Want to know 70-774 Dumps Questions features? Want to lear more about 70-774 Exam Questions experience? Study 70-774 Exam Questions. Gat a success with an absolute guarantee to pass Microsoft 70-774 (Perform Cloud Data Science with Azure Machine Learning (beta)) test on your first attempt.
Online Microsoft 70-774 free dumps demo Below:
NEW QUESTION 1
You plan to use Azure Machine Learning to develop a predictive model. You plan to include an Execute Python Script module.
What capability does the module provide?
- A. Outputting a file to a network location.
- B. Performing interactive debugging of a Python script.
- C. Saving the results of a Python script run in a Machine Learning environment to a local file.
- D. Visualizing univariate and multivariate summaries by using Python code.
Answer: D
Explanation: References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/execute-python-scripts
NEW QUESTION 2
You deploy Microsoft Data Management Gateway.
You plan to use the Import Data module in Azure Machine Learning Studio to import data from an on-premises Microsoft SQL Server instance.
Which operation can you perform?
- A. Write the data back to the on-premises SQL Server instance.
- B. Filter the data as the data is being read by using the Import Data module.
- C. Run a Transact-SQL query and use SQL views to filter the data as the data is being read.
- D. Access the on-premises SQL Server instance without using credentials, and then import the data.
Answer: D
NEW QUESTION 3
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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 results as a dataset.
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
NEW QUESTION 4
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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:
Solution: You create a Machine Learning experiment that implements the Multiclass Neural Network module. Does this meet the goal?
- A. Yes
- B. No
Answer: A
NEW QUESTION 5
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You need to transform the columns in a dataset. The resulting data must be mean centered and have a variance of L The solution must use a native module.
Which module should you use?
- A. Execute Python Script
- B. Import Data
- C. Edit Metadata
- D. Select Columns in Dataset
- E. Clean Missing Data
- F. Tune Model Hyperparameters
- G. Clip Values
- H. Normalize Data
Answer: H
NEW QUESTION 6
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.
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 for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
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 part 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: BDE
NEW QUESTION 7
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 use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
The image files to train the object recognition model are stored in a Microsoft SQL Server 2021 Standard edition database on an Azure virtual machine (VM).
You need to support R packages that can use full parallel threading and processing for RevoScaleR.
How should you implement R? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation: 
NEW QUESTION 8
You have an Apache Spark cluster in Azure HDinsight. The cluster includes 200 TB in five Apache Hive tables that have multiple foreign key relationships.
You have an Azure Machine Learning model that was built by using SPARK Accelerated Failure Time (AFT) Survival Regression Model (spark-survreg).
You need to prepare the Hive data into a single table as input for the Machine Learning model. The Hive data must be prepared in the least amount of time possible.
What should you use to prepare the data?
- A. a Hive user-defined function (UDF)
- B. Spark SQL
- C. the GPU
- D. Java Mapreduce jobs
Answer: A
NEW QUESTION 9
You are working on an Azure Machine Learning experiment that uses four different logistic regression algorithms. You are evaluating the algorithms based on the data in the following table.
Which model produces predictions that are the closest to the actual outcomes?
- A. Model 1
- B. Model 2
- C. Model 3
- D. Model 4
Answer: A
NEW QUESTION 10
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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:
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
NEW QUESTION 11
You have a dataset that is missing values in a column named Column3. Column3 is correlated to two columns named Column4 and Column5.
You need to improve the accuracy of the dataset, while minimizing data loss. What should you do?
- A. Replace the missing values in Column3 by using probabilistic Principal Component Analysis (PCA).
- B. Remove all of the rows that have the missing values in Column4 and Column5.
- C. Replace the missing values in Column3 with a mean value.
- D. Remove the rows that have the missing values in Column3.
Answer: A
NEW QUESTION 12
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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 consolidate the output of the Score Model modules by using the Add Rows module, and then
use the Execute R Script module.
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
NEW QUESTION 13
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 use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to preprocess the training data by using a Principal Component Analysis (PCA) algorithm in the least amount of time possible. Which implementation method should you use?
- A. Azure HDInsight using HiveML
- B. Azure Machine Learning Studio and a custom Execute Python Script module
- C. Azure HDInsight using Microsoft R Server
- D. Azure Machine Learning Studio with a custom Execute R Script module
Answer: C
NEW QUESTION 14
You have data about the following:
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: C
Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905970.aspx#RatingPredictionOptions
NEW QUESTION 15
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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 module to select the MAE.
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation: https://msdn.microsoft.com/en-us/library/azure/dn905915.aspx
NEW QUESTION 16
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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:
Solution: You create an Azure notebook that supports the Microsoft Cognitive Toolkit. Does this meet the goal?
- A. Yes
- B. No
Answer: B
NEW QUESTION 17
You are analyzing taxi trips in New York City. You leverage the Azure Data Factory to create data pipelines and to orchestrate data movement.
You plan to develop a predictive model for 170 million rows (37 GB) of raw data in Apache Hive by using Microsoft R Server to identify which factors contribute to the passenger tipping behavior.
All of the platforms that are used for the analysis are the same. Each worker node has eight processor cores and 26 GB of memory.
Which type of Azure HDInsight cluster should you use to produce results as quickly as possible?
- A. Hadoop
- B. HBase
- C. Interactive Hive
- D. Spark
Answer: D
Explanation: References:
https://azure.microsoft.com/en-gb/blog/general-availability-of-hdinsight-interactive-query-blazing-fast-data-war
NEW QUESTION 18
You have the following three training datasets for a restaurant:
You must recommend restaurant 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: B
Explanation: References:
https://msdn.microsoft.com/en-us/library/azure/dn905987.aspx
P.S. Easily pass 70-774 Exam with 64 Q&As 2passeasy Dumps & pdf Version, Welcome to Download the Newest 2passeasy 70-774 Dumps: https://www.2passeasy.com/dumps/70-774/ (64 New Questions)
