PL-300 Prepare the data Practice Question
You are designing a Power BI solution that ingests data from multiple sources: Azure Blob Storage, Salesforce, and an on-premises Oracle database. The data must be combined into a single semantic model. The Oracle database contains sensitive customer information that must be masked before being loaded. Which approach should you use to prepare the data?
⚠ Common exam trap
It's easy for candidates to assume data masking must be done at the database level or via a separate service like SSAS, but Power BI dataflows can perform masking during the transformation phase, making them the most integrated and efficient solution for this multi-source scenario.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a Power BI dataflow that extracts, transforms, and masks data before loading into the semantic model
Power BI dataflows provide a cloud-based ETL solution that can connect to Azure Blob Storage, Salesforce, and on-premises Oracle (via an on-premises data gateway), perform transformations including data masking, and then load the prepared data into a shared semantic model. This approach centralizes data preparation, ensures sensitive data is masked before any downstream consumption, and supports scheduled refreshes without requiring additional infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a Power BI dataflow that extracts, transforms, and masks data before loading into the semantic model
Why this is correct
Power BI dataflows are a native, cloud-based ETL service in the Power BI service that can connect to a wide range of sources, apply data cleansing and shaping logic through Power Query Online, and include masking steps such as hashing, replacing, or removing sensitive columns before the resulting entity is loaded into a semantic model. This centralized approach supports enterprise-scale data preparation, scheduled refresh, and reusable entities that can serve multiple reports and datasets, making it the right architecture for this scenario.
- ✗
Use DirectQuery to connect to all sources and rely on database-level masking
Why it's wrong here
Using DirectQuery means Power BI issues native queries directly to the underlying sources and does not provide a transformation or masking layer within Power BI—you are limited to whatever masking is already enforced in the source, which is not consistent across all source types and may not exist at all in many databases. Additionally, DirectQuery can introduce performance and latency issues when many sources are combined, and it does not allow Power Query transformations such as merging, filtering, or masking sensitive values before data reaches the semantic model.
- ✗
Import all data into Power BI Desktop and apply transformations in the Power Query Editor
Why it's wrong here
Importing all data into Power BI Desktop and transforming it in Power Query Editor is functionally possible, but it is a single-desktop-authoring approach that lacks the central management, sharing, and governance capabilities of Power BI dataflows. Each developer would need to reproduce the same masking logic, and the dataset refresh would depend on a developer's machine or a separately published gateway, which does not scale well for an enterprise ingesting data from multiple sources.
- ✗
Stage the data in an Azure SQL Database and use SQL Server Analysis Services to mask data
Why it's wrong here
Staging data in Azure SQL Database and using SQL Server Analysis Services (SSAS) to mask data is not a native part of Power BI data preparation—SSAS is a separate analytical engine and its masking capabilities are not integrated with Power BI ingestion or semantic model build processes. This approach would require substantial additional infrastructure, such as Azure SQL Database provisioning, pipeline orchestration (e.g., Azure Data Factory), and SSAS deployment, adding complexity without providing the native, source-to-model preparation pipeline that a dataflow offers.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.