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NEW QUESTION: 1
お客様は、ミッションクリティカルなMicrosoft SQLデータベースを仮想化しており、最適なNUMAパフォーマンスを提供する構成が必要です。
*データベース仮想マシンを配置できるクラスターは2つあります。クラスターAはvSphere 6.0で、クラスターBはvSphere 6.5です。
*すべてのESXiホストには、デュアルIntel Xeon E5-2650 v3プロセッサー（2ソケット、ソケットあたり10コア）と、デフォルト構成のvNUMAを備えた256Gb RAMが含まれています。
このシナリオで、正しい3つのステートメントはどれですか。 （3つ選択してください。）
A. 10個のvCPU VMをクラスターBに配置し、2個のソケットとソケットあたり5個のコアで構成すると、2個のvNUMAノードが生成されます。
B. 仮想マシンでメモリのホットアドを有効にすると、vNUMAが無効になります。
C. 10個のvCPU VMをクラスターAに配置し、2個のソケットとソケットあたり5個のコアで構成すると、2個のvNUMAノードが生成されます。
D. VMをクラスターBに配置し、ソケットごとに5ソケットと2コアで構成すると、vNUMAノードが1つになります。
E. 仮想マシンでCPUホットアドを有効にすると、vNUMAが無効になります。
Answer: C,D,E
Explanation:
Enabling CPU Hot-Add disables vNUMA: https://kb.vmware.com/s/article/2040375 As for the vNUMA in ESXi 6.0 vs 6.5, there have been some major changes. Really good article explaining it here: https://blogs.virtualmaestro.in/2018/05/vnuma-vmware-vsphere-65.html. Essentially, in 6.0 you defined the vNUMA domain by specifying Cores and Sockets (As described in the example). In 6.5, it is abstracted (simply multiplies Sockets and Cores together) and vSphere automatically decides the optimal vNUMA setting. In the example above, the 10 vCPU's can fit on a single NUMA domain.
NEW QUESTION: 2
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - Vary the length of frequency bands between modeling epochs.
2 - Standardize to mono audio clips.
3 - Use an Inverse Fourier transform on frequency changes over time.
Topic 2, Case Study
Overview
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Datasets
There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:
The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
Dataset issues
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment Requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
NEW QUESTION: 3
Which deliverable is specific to on-premise and NOT for the cloud?
Please choose the correct answer.
Response:
A. Technical Design Solution
B. Knowledge Transfer
C. Adaptation Specification
D. Learning Needs Analysis
Answer: B
NEW QUESTION: 4
Given:
What is the result?
A. myStr: 7007, myNum: 9009
B. myStr: 7007, myNum: 7007
C. myStr: 9009, myNum: 9009
D. Compilation fails
Answer: A