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NEW QUESTION: 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 have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training data. You plan to automate the process of adding more data to the training models and to constantly tune the models by using the additional data, including data that is collected in near real-time. The system will be used to analyze event data gathered from many different sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from
Facebook, which are event-based only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure
HDInsight cluster types for the various tasks associated to the processing pipeline.
You are planning a storage strategy for a large amount of analytic data used for the crime data analytics system. The initial data load involves over 100 billion records, and more than two billion records will be added daily.
You already created an Apache Hadoop cluster in HDInsight premium.
You need to implement the storage strategy to meet the following requirements:
What should you create?
A. a virtual machine (VM) by using the Data Science Virtual Machine template for Windows that has premium storage, a G-series size, and uses Microsoft SQL Server 2016 to store the data
B. an Azure Blob storage account by using the Azure portal
C. an Azure Data Lake Analytics service by using Azure PowerShell
D. an Azure Data Lake Store account by using the Azure portal
References: https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-get- started-portal
NEW QUESTION: 2
ICMPエコーIP SLAをサポートするには、どの2つの基準を満たす必要がありますか？ （2つ選択）
NEW QUESTION: 3
A. Option C
B. Option B
C. Option D
D. Option A
MPLS belongs to the family of packet-switched networks. MPLS operates at a layer that is generally considered to lie between traditional definitions of OSI Layer 2 (data link layer) and Layer 3 (network layer), and thus is often referred to as a layer 2.5 protocol.
NEW QUESTION: 4
According to the CAPM, the expected return from a risky asset is a function of:
A. how much the risky asset contributes to portfolio risk
B. all of the above
C. diversifiable risk that the asset brings
D. the riskiness, ie the volatility of the risky asset alone
According to the CAPM, the expected return from a risky asset is a function of the contribution of the risky asset to the total risk of the market portfolio. Nothing else matters. All assets are priced according to the risk they bring to the market portfolio, regardless of their individual level of risk. An asset that is very volatile on its own, but has a negative correlation to the market may be priced high, ie have low expected return, because of its impact on the risk of the market portfolio. Therefore Choice 'a' is the correct answer, and the other options are incorrect.
Recall that according to the CAPM = covariancex, y / variancex, where x is the market portfolio and y is the risky asset.
The beta itself is a function of the covariance of the asset's returns with market returns, and therefore only the driver of expected return for an asset is its beta, which is determined by the asset's contribution to portfolio risk. ( = covariance(x, y) / variance(x), where x is the market portfolio and y is the risky asset. )