Perform Cloud Data Science with Azure Machine Learning v7.0 (70-774)

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Total 37 questions

You have the following three training datasets for a restaurant:
* User Feature
* Item feature
* Ratings of items by users
You must recommend restaurants to a particular user based only on the users features.
You need to use a Matchbox Recommender to make recommendations.
How many input parameters should you specify?

  • A. 1
  • B. 2
  • C. 3
  • D. 4


Answer : D

You are performing exploratory analysis of files that are encoded in a complex proprietary format. The format requires disk intensive access to several dependent files in HDFS.
You need to build an Azure Machine Learning model by using a canopy clustering algorithm. You must ensure that changes to proprietary file formats can be maintained by using the least amount of effort.
Which Machine Learning library should you use?

  • A. MicrosoftML
  • B. scikit-learn
  • C. SparkR
  • D. Mahout


Answer : C

Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
You are working on an Azure Machine Learning Experiment.
You have the dataset configured as shown in the following table:


You need to ensure that you can compare the performance of the models and add annotations to the results.
Solution: You connect the Score Model modules from each trained model as inputs for the
Evaluate Model module, and then save the result as a dataset.
Does this meet the goal?

  • A. YES
  • B. NO


Answer : A

Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
Start of repeated Scenario:
A Travel agency named Margies Travel sells airline tickets to customers in the United
States.
Margies Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD
HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.
End of repeated Scenario:
You need to remove the bias and to identify the columns in the input dataset that have the greatest predictive power.
Which module should you use for each requirement? To answer drag the appropriate modules to the correct requirements.




Answer :

Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following:
* Hidden Layers that support a directed graph structure.
* User-defined core components on the GPU
Solution: You create an endpoint to the computer Vision APL
Does this meet the goal?

  • A. YES
  • B. NO


Answer : B

Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.

Start of repeated scenario -
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment tor fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You need to alter the list of columns that will be used for predicting fraud for an input web service endpoint. The columns from the original data source must be retained while running the Machine Learning experiment.
Which module should you add after the web service input module and before the prediction module?

  • A. Edit Metadata
  • B. Import Data
  • C. SMOTE
  • D. Select Columns in Dataset


Answer : A

Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
You are designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following:
* Hidden Layers that support a directed graph structure.
* User-defined core components on the GPU
Solution: You create an Azure notebook that supports the Microsoft Cognitive Toolkit.
Does this meet the goal?

  • A. YES
  • B. NO


Answer : B

You are building an Azure Machine Learning Experiment.
You need to transform a string column that has 47 distinct values into a binary indicator column. The solution must use the One-vs-All Multiclass model.
Which module should you use?

  • A. Select Columns Transform
  • B. Convert to Indicator Values
  • C. Group Categorical Values
  • D. Edit Metadata


Answer : C

You plan to use the Data Science Virtual Machine for development, but you are unfamiliar with R scripts.
You need to generate R code for an experiment.
Which IDE should you use?

  • A. XgBoost
  • B. Rattle
  • C. Vowpal Wabbit
  • D. R Tools for Visual studio


Answer : D

You have an Azure Machine Learning experiment. You discover that a model causes many errors in a production dataset. The model causes only few errors in the Training data.
What is the cause of the errors?

  • A. overfitting
  • B. generalization
  • C. underfitting
  • D. a simple predictor


Answer : A

Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
You have a non-tabular file that is saved in Azure Blob Storage.
You need to download the file locally, access the data in the file, and then format the data as a dataset.
Which module should you use?

  • A. Execute Python Script
  • B. Tune Model Hyperparameters
  • C. Normalize Data
  • D. Select Columns in Dataset
  • E. Import Data
  • F. Edit Metadata
  • G. Clip Values
  • H. Clean Missing Data


Answer : E

Note: This question is part of a series of questions that present the same Scenario.
Each question I 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 correct solution.
Start of repeated Scenario:
A Travel agency named Margies Travel sells airline tickets to customers in the United
States.
Margies Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flights Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD
HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.
End of repeated Scenario:
You have an untrained Azure Machine Learning model that you plan to train to predict flight delays.
You need to assess the variability of the dataset and the reliability of the predictions from the model.
Which modules should you use?

  • A. Cross-validate Model.
  • B. Evaluate Model.
  • C. Tune Model Hyperparameters
  • D. Train Model
  • E. Score Model


Answer : D

You have an Execute R Script module that has one input from either a Partition and
Sample module or a Web Service input module.
You need to preprocess tweets by using R. The Solution must meet the following requirements:
* Remove digit
* Remove punctuation
* Convert to lowercase
How should you complete the R code? To answer drag the appropriate value to correct
Target.




Answer :

You are building an Azure Machine Learning Solution for an Online retailer.
When a customer selects a product, you need to recommend products that the customer might like to purchase at the same time. The recommendation should be based on what other customers purchased the same product.
Which model should you use?

  • A. Collaborative Filtering
  • B. Boosted Decision Tree Regression Model
  • C. Two-Class boosted decision tree
  • D. K-Means Clustering


Answer : A

You are building an Azure Machine Learning workflow by using Azure Machine Learning
Studio.
You create an Azure notebook that supports the Microsoft Cognitive Toolkit.
You need to ensure that the stochastic gradient descent (SGO) configuration maximizes the samples per second and supports parallel modeling that is managed by a parameter server.
Which SGD algorithm should you use?

  • A. DataParallelASGD
  • B. DataParallelSGD
  • C. ModelAveragingSGD
  • D. BlockMomentumSGD


Answer : B

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Total 37 questions