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[General] Examcollection DP-100 Questions Answers | DP-100 Free Sample

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【General】 Examcollection DP-100 Questions Answers | DP-100 Free Sample

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Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:
Sample QuestionsMicrosoft Designing and Implementing a Data Science Solution on Azure Sample Questions
Number of Questions40-60
Duration120 mins
Exam Price$165 (USD)
Schedule ExamPearson VUE
Books / TrainingDP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Exam NameMicrosoft Certified - Azure Data Scientist Associate
Passing Score700 / 1000

Microsoft DP-100 Exam Syllabus Topics:
TopicDetails
Manage Azure resources for machine learning (25-30%)
Create an Azure Machine Learning workspace- create an Azure Machine Learning workspace
- configure workspace settings
- manage a workspace by using Azure Machine Learning studio
Manage data in an Azure Machine Learning workspace- select Azure storage resources
- register and maintain datastores
- create and manage datasets
Manage compute for experiments in Azure Machine Learning- determine the appropriate compute specifications for a training workload
- create compute targets for experiments and training
- configure Attached Compute resources including Azure Databricks
- monitor compute utilization
Implement security and access control in Azure Machine Learning- determine access requirements and map requirements to built-in roles
- create custom roles
- manage role membership
- manage credentials by using Azure Key Vault
Set up an Azure Machine Learning development environment- create compute instances
- share compute instances
- access Azure Machine Learning workspaces from other development environments
Set up an Azure Databricks workspace- create an Azure Databricks workspace
- create an Azure Databricks cluster
- create and run notebooks in Azure Databricks
- link and Azure Databricks workspace to an Azure Machine Learning workspace
Run Experiments and Train Models (20-25%)
Create models by using the Azure Machine Learning Designer- create a training pipeline by using Azure Machine Learning designer
- ingest data in a designer pipeline
- use designer modules to define a pipeline data flow
- use custom code modules in designer
Run model training scripts- create and run an experiment by using the Azure Machine Learning SDK
- configure run settings for a script
- consume data from a dataset in an experiment by using the Azure Machine Learning SDK
- run a training script on Azure Databricks compute
- run code to train a model in an Azure Databricks notebook
Generate metrics from an experiment run- log metrics from an experiment run
- retrieve and view experiment outputs
- use logs to troubleshoot experiment run errors
- use MLflow to track experiments
- track experiments running in Azure Databricks
Use Automated Machine Learning to create optimal models- use the Automated ML interface in Azure Machine Learning studio
- use Automated ML from the Azure Machine Learning SDK
- select pre-processing options
- select the algorithms to be searched
- define a primary metric
- get data for an Automated ML run
- retrieve the best model
Tune hyperparameters with Azure Machine Learning- select a sampling method
- define the search space
- define the primary metric
- define early termination options
- find the model that has optimal hyperparameter values
Deploy and operationalize machine learning solutions (35-40%)
Select compute for model deployment- consider security for deployed services
- evaluate compute options for deployment
Deploy a model as a service- configure deployment settings
- deploy a registered model
- deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
- consume a deployed service
- troubleshoot deployment container issues
Manage models in Azure Machine Learning- register a trained model
- monitor model usage
- monitor data drift
Create an Azure Machine Learning pipeline for batch inferencing- configure a ParallelRunStep
- configure compute for a batch inferencing pipeline
- publish a batch inferencing pipeline
- run a batch inferencing pipeline and obtain outputs
- obtain outputs from a ParallelRunStep

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q410-Q415):NEW QUESTION # 410
You need to build a feature extraction strategy for the local models.
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.

Answer:
Explanation:


NEW QUESTION # 411
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:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?
  • A. No
  • B. Yes
Answer: B
Explanation:
Explanation
Use two file paths.
Use Dataset.Tabular_from_delimeted as the data isn't cleansed.
Note:
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.
Reference:
https://docs.microsoft.com/en-us ... e-register-datasets

NEW QUESTION # 412
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
  • A. Latent Dirichlet Allocation
  • B. Execute Python Script
  • C. Preprocess Text
  • D. Edit Metadata
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Typical metadata changes might include marking columns as features.
References:
https://docs.microsoft.com/en-us ... rence/edit-metadata Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.

Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

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:



NEW QUESTION # 413
You manage an Azure Machine Learning workspace named workspace 1 with a compute instance named computet.
You must remove a kernel named kernel 1 from computet1. You connect to compute 1 by using noa terminal window from workspace 1.
You need to enter a command in the terminal window to remove kernel 1.
Which command should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection it worth one point.

Answer:
Explanation:

Explanation:


NEW QUESTION # 414
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or 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.

Answer:
Explanation:

Reference:
https://docs.microsoft.com/en-us ... artition-and-sample

NEW QUESTION # 415
......
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