTSPOT
You are using Azure Machine Learning to train machine learning models. You need to compute the target on which to
remotely run the training script.

You run the following Python 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.

Hot Area:

q14

Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.

View compute targets
1. To see all compute targets for your workspace, use the following steps:
2. Navigate to Azure Machine Learning studio.
3. Under Manage, select Compute.
4. Select tabs at the top to show each type of computing target.

q14-2

Box 3: Yes
min_nodes is not specified, so it defaults to 0.

Reference:
https://docs.microsoft.com/en-us/python/api/azuremlcore/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

QUESTION 15

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.

You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Scale and Reduce sampling mode.

Does the solution meet the goal?

A. Yes
B. No
Correct Answer: B

Instead, use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underrepresented 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.

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

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