Online Access Free 070-475 Practice Test

You have a pipeline that contains an input dataset in Microsoft Azure Table Storage and an output dataset in Azure Blob storage. You have the following JSON data.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the JSON data.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: Every three days at 10.00
anchorDateTime defines the absolute position in time used by the scheduler to compute dataset slice boundaries.
"frequency": "<Specifies the time unit for data slice production. Supported frequency: Minute, Hour, Day, Week, Month>",
"interval": "<Specifies the interval within the defined frequency. For example, frequency set to 'Hour' and interval set to 1 indicates that new data slices should be produced hourly> Box 2: Every minute up to three times.
retryInterval is the wait time between a failure and the next attempt. This setting applies to present time. If the previous try failed, the next try is after the retryInterval period.
Example: 00:01:00 (1 minute)
Example: If it is 1:00 PM right now, we begin the first try. If the duration to complete the first validation check is 1 minute and the operation failed, the next retry is at 1:00 + 1min (duration) + 1min (retry interval) =
1:02 PM.
For slices in the past, there is no delay. The retry happens immediately.
retryTimeout is the timeout for each retry attempt.
maximumRetry is the number of times to check for the availability of the external data.

You have raw data in Microsoft Azure Blob storage. Each data file is 10 KB and is the XML format.
You identify the following requirements for the data:
* The data must be converted into a flat data structure by using a C# MapReduce job.
* The data must be moved to an Azure SQL database, which will then be used to visualize the data.
* Additional stored procedures must run against the data once the data is in the database.
You need to create the workflow for the Azure Data Factory pipeline.
Which activity type should you use for each requirement? To answer, drag the appropriate workflow components to the correct requirements. Each workflow component may be used once, more than once, or not at all. You may need to drag the split bar between the panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: HDinsightMapReduce
The HDInsight MapReduce activity in a Data Factory pipeline invokes MapReduce program on your own or on-demand HDInsight cluster.
Box 2: HDInsightStreaming
Box 3: SQLServerStoredProcedure

You are designing an Internet of Thing: (IoT) solution intended to identify trends. The solution requires the realtime analysis of data originating from sensors. The results of the analysis will be stored in a SQL database.
You need to recommend a data processing solution that uses the Transact-SQL language.
Which data processing solution should you recommend?

• A.Microsoft Azure HDInsight Hadoop clusters
• B.Microsoft SQL Server Integration Services (SSIS)
• C.Microsoft Azure Stream Analytics
• D.Microsoft Azure Machine Learning

You need to create a query that identifies the trending topics.
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

From scenario: Topics are considered to be trending if they generate many mentions in a specific country during a 15-minute time frame.
Box 1: TimeStamp
Azure Stream Analytics (ASA) is a cloud service that enables real-time processing over streams of data flowing in from devices, sensors, websites and other live systems. The stream-processing logic in ASA is expressed in a SQL-like query language with some added extensions such as windowing for performing temporal calculations.
ASA is a temporal system, so every event that flows through it has a timestamp. A timestamp is assigned automatically based on the event's arrival time to the input source but you can also access a timestamp in your event payload explicitly using TIMESTAMP BY:
SELECT * FROM SensorReadings TIMESTAMP BY time
Box 2: GROUP BY
Example: Generate an output event if the temperature is above 75 for a total of 5 seconds SELECT sensorId, MIN(temp) as temp FROM SensorReadings TIMESTAMP BY time GROUP BY sensorId, SlidingWindow(second, 5) HAVING MIN(temp) > 75 Box 3: SlidingWindow Windowing is a core requirement for stream processing applications to perform set-based operations like counts or aggregations over events that arrive within a specified period of time. ASA supports three types of windows: Tumbling, Hopping, and Sliding.
With a Sliding Window, the system is asked to logically consider all possible windows of a given length and output events for cases when the content of the window actually changes - that is, when an event entered or existed the window.

You need to recommend a platform architecture for a big data solution that meets the following requirements:
Supports batch processing
Provides a holding area for a 3-petabyte (PB) dataset
Minimizes the development effort to implement the solution
Provides near real time relational querying across a multi-terabyte (TB) dataset Which two platform architectures should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A.Microsoft Azure Table Storage
• B.a Microsoft Azure HDInsight Storm cluster
• C.a Microsoft Azure HDInsight Hadoop cluster
• D.a Microsoft SQL Server database
• E.a Microsoft Azure SQL data warehouse