HOTSPOT

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.

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Correct Answer:

You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.

You have a deployment pipeline named deployPipeline1 that deploys Workspace1 to Workspace2.

You restructure Workspace1 by adding a folder named Folder1 and moving Pipeline1 to Folder1.

You use deployPipeline1 to deploy Workspace1 to Workspace2.

What occurs to Workspace2?

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You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.

In Workspace1, you create a new notebook named Notebook2.

You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.

What should you do?

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DRAG DROP

You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData.

The following is a sample of the data in TaxiData.

You need to build two KQL queries. The solution must meet the following requirements:

– One of the queries must partition RunningTotalAmount by VendorID.

– The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.

How should you complete each query? To answer, drag the appropriate values 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.

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Correct Answer:

Explanation:

Partition the RunningTotalAmount by VendorID. – Row_cumsum

The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID != prev(VendorID)).

Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type – Row_window_session

You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys.

DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year.

You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.

Users report that the performance of some of the reports has degraded over time and some visuals show errors.

You need to resolve the performance issues.

The solution must meet the following requirements:

Provide the best query performance.

Minimize operational costs.

Which should you do?

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You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains a lake house named Lakehouse1, a data pipeline, a notebook, and several Microsoft Power BI reports.

A user named User1 wants to use SQL to analyze the data in Lakehouse1.

You need to configure access for User1.

The solution must meet the following requirements:

– Provide User1 with read access to the table data in Lakehouse1.

– Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1.

– Prevent User1 from accessing other items in Workspace1.

What should you do?

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HOTSPOT

You are processing streaming data from an external data provider.

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.

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Correct Answer:

Explanation:

Litware from New York will be displayed at the top of the result set C Yes

The data is sorted first by Location in descending order and then by UnitsSold in descending order.

Since "New York" is alphabetically the last Location, it will appear first in the result set. Within "New York", Litware has the highest UnitsSold (1000), so it will be displayed at the top.

Fabrikam in Seattle will have value = 2 in the Rank column C No

The row_rank_dense function assigns dense ranks based on UnitsSold within each location.

In "Seattle":

– Contoso has UnitsSold = 300 → Rank 1

– Litware has UnitsSold = 100 → Rank 2

– Fabrikam also has UnitsSold = 100, so it shares the same rank (2) as Litware.

Litware in San Francisco will have the same value in the Rank column as Litware in New York C No The rank is calculated separately for each location. In "San Francisco":

– Both Relecloud and Litware have UnitsSold = 500, so they share the same rank (1).

– In "New York", Litware has the highest UnitsSold = 1000 → Rank 1.

Since ranks are calculated independently for each location, Litware in San Francisco does not share the same rank as Litware in New York.

Topic 2, Litware, Inc

Case Study

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:

– Sales Date

– Author

– Price

– Units

– SKU

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:

– Sales

– Fabric Admins

– Streaming Admins

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:

– Process the SEO data in near-real-time (NRT).

– Make the book reviews available in the lakehouse without making a copy of the data.

– When a new book cover image arrives in the Files folder, process the image as soon as possible.

You need to implement the solution for the book reviews.

Which should you do?

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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 have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.

You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.

Solution: You change the join type to kind=outer.

Does this meet the goal?

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HOTSPOT

You need to recommend a method to populate the POS1 data to the lakehouse medallion layers.

What should you recommend for each layer? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Bronze Layer: A pipeline Copy activity

The bronze layer is used to store raw, unprocessed data. The requirements specify that no transformations should be applied before landing the data in this layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and the ability to ingest the data directly into the Delta format in the bronze layer.

Silver Layer: A notebook

The silver layer involves extensive data cleansing (deduplication, handling missing values, and standardizing capitalization). A notebook provides the flexibility to implement complex transformations and is well-suited for this task.