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

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

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

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

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

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

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

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

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

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