70-774 Exam
Dec 2021 updated: Pass4sure Microsoft 70-774 exam topics 1-10
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Q1. 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 Margie’s Travel sells airline tickets to customers in the United States.
Margie’s 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 flight’s 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 plan to predict flight delays that are 30 minutes or more.
You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage. Which attribute should you remove?
A. OriginAirportID
B. DepDel
C. DepDel30
D. Carrier
E. DestAirportID
Answer: B
Q2. 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
Q3. 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 save the output of the Score Model modules as a combined set, and then use the Project Columns modules to select the MAE.
Does this meet the goal?
A. YES
B. NO
Answer: A
Q4. 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 a Machine Learning Experiment that implements the Multiclass Decision Jungle Module.
Does this meet the goal?
A. YES
B. NO
Answer: B
Q5. You are building an Azure Machine Learning experiment.
You need to transform a string column into a label column for a Multiclass Decision Jungle module.
Which module should you use?
A. Select Columns Transform
B. Group Categorical Values
C. Convert to Indicator Values
D. Edit Metadata
Answer: C
Q6. 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 plan to configure the resources for part of a workflow that will be used to preprocess data from files stored in Azure Blob storage. You plan to use Python to preprocess and store the data in Hadoop.
You need to get the data into Hadoop as quickly as possible.
Which three actions should you perform? Each correct answer presents pan of the solution.
NOTE: Each correct selection is worth one point.
A. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
B. Create an Azure HDInsight Hadoop cluster.
C. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
D. Process the files by using Python to store the data to a Hadoop instance.
E. Create the Machine Learning experiment, and then add an Execute Python Script module.
Answer: A,C,E
Q7. 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
Q8. You are building an Azure Machine Learning experiment.
You need to transform a string column into a label column for a Multiclass Decision Jungle module.
Which module should you use?
A. Select Columns Transform
B. Group Categorical Values
C. Convert to Indicator Values
D. Edit Metadata
Answer: C
Q9. You have data about the following:
* Users
* Movies
* User ratings of the movies
You need to predict whether a user will like a particular movie. Which Matchbox recommender should you use?
A. Item Recommendation
B. Related items
C. Rating Prediction
D. Related Users.
Answer: B
Q10. DRAG DROP
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 Margie’s Travel sells airline tickets to customers in the United States.
Margie’s 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 flight’s 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: