PL-300 Practice Questions
Prepare for PL-300 with more than an answer.
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- v1Version 1 214 questions Current
- DA-100Legacy Analyzing Data with Power BI 47 questions Locked
- Exam fee
- $165 USD
- Level
- Associate
- Valid for
- 1 year
Domains covered on the exam 4
- Prepare the data25%
- Model the data25%
- Visualize and analyze the data25%
- Manage and secure Power BI20%
- 1
You are importing a JSON file containing nested data about customer orders. The file structure has a top-level array of orders, and each order object contains a nested array of line items. In Power Query, after connecting to the JSON file and converting it to a table, what is the next critical step to correctly flatten the data so that you have one row per line item?
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Correct answer: B
When dealing with nested arrays (lists) in Power Query, the 'Expand to New Rows' operation is the correct tool. It takes each item within the nested list in the selected column and creates a new row for it, duplicating the values from the other columns in the parent record. This effectively flattens the hierarchical structure into a relational, tabular format.
- 2
You have a Power BI dataset connecting to an Azure Synapse Analytics SQL pool using DirectQuery. Users report that a specific matrix visual with many measures is extremely slow. The data engineering team has confirmed the underlying database is performing well. You need to diagnose which specific element within the Power BI report is causing the slowdown. What is the most effective tool within Power BI Desktop for this purpose?
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Correct answer: C
The Performance Analyzer is the built-in tool specifically designed to record and display the performance of every visual in a report. By starting a recording and interacting with the slow matrix, it will provide a detailed breakdown of the time spent on the DAX query, visual display, and other operations for that specific visual. This allows you to pinpoint the exact DAX query being sent to the DirectQuery source and analyze its performance, making it the most direct tool for this diagnosis.
- 3
You are building a report that includes a line chart showing sales over time. To help users understand the data, you need to add a horizontal line representing the average sales for the entire period shown on the chart. Which feature in the Visualizations pane should you use?
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Correct answer: B
The Analytics pane (represented by a magnifying glass icon in the Visualizations pane) contains options for adding dynamic reference lines to visuals. The 'Average line' feature will automatically calculate the average of the measure in the visual and display it as a line, providing immediate context for the data points.
- 4
A project manager wants a Power BI dashboard that provides an at-a-glance view of key project metrics from multiple reports. The manager needs to receive an email with a snapshot of the dashboard every Monday morning and also be notified immediately if the 'Projects Over Budget' KPI exceeds 10. Which TWO features of the Power BI service should be configured to meet these requirements? (Select TWO).
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Correct answer: B, C
The Subscriptions feature is designed to send scheduled email snapshots of a report or dashboard to specified users.
Data Alerts are used to monitor specific tiles on a dashboard (like KPIs, cards, or gauges) and trigger a notification when the data crosses a defined threshold.
- 5
Case Study: Global Retail Corp
Company Background:
Global Retail Corp is a multinational retailer with operations in North America, Europe, and Asia. They have a central data warehouse built on Azure Synapse Analytics which houses sales, inventory, and customer data. Each region operates semi-autonomously, maintaining its own product categorization in local ERP systems. The company is standardizing its analytics on Power BI.Current Situation:
A central BI team is building a global sales report. They are connecting to the Azure Synapse data warehouse using DirectQuery to ensure data is always current. A significant challenge is that product categories differ across regions. For example, the same product might be 'Electronics' in North America, 'Électronique' in Europe, and '電子製品' in Asia. The company wants a single, unified 'Global Category' slicer in the report.Requirements:
- The model must maintain a DirectQuery connection to the main sales fact table in Azure Synapse to ensure real-time data.
- A mapping table, which translates regional categories to a standardized global category, is maintained in a SharePoint list and is updated infrequently.
- The final report must not show regional category names, only the standardized global categories.
- The solution must be performance-efficient, minimizing the impact on the DirectQuery source.
Which data modeling approach best satisfies all the requirements?
graph TD subgraph Azure Synapse (DirectQuery) FactSales[FactSales] DimProduct[DimProduct] end subgraph SharePoint (Import) CategoryMap[Category Mapping List] end subgraph Power BI Model Report --> Model end FactSales --> Model DimProduct --> Model CategoryMap --> Model Internet((User)) --> ReportShow answer details
Correct answer: B
This is the optimal solution. A composite model allows for mixing DirectQuery and Import storage modes. This meets the real-time data requirement for sales data while efficiently handling the static mapping data from SharePoint. Creating a relationship between the dimension table in DirectQuery and the imported mapping table allows slicers based on the global category to correctly filter the DirectQuery source, satisfying all stated requirements.
- 6
A financial services firm is developing a Power BI report to analyze stock market data. The primary data source is a large Azure SQL Database containing billions of transaction records. The report must provide sub-second query performance for visuals and allow analysts to explore the data using slicers. The data in the database is updated every few minutes. The firm wants to avoid data duplication and minimize data latency. Which storage mode configuration should be used for the main transaction table in the Power BI model?
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Correct answer: B
DirectQuery is the optimal choice for this scenario. It connects directly to the Azure SQL Database, avoiding data duplication and ensuring that the report reflects the most current data. Given the large volume of data (billions of records), Import mode would be impractical and lead to slow refreshes. DirectQuery sends queries to the source database, leveraging its processing power to handle large datasets and provide near real-time data with low latency.
- 7
You are developing a Power BI report for a logistics company. You have two tables: 'Shipments' and 'Carriers'. The 'Shipments' table contains a 'CarrierID' column. The 'Carriers' table contains 'CarrierID' and 'CarrierName'. You need to add the 'CarrierName' to the 'Shipments' table to facilitate analysis. The 'Shipments' table is very large, with over 50 million rows. Which Power Query operation is the most performance-efficient for this task?
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Correct answer: B
Merge Queries is the correct operation. It is equivalent to a join in SQL and allows you to combine two tables based on a matching column, in this case 'CarrierID'. This operation adds columns from one table to another. Appending queries combines tables vertically by adding rows, which is not the desired outcome here. For large tables, merging is generally more efficient than trying to perform lookups row by row.
- 8
You are cleaning a dataset in Power Query that contains a 'ProductSKU' column. The SKU is formatted as 'CAT-ID-SIZE', for example, 'SHRT-105-XL'. You need to extract the three parts ('CAT', 'ID', and 'SIZE') into separate columns. Which transformation provides the most direct way to achieve this?
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Correct answer: C
The 'Split Column by Delimiter' transformation is specifically designed for this purpose. By specifying the hyphen '-' as the delimiter, Power Query will automatically create new columns containing the separated parts of the string. This is the most efficient and direct method for this common data cleaning task.
- 9
A hospital analyst is creating a Power BI model to track patient readmissions. The model includes a 'Patients' dimension table and an 'Admissions' fact table. The hospital wants to analyze admissions based on both the admission date and the discharge date. Both dates need to relate to a central 'Calendar' dimension table. How should this be implemented in the data model to avoid ambiguity and follow best practices?
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Correct answer: B
This is the classic implementation of a role-playing dimension. The 'Calendar' table plays multiple roles (admission calendar and discharge calendar). Power BI only allows one active relationship between two tables. The best practice is to set the primary relationship (e.g., on Admission Date) as active and create an inactive relationship for the secondary date. DAX measures can then activate the inactive relationship on-demand using the USERELATIONSHIP function, allowing for flexible analysis without creating model ambiguity.
- 10
You are optimizing a Power BI data model for a retail company. The model contains a large fact table, 'Sales', with 100 million rows. You notice that several visuals are slow to render. Using Performance Analyzer, you identify that a measure calculating the 'Year-to-Date Sales' is the primary bottleneck. The current DAX formula for the measure is: YTD Sales = TOTALYTD(SUM(Sales[SalesAmount]), 'Calendar'[Date]). What is the most likely cause of the poor performance?
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Correct answer: B
Time intelligence functions in DAX, such as TOTALYTD, rely on a properly configured date table. If the 'Calendar' table is not officially marked as a date table in the model properties, the DAX engine cannot use its optimized time intelligence algorithms. Instead, it falls back to a much slower, less efficient calculation method, which becomes a significant performance bottleneck on large fact tables. Marking the table as a date table is a critical optimization step.
