Question # 1 From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays. The data contains the following columns
Answer Description
Question # 2 What should you do to improve high availability of the real-time data processing solution?
A. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
B. Deploy a High Concurrency Databricks cluster.
C. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
D. Set Data Lake Storage to use geo-redundant storage (GRS).
Click for Answer
A. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
Answer Description Explanation: Guarantee Stream Analytics job reliability during service updates Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure’s paired region model. Scenario: The application development team will create an Azure event hub to receive realtime sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability
Question # 3 You store files in an Azure Data Lake Storage Gen2 container. The container has the storage policy shown in the following exhibit
Answer Description
Question # 4 Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a tumbling window, and you set the window size to 10 seconds.
Does this meet the goal? A. YesB. No
Click for Answer
A. Yes
Answer Description
Explanation:
A tumbling window is the correct type of window for counting events (such as tweets) in distinct, non-overlapping time intervals, ensuring that each event (tweet) is counted only once in each time window. Since the window size is set to 10 seconds, it will count all tweets in each 10-second interval without overlap, achieving the desired outcome. Thus, the solution of using a tumbling window with a 10-second window size does meet the goal.
Question # 5 You need to design a solution that will process streaming data from an Azure Event Hub and output the data to Azure Data Lake Storage. The solution must ensure that analysts can interactively query the streaming data. What should you use?
A. event triggers in Azure Data Factory
B. BAzure Stream Analytics and Azure Synapse notebooks
C. Structured Streaming in Azure Databricks
D. Azure Queue storage and read-access geo-redundant storage (RA-GRS)
Click for Answer
B. BAzure Stream Analytics and Azure Synapse notebooks
Question # 6 You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements: • Minimize query latency. • Maximize the number of users that can run queues on the cluster at the same time « Reduce overall costs without compromising other requirements Which cluster type should you recommend?
A. Standard with Auto termination
B. Standard with Autoscaling
C. High Concurrency with Autoscaling
D. High Concurrency with Auto Termination
Click for Answer
C. High Concurrency with Autoscaling
Answer Description Explanation: A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies. Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don’t need to provision the cluster to match a workload. Reference: https://docs.microsoft.com/en-us/azure/databricks/clusters/configure
Question # 7 You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName. You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks. A new column must be created that concatenates the FirstName and LastName values. You create the following components: A destination table in Azure Synapse An Azure Blob storage container A service principal Which five actions should you perform in sequence next in is Databricks notebook? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order
Answer Description
Question # 8 You have an Azure Synapse Analytics dedicated SQL pool that contains a large fact table. The table contains 50 columns and 5 billion rows and is a heap. Most queries against the table aggregate values from approximately 100 million rows and return only two columns. You discover that the queries against the fact table are very slow. Which type of index should you add to provide the fastest query times?
A. nonclustered columnstore
B. clustered columnstore
C. nonclustered
D. clustered
Click for Answer
Answer Description Explanation: Clustered columnstore indexes are one of the most efficient ways you can store your data in dedicated SQL pool. Columnstore tables won't benefit a query unless the table has more than 60 million rows. Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sqlpool
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