真正上考場時，時間壓力往往比題目本身更可怕。Fast2test 的 Desktop Test Engine 完整模擬 Microsoft Perform Cloud Data Science with Azure Machine Learning 的考試環境，搭配 65 道 070-774 練習題反覆實戰演練，正式考試時就能像平常練習一樣從容。

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Microsoft Perform Cloud Data Science with Azure Machine Learning 考生最常問的問題

最新的 MCSA-Machine Learning 070-774 免費考試真題：

問題 #1

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. Process the files by using Python to store the data to a Hadoop instance.
B. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
C. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
D. Create an Azure HDInsight Hadoop cluster.
E. Create the Machine learning experiment, and then add an Execute Python Script module.

問題 #2

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. underfitting
C. generalization
D. a simple predictor

問題 #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 section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to ARFF module, and then add the Execute R Script module.
Does this meet the goal?

A. No
B. Yes

問題 #4

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. Mahout
C. SparkR
D. scikit-learn

問題 #5

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. Remove the rows that have the missing values in Column3.
B. Replace the missing values in Column3 with a mean value.
C. Remove all of the rows that have the missing values in Column4 and Column5.
D. Replace the missing values in Column3 by using probabilistic Principal Component Analysis (PCA).

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