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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 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 the Machine learning experiment, and then add an Execute Python Script module.
B. Process the files by using Python to store the data to a Hadoop instance.
C. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
D. Create an Azure HDInsight Hadoop cluster.
E. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
Answer: A,B,D
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 2
毎日200,000個の新しいファイルを生成するAzureStorageアカウントがあります。ファイル名の形式は次のとおりです。
{YYYY} / {MM} / {DD} / {HH} / {CustomerID} .csv。
ストレージアカウントからAzureDataLakeに1時間に1回新しいデータを読み込むAzureDataFactoryソリューションを設計する必要があります。このソリューションでは、ロード時間とコストを最小限に抑える必要があります。
ソリューションをどのように構成する必要がありますか?回答するには、回答領域で適切なオプションを選択します。
注:正しい選択はそれぞれ1ポイントの価値があります
Answer:
Explanation:
Explanation
Box 1: Incremental load
When you start to build the end to end data integration flow the first challenge is to extract data from different data stores, where incrementally (or delta) loading data after an initial full load is widely used at this stage.
Now, ADF provides a new capability for you to incrementally copy new or changed files only by LastModifiedDate from a file-based store. By using this new feature, you do not need to partition the data by time-based folder or file name. The new or changed file will be automatically selected by its metadata LastModifiedDate and copied to the destination store.
Box 2: Tumbling window
Tumbling window triggers are a type of trigger that fires at a periodic time interval from a specified start time, while retaining state. Tumbling windows are a series of fixed-sized, non-overlapping, and contiguous time intervals. A tumbling window trigger has a one-to-one relationship with a pipeline and can only reference a singular pipeline.
Reference:
https://azure.microsoft.com/en-us/blog/incrementally-copy-new-files-by-lastmodifieddate-with-azure-data-factor
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger
NEW QUESTION: 3
When setting up Web-meetings with ENOVIA, what responsibilities can a participant perform?
A. Join the meeting
B. Start the meeting
C. All the above
D. Close or End the Meeting
Answer: A