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NEW QUESTION: 1
You are the primary technical resource for an enterprise deployment of Microsoft Dynamics CRM 2016 on premises that has 300 seats.
You are asked about hardware requirements for Microsoft SQL Server.
You need to make the appropriate recommendation.
What are the hardware requirements for SQL Server for this organization?
A. Intel Xeon Phi Coprocessors, 64-GB RAM, and PCI Express Solid State Storage
B. Dual-core x64 architecture 1.5 GHz processor, 4 GB RAM. and SAS RAID 5 hard disk array
C. Dual-core x64 architecture 1.5 GHz processor, 4 GB RAM. and SAS RAID O hard disk array
D. Quad-core x64 architecture 2 GHz AMD Opteron processor. 16-GB RAM, and SAS RAID 5 hard disk array
Answer: C
NEW QUESTION: 2
You design an AI solution that uses an Azure Stream Analytics job to process data from an Azure IoT hub.
The IoT hub receives time series data from thousands of IoT devices at a factory.
The job outputs millions of messages per second. Different applications consume the messages as they are available. The messages must be purged.
You need to choose an output type for the job.
What is the best output type to achieve the goal? More than one answer choice may achieve the goal.
A. Azure Blob storage
B. Azure Event Hubs
C. Azure SQL Database
D. Azure Cosmos DB
Answer: D
Explanation:
Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low-latency queries on unstructured JSON data.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-documentdb-output
NEW QUESTION: 3
クラウドセンチメントローカルモデルの機能エンジニアリング戦略を実装する必要があります。
あなたは何をするべきか？
A. スピアマン相関係数を適用します。
B. ピアソン相関係数を適用します。
C. 分散分析（ANOVA）を適用します。
D. 線形判別分析を適用します。
Answer: D
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.
C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear-discriminant-analysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation