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Microsoft DP-100 Certification Exam is a highly regarded certification in the field of data science. DP-100 exam is designed to test the candidate's expertise in designing and implementing data science solutions on Microsoft Azure. Candidates who pass the exam will be able to demonstrate their ability to design and implement data science solutions on Microsoft Azure, which can open up new career opportunities in the field of data science.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q383-Q388):

NEW QUESTION # 383
You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.
You create the following config.json file.

You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. region
B. resource_group
C. login
D. key
E. subscription_id

Answer: B,E

Explanation:
To use the same workspace in multiple environments, create a JSON configuration file. The configuration file saves your subscription (subscription_id), resource (resource_group), and workspace name so that it can be easily loaded.
The following sample shows how to create a workspace.
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2'
)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace

NEW QUESTION # 384
You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
- Minimum nodes: 2
- Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
- Minimum nodes: 0
- Maximum nodes: 8
You need to reconfigure the compute resource.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Use the Azure Machine Learning designer.
B. Run the refresh_state() method of the BatchCompute class in the Python SDK.
C. Run the update method of the AmlCompute class in the Python SDK.
D. Use the Azure portal.
E. Use the Azure Machine Learning studio.

Answer: C,D,E

Explanation:
A: You can manage assets and resources in the Azure Machine Learning studio.
B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this AmlCompute target.
C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-
core/azureml.core.compute.amlcompute(class)

NEW QUESTION # 385
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?

A. Streaming
B. Cosine
C. Weight
D. Batch

Answer: D

Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
Reference:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Topic 1, Overview
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
* Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%.
* The ad propensity model uses cost factors shown in the following diagram:

The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

Penalty detection and sentiment
Findings
* Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
* Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
* Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
* Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
* Global penalty detection models must be trained by using dynamic runtime graph computation during training.
* Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
* Ad response rates declined.
* Drops were not consistent across ad styles.
* The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
* Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
* All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
* Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
* The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

NEW QUESTION # 386
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

NEW QUESTION # 387
You need to select an environment that will meet the business and data requirements.
Which environment should you use?

A. Microsoft Machine Learning Server
B. Azure Machine Learning Studio
C. Azure HDInsight with Spark MLlib
D. Azure Cognitive Services

Answer: A

NEW QUESTION # 388
......

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