You have an enterprise data warehouse in Azure Synapse Analytics. You need to monitor the data warehouse to identify whether you must scale up to a higher service level to accommodate the current workloads Which is the best metric to monitor? More than one answer choice may achieve the goal. Select the BEST answer.
Correct Answer: D
Question 127
You have the following Azure Data Factory pipelines * ingest Data from System 1 * Ingest Data from System2 * Populate Dimensions * Populate facts ingest Data from System1 and Ingest Data from System1 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System* Populate Facts must execute after the Populate Dimensions pipeline. All the pipelines must execute every eight hours. What should you do to schedule the pipelines for execution?
Correct Answer: A
Question 128
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 hopping window that uses a hop size of 5 seconds and a window size 10 seconds. Does this meet the goal?
Correct Answer: B
Explanation Instead use a tumbling window. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. Reference: https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics Topic 1, Litware, inc. Case study This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study To display the first question in this case study, click the button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the button to return to the question. Overview Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared foods, such as sandwiches and pizzas. Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout. Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks notebooks. Requirements Business Goals Litware wants to create a new analytics environment in Azure to meet the following requirements: * See inventory levels across the stores. Data must be updated as close to real time as possible. * Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products. * Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data. Technical Requirements Litware identifies the following technical requirements: * Minimize the number of different Azure services needed to achieve the business goals. * Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware. * Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services. * Use Azure Active Directory (Azure AD) authentication whenever possible. * Use the principle of least privilege when designing security. * Stage Inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed. * Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant. * Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion. Planned Environment Litware plans to implement the following environment: * The application development team will create an Azure event hub to receive real-time 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. * Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table. * Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table. * Daily inventory data comes from a Microsoft SQL server located on a private network. * Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next year. * Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every four hours. * Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.
Question 129
The storage account container view is shown in the Refdata exhibit. (Click the Refdata tab.) You need to configure the Stream Analytics job to pick up the new reference data. What should you configure? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Correct Answer:
Question 130
You have an Azure Data Factory pipeline named pipeline1 that is invoked by a tumbling window trigger named Trigger1. Trigger1 has a recurrence of 60 minutes. You need to ensure that pipeline1 will execute only if the previous execution completes successfully. How should you configure the self-dependency for Trigger1?
Correct Answer: D
Tumbling window self-dependency properties In scenarios where the trigger shouldn't proceed to the next window until the preceding window is successfully completed, build a self-dependency. A self-dependency trigger that's dependent on the success of earlier runs of itself within the preceding hour will have the properties indicated in the following code. Example code: "name": "DemoSelfDependency", "properties": { "runtimeState": "Started", "pipeline": { "pipelineReference": { "referenceName": "Demo", "type": "PipelineReference" } }, "type": "TumblingWindowTrigger", "typeProperties": { "frequency": "Hour", "interval": 1, "startTime": "2018-10-04T00:00:00Z", "delay": "00:01:00", "maxConcurrency": 50, "retryPolicy": { "intervalInSeconds": 30 }, "dependsOn": [ { "type": "SelfDependencyTumblingWindowTriggerReference", "size": "01:00:00", "offset": "-01:00:00" } ] } } }
