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Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Certification Exam is a popular certification exam designed to test the skills and knowledge of data professionals in designing and implementing data science solutions on the Microsoft Azure platform. DP-100 exam is ideal for data scientists, data analysts, and other professionals who want to demonstrate their expertise in working with data on the Azure platform.

The DP-100 Exam is an excellent opportunity for data scientists and engineers to validate their skills and gain a competitive advantage in the job market. Designing and Implementing a Data Science Solution on Azure certification provides an added advantage for organizations to identify skilled data scientists and engineers who can design and implement data science solutions on Azure.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q18-Q23):

NEW QUESTION # 18
You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.
The experiment fails.
You need to troubleshoot the failed experiment.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

A. Use the get_details_with_logs() method of the run object to display the experiment run logs.
B. View the logs for the experiment run in Azure Machine Learning studio.
C. Use the get_metrics() method of the run object to retrieve the experiment run logs.
D. Use the get_output() method of the run object to retrieve the experiment run logs.
E. View the log files for the experiment run in the experiment folder.

Answer: A,B

Explanation:
Use get_details_with_logs() to fetch the run details and logs created by the run.
You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
*Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
*Access a user's tendency to respond to an advertisement.
*Customize styles of ads served on mobile devices.
*Use video to detect penalty events.
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 # 19
You train a machine learning model by using Aunt Machine Learning.
You use the following training script m Python to log an accuracy value.

You must use a Python script to define a sweep job.
You need to provide the primary metric and goal you want hyper parameter tuning to optimize.
How should you complete the Python script? To answer select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 20
You use a training pipeline in the Azure Machine Learning designer. You register a datastore named ds1. The datastore contains multiple training data files. You use the Import Data module with the configured datastore.
You need to retrain a model on a different set of data files.
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.

Answer:

Explanation:

Explanation

NEW QUESTION # 21
Hotspot Question
You manage an Azure Machine Learning workspace. The development environment is configured with a Serverless Spark compute in Azure Machine Learning Notebooks.
You perform interactive data wrangling to clean up the Titanic dataset and store it as a new dataset. (Line numbers are used for reference only.)

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 22
You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.
You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training The script will accept a parameter designating the dataset You need to complete the script.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 23
......

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Hugo Lee Hugo Lee

Biography

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Hugo Lee Hugo Lee

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