You have a Fabric workspace that contains an eventstream named EventStreaml. EventStreaml outputs events to a table named Tablel in a lakehouse. The streaming data is souiced from motorway sensors and represents the speed of cars. You need to add a transformation to EventStream1 to average the car speeds. The speeds must be grouped by non-overlapping and contiguous time intervals of one minute. Each event must belong to exactly one window. Which windowing function should you use?
Correct Answer: A
Question 37
You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1. Notebook1 reads data into a DataFrame from a table named Table1 and applies transformation logic. The data from the DataFrame is then written to a new Delta table named Table2 by using a merge operation. You need to consolidate the underlying Parquet files in Table1. Which command should you run?
Correct Answer: C
To consolidate the underlying Parquet files in Table1 and improve query performance by optimizing the data layout, you should use the OPTIMIZE command in Delta Lake. The OPTIMIZE command coalesces smaller files into larger ones and reorganizes the data for more efficient reads. This is particularly useful when working with large datasets in Delta tables, as it helps reduce the number of files and improves performance for subsequent queries or operations like MERGE.
Question 38
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains a table named Customer. Customer contains the following data.
Correct Answer:
Explanation:
Question 39
You are building a data loading pattern for Fabric notebook workloads. You have the following code segment: For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Correct Answer:
Question 40
You have a Fabric workspace that contains a lakehouse named Lakehouse1. In an external data source, you have data files that are 500 GB each. A new file is added every day. You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements Trigger the process when a new file is added. Provide the highest throughput. Which type of item should you use to ingest the data?
Correct Answer: A
To efficiently ingest large data files (500 GB each) into Lakehouse1 with high throughput and trigger the process when a new file is added, a Data pipeline is the most suitable solution. Data pipelines in Fabric are ideal for orchestrating data movement and can be configured to automatically trigger based on file arrivals or other events. This solution meets both requirements: ingesting the data without transformations (since you just need to copy the data) and triggering the process when new files are added. Topic 1, Litware, Inc Overview 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 Next 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 Question button to return to the question. Overview Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents. Existing Environment. Fabric Environment Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1. The company has a data engineering team that uses Python for data processing. Existing Environment. Data Processing The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system. Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled. Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder. Existing Environment. Sales Data Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes. In the source system, the sales data refreshes every six hours starting at midnight each day. The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source: A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address. Existing Environment. Security Groups Litware has the following security groups: Existing Environment. Performance Issues Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: "The SQL query failed while running." The data engineering team wants to debug the issue and find queries that cause more than one failure. When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process. The company's sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning. Requirements. Planned Changes Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets. Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API. Requirements. Version Control Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege. Requirements. Governance Requirements To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned. Requirements. Data Requirements Litware identifies the following data requirements:
