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Microsoft DP-100 Practice Test - Questions Answers, Page 10

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DRAG DROP

You are building an intelligent solution using machine learning models.

The environment must support the following requirements:

Data scientists must build notebooks in a cloud environment

Data scientists must use automatic feature engineering and model building in machine learning pipelines.

Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.

Notebooks must be exportable to be version controlled locally.

You need to create the environment.

Which four 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.


Question 91
Correct answer: Question 91

Explanation:

Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library

Step 2: Install Microsot Machine Learning for Apache Spark

You install AzureML on your Azure HDInsight cluster.

Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.

Step 3: Create and execute the Zeppelin notebooks on the cluster

Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.

Notebooks must be exportable to be version controlled locally.

References:

https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook

https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

HOTSPOT

You are performing sentiment analysis using a CSV file that includes 12,000 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 Text module to the experiment to extract key phrases from the customer review column in the dataset.

You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.

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

NOTE: Each correct selection is worth one point.


Question 92
Correct answer: Question 92

Explanation:

Vocabulary mode: Create

For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.

N-Grams size: 3 For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.

Weighting function: Leave blank The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features-from-text

DRAG DROP

You configure a Deep Learning Virtual Machine for Windows.

You need to recommend tools and frameworks to perform the following:

Build deep neural network (DNN) models

Perform interactive data exploration and visualization

Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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 93
Correct answer: Question 93

Explanation:

Box 1: Vowpal Wabbit

Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.

Box 2: PowerBI Desktop

Power BI Desktop is a powerful visual data exploration and interactive reporting tool BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8-model

https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration

DRAG DROP

You are creating an experiment by using Azure Machine Learning Studio.

You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.

You need to select appropriate methods for producing the experiment.

Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.


Question 94
Correct answer: Question 94

Explanation:

The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.

Incorrect Answers:

Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.

Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.

Missing Value Scrubber: The Missing Values Scrubber module is deprecated.

Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.

Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.

References:

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

HOTSPOT

You are retrieving data from a large datastore by using Azure Machine Learning Studio.

You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

You add the Partition and Sample module to your experiment.

You need to select the properties for the module.

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

NOTE: Each correct selection is worth one point.


Question 95
Correct answer: Question 95

Explanation:

Box 1: Sampling

Create a sample of data This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

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

2. Partition or sample mode: Set this to Sampling.

3. Rate of sampling. See box 2 below.

Box 2: 0

3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.

This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.

References:

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

HOTSPOT

The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.

You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.

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

NOTE: Each correct selection is worth one point.


Question 96
Correct answer: Question 96

Explanation:

Box 1: register_azure_blob_container

Register an Azure Blob Container to the datastore.

Box 2: create_if_not_exists = False

Create the file share if it does not exists, defaults to False.

Reference:

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

HOTSPOT

A coworker registers a datastore in a Machine Learning services workspace by using the following code:

You need to write code to access the datastore from a notebook.

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 97
Correct answer: Question 97

Explanation:

Box 1: DataStore

To get a specific datastore registered in the current workspace, use the get() static method on the Datastore class:

# Get a named datastore from the current workspace

datastore = Datastore.get(ws, datastore_name='your datastore name')

Box 2: ws

Box 3: demo_datastore

Reference:

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

DRAG DROP

An organization uses Azure Machine Learning service and wants to expand their use of machine learning.

You have the following compute environments. The organization does not want to create another compute environment.

You need to determine which compute environment to use for the following scenarios.

Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment 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 98
Correct answer: Question 98

Explanation:

Box 1: nb_server

Box 2: mlc_cluster

With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine

Learning Compute, Azure HDInsight or a remote virtual machine.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

HOTSPOT

You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.

ComputeOne is currently idle and has zero active nodes.

You define a Python variable named ws that references the Azure Machine Learning workspace. 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.


Question 99
Correct answer: Question 99

Explanation:

Box 1: Yes

ComputeTargetException class: An exception related to failures when creating, interacting with, or configuring a compute target. This exception is commonly raised for failures attaching a compute target, missing headers, and unsupported configuration values.

Create(workspace, name, provisioning_configuration)

Provision a Compute object by specifying a compute type and related configuration.

This method creates a new compute target rather than attaching an existing one.

Box 2: Yes

Box 3: No

The line before print('Step1') will fail.

Reference:

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

HOTSPOT

You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.

You must use CUDA-based model training.

You need to provision the Compute Instance.

Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area.

NOTE: Each correct selection is worth one point.


Question 100
Correct answer: Question 100

Explanation:

CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

Reference:

https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html

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