Preparing for the PL-300T00 Power BI Data Analyst exam can be daunting, but with the right guidance and resources, you can ace it. This blog post provides the top 25 interview questions and answers to help you succeed in designing Microsoft Azure infrastructure solutions.
Power BI is a suite of business analytics tools developed by Microsoft that enables users to visualize data and share insights across an organization or embed them in an app or website. It helps organizations turn raw data into informative insights for better decision-making. The key components of Power BI include Power BI Desktop (for authoring reports), Power BI Service (cloud-based sharing and collaboration), Power BI Mobile (mobile apps), Power BI Gateway (for data refresh and connectivity), and Power BI Report Server (for on-premises report publishing). Together, they enable end-to-end data transformation, analysis, and reporting.
Import mode pulls the data into Power BI and stores it in a compressed, in-memory cache, which offers fast performance and rich modeling capabilities. DirectQuery, on the other hand, does not import data into Power BI. Instead, it queries the source database directly each time a report is run, ensuring real-time or near real-time data access. Import mode is preferred for speed and functionality, while DirectQuery is used when data freshness and source governance are priorities.
DAX (Data Analysis Expressions) is a formula language used in Power BI, Excel, and other Microsoft products to define custom calculations and expressions on data models. DAX is critical for performing aggregations, filtering data contextually, creating calculated columns or measures, and implementing business logic. With DAX, analysts can define metrics like year-to-date sales, running totals, or percent growth, enabling deeper insights from data.
RLS in Power BI enables the restriction of data access for specific users by applying DAX filters on roles. You define roles and rules in Power BI Desktop, which are then published to the Power BI service. When a user views the report, only the data that meets the rule criteria is visible to them. This ensures data confidentiality and compliance, especially in multi-user environments.
You can configure scheduled refresh in the Power BI Service under the dataset settings. After publishing a report to the Power BI workspace, navigate to the dataset, and under the Schedule Refresh tab, you can:
| Feature | Calculated Column | Measure |
|---|---|---|
| Storage | Stored in the model | Calculated at runtime |
| Usage | Used like a regular column | Used in aggregations |
| Performance | Slower for large datasets | Faster and more efficient |
| Example | Profit = Sales - Cost |
Total Sales = SUM(Sales[Amount]) |
Power BI Gateway acts as a bridge between on-premises data sources and the Power BI Service. It enables scheduled refreshes and live queries for datasets using local databases.There are two modes: Personal mode (for individual use) and Standard mode (enterprise use).The gateway ensures secure data transfer without moving the data itself.It is essential for hybrid data architecture setups.
Query folding is the process by which Power Query transformations are translated into native queries that are executed by the data source rather than within Power BI. This helps optimize performance by pushing computations to the source system, reducing data transfer and load times. Query folding is more effective with relational databases and can break if unsupported transformations are applied. Maintaining query folding ensures efficient data refresh and optimized performance.
Power Query Editor is a data transformation tool within Power BI Desktop that enables users to clean, shape, and combine data from multiple sources before loading it into the data model. It supports a wide variety of transformations such as removing rows/columns, pivoting data, merging tables, and changing data types. Power Query uses the M language under the hood and ensures repeatable and automated ETL workflows.
KPIs (Key Performance Indicators) in Power BI help visualize how well a company is achieving its key business objectives. They typically consist of:
Data lineage provides a visual map of how data flows from source systems through transformations to the final report visuals. It is essential for:
A semantic model defines how users interpret and analyze the data. It includes relationships, hierarchies, calculated fields, and metadata. It transforms raw data into user-friendly structures using naming conventions, formatting, and business logic. Power BI uses this model to create self-service analytics experiences, ensuring consistency and ease of use across the organization.
A hierarchy in Power BI is a structured arrangement of fields that define levels of data granularity.
For example, a Date Hierarchy might include Year → Quarter → Month → Day.
It enables drill-down capabilities in visuals for in-depth analysis.
Hierarchies simplify report creation by combining multiple fields into one structure.
They enhance navigation and user interaction in reports.
Aggregations are summarized versions of detailed data (e.g., sum of sales by region) that improve performance in large models. Power BI can detect and use these aggregations to respond to queries faster. You can define automatic or manual aggregations in the model. They help in optimizing memory usage and reducing query time for large datasets.
You can share reports with external users by using Power BI Pro licenses and inviting them by email.
Ensure the external user also has a Power BI Pro license.
Use Azure B2B or enable Guest Access via Azure Active Directory.
Reports can also be embedded in secure portals or websites.
Proper permissions and RLS rules should be configured for secure sharing.
Power BI includes several features to support data governance: