Topic 1, Contoso, Ltd

Case Study

Overview

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Overview. Company Overview

Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics.

Overview. IT Structure

The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems.

The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data.

The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL.

Existing Environment. Fabric

Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.

Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode.

Existing Environment. Source Systems

Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website.

The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint.

Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions.

Existing Environment. Product Data

POS1 contains a product list and related data.

The data comes from the following three tables:

– Products

– ProductCategories

– ProductSubcategories

In the data, products are related to product subcategories, and subcategories are related to product categories.

Existing Environment. Azure

Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:

– DataAnalysts: Contains the data analysts

– DataEngineers: Contains the data engineers

Contoso has an Azure subscription.

The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric.

Existing Environment. User Problems

The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric.

The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail.

Requirements. Planned Changes

Contoso plans to create the following two lakehouses:

– Lakehouse1: Will store both raw and cleansed data from the sources

– Lakehouse2: Will serve data in a dimensional model to users for analytical queries

Additional items will be added to facilitate data ingestion and transformation.

Contoso plans to use Azure Repos for source control in Fabric.

Requirements. Technical Requirements

The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization.

Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers.

Data imports must run simultaneously, when possible.

The use of email data from the Amazon S3 bucket must meet the following requirements:

– Minimize egress costs associated with cross-cloud data access.

– Prevent saving a copy of the raw data in the lakehouses.

Items that relate to data ingestion must meet the following requirements:

– The items must be source controlled alongside other workspace items.

– Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.

– No changes other than changes to the file formats must be implemented before the data lands in the bronze layer.

– Development effort must be minimized and a built-in connection must be used to import the source data.

– In the event of a connectivity error, the ingestion processes must attempt the connection again.

Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB.

Once a week, old files that are no longer referenced by a Delta table log must be removed.

Requirements. Data Transformation

In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1.

Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer.

Requirements. Data Security

Security in Fabric must meet the following requirements:

– The data engineers must have read and write access to all the lakehouses, including the underlying files.

– The data analysts must only have read access to the Delta tables in the gold layer.

– The data analysts must NOT have access to the data in the bronze and silver layers.

– The data engineers must be able to commit changes to source control in WorkspaceA.

You need to ensure that the data analysts can access the gold layer lakehouse.

What should you do?

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You have a Fabric warehouse named DW1 that loads data by using a data pipeline named Pipeline1. Pipeline1 uses a Copy data activity with a dynamic SQL source. Pipeline1 is scheduled to run every 15 minutes.

You discover that Pipeline1 keeps failing.

You need to identify which SQL query was executed when the pipeline failed.

What should you do?

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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?

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You have a Fabric workspace that contains an eventstream named Eventstream1. Eventstream1 processes data from a thermal sensor by using event stream processing, and then stores the data in a lakehouse.

You need to modify Eventstream1 to include the standard deviation of the temperature.

Which transform operator should you include in the Eventstream1 logic?

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You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1.

You need to identify which version of Delta was used when Notebook1 was executed.

What should you use?

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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 add the make_list() function to the output columns.

Does this meet the goal?

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You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.

You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each.

You need to minimize how long it takes to query Table1.

What should you do?

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Correct Answer: C
C

Explanation:

Problem Overview:

– Table1 has 2,000 small Parquet files (1 MB each).

– Query performance suffers when the table contains numerous small files because the query engine must process each file individually, leading to significant overhead.

Solution:

– To improve performance, file compaction is necessary to reduce the number of small files and create larger, optimized files.

Commands and Their Roles:

– OPTIMIZE Command:

– Compacts small Parquet files into larger files to improve query performance.

– It supports optional features like V-Order, which organizes data for efficient scanning.

VACUUM Command:

– Removes old, unreferenced data files and metadata from the Delta table.

– Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.

You have five Fabric workspaces.

You are monitoring the execution of items by using Monitoring hub.

You need to identify in which workspace a specific item runs.

Which column should you view in Monitoring hub?

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You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table.

The table contains the following columns.

You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.

You need to prepare the data.

Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

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You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod.

You need to deploy an eventhouse as part of the deployment process.

What should you use to add the eventhouse to the deployment process?

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