Power BI Scenario-Based Questions 2025

This article concerns real-time and knowledgeable Power BI Scenario-Based Questions 2025. It is drafted with the interview theme in mind to provide maximum support for your interview. Go through these Power BI Scenario-Based Questions 2025 to the end, as all scenarios have their importance and learning potential.

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Disclaimer:
These solutions are based on my experience and best effort. Actual results may vary depending on your setup. Codes may need some tweaking.

Question 1: What issues can occur when using DirectQuery in Power BI for live dashboards, and how would you handle them?

DirectQuery sends real-time queries to the source which can cause major performance lag.
I’ve seen visuals take 15–30 seconds to load due to backend strain and complex joins.
I used Performance Analyzer to identify visuals firing heavy queries.
Then, I switched summary tables to Import mode and kept detailed ones in DirectQuery.
This hybrid setup cut query load drastically without removing live capability.
I also optimized source-side indexing and filters to improve query response.
Business users got quicker insights and the backend remained stable.
It taught the team to avoid DirectQuery for high-volume or highly interactive reports.

Question 2: If stakeholders demand real-time dashboard updates, how would you explain the trade-offs of using streaming datasets?

Streaming datasets allow real-time updates but lack features like time intelligence or historical filters.
You can’t create calculated columns, relationships, or complex DAX on them.
I’d ask stakeholders if they really need second-by-second updates or if 5-10 min refresh works.
I explain the extra costs of setting up streaming or push datasets via Azure or APIs.
In most cases, a scheduled Import with near-real-time refresh is more manageable.
I sometimes combine streaming visuals with static ones for a balanced view.
This avoids over-engineering and still satisfies urgent data visibility.
Helps keep the dashboard light, fast, and maintainable.

Question 3: If a stakeholder reports mismatched values in Power BI versus Excel or source reports, how would you investigate?

First, I clarify if they’re comparing the same metric logic—many times, they’re not.
I double-check DAX logic, especially filter context and applied slicers.
I verify the data model relationships and ensure correct join behavior.
Then, I compare the data refresh timing with the source system.
I export source data to Excel and match it against Power BI visuals.
Also check for duplicated or filtered data in Power Query steps.
Often, it’s a small transformation issue or misunderstood business rule.
Once fixed, I document the root cause for transparency.

Question 4: What process changes would you recommend when multiple teams work on the same Power BI model and create confusion?

I’ve seen this mess up enterprise reports due to duplicate measures and naming conflicts.
I suggest creating a certified dataset with all core KPIs and logic defined.
Teams can build separate reports off that dataset instead of editing the base model.
Use Dev → Test → Prod workspaces with proper deployment pipelines.
Maintain a naming convention for measures, tables, and visuals.
Document logic behind each KPI so teams don’t rebuild what already exists.
Assign clear ownership to datasets and restrict edit access to owners.
This boosts consistency, governance, and team coordination.

Question 5: If a Power BI dashboard becomes slow due to many visuals, how would you decide what to optimize or remove?

I open Performance Analyzer and note visuals with long DAX or render time.
I check if the same metric is shown in multiple visuals unnecessarily.
I replace complex visuals like stacked combos with cards or simple bars.
Drill-through and detail tabs help reduce visuals on the main page.
For mobile users, I create separate phone layouts to trim visuals.
I discuss with users what’s most important for decision-making.
Any visual not adding business value is removed without hesitation.
Result: faster dashboards and happier users.

Question 6: Can Power BI be used for forecasting inventory? How would you approach such a request?

Power BI can provide basic forecast trends using built-in visuals like line charts.
But for complex forecasting, I suggest Azure ML or Python-based prediction models.
These models push results back into Power BI via API or dataflow.
This keeps your dashboards lightweight but powered by real machine learning.
I clarify that built-in Power BI forecasting isn’t meant for supply chain precision.
I’ve used both methods in retail projects depending on time and budget.
Helps set realistic expectations with the business team.
Keeps architecture lean and scalable.

Question 7: What’s a real-world mistake you’ve seen when using calculated columns instead of measures in Power BI?

Once I saw calculated columns used to calculate YTD values—terrible choice.
The model grew 3x in size and refresh time went from 5 to 20 minutes.
I changed the logic to use DAX measures with time intelligence functions.
That reduced memory use and allowed dynamic slicers.
Measures are computed on-the-fly and adapt to filter context better.
Calculated columns are only good for static values, not aggregations.
After this, the dev team learned to rethink when and why they use columns.
It was a big win in performance and clarity.

Question 8: You inherit a Power BI model with messy relationships. How would you fix or improve it?

I start by mapping all relationships and checking for bidirectional filters.
Then I try restructuring into a clean star schema if possible.
Remove unnecessary many-to-many joins unless absolutely required.
Replace weak joins with proper surrogate keys if available.
If inactive relationships are used, verify with USERELATIONSHIP in DAX.
Document changes clearly and test each visual for expected outputs.
A cleaner model means fewer bugs and easier long-term updates.
It also boosts performance and reduces confusion.

Question 9: A team wants drill-through between unrelated tables. What would you suggest if the model doesn’t support it?

I explain that drill-through works only if there’s a relationship or filter context.
If not possible, I simulate it using bookmarks and navigation buttons.
Sometimes I build bridge tables or flat summary tables for workaround.
I show them alternate ways to explore details using slicers or tooltips.
Educate the team on Power BI’s structural limitations.
Avoid creating forced relationships that break the model logic.
It’s better to adjust the report design than compromise data quality.
Keeps UX clean while staying technically sound.

Question 10: How do you fix unexpected issues in Power BI caused by Row-Level Security (RLS)?

Start by checking if users are part of multiple roles with overlapping rules.
RLS in Power BI uses a union of all assigned roles, not intersection.
Use “View as Role” to test what each user sees in practice.
In one case, a user had both “Manager” and “All Users” roles—data conflicted.
I created exclusive roles and clarified logic with the business owner.
Also added a default “Viewer” role for users who didn’t fit any category.
Documented the logic behind each RLS rule for future clarity.
After that, access became consistent and audit-friendly.

Question 11: How would you handle a situation where a dataset refresh fails regularly due to size and memory limits?

I check refresh logs to identify which table or step consumes the most memory
Look for large unnecessary columns or rows that can be filtered or removed
Consider using incremental refresh to load only changed data
Split big tables into aggregates and detail tables to reduce memory footprint
Migrate some heavy transforms to SQL or Azure dataflows outside Power BI
Test refresh performance after trimming and optimizing
Communicate timeline and reason to stakeholders for any delay
This resolves failures and makes refresh predictable and efficient.

Question 12: If users complain that filters aren’t affecting visuals as expected, how would you diagnose it?

First, check relationships—maybe the filter field isn’t linked to the visual’s data
Investigate if the field used is connected through a one‑to‑many or many‑to‑many join
Review if there are inactive relationships requiring USERELATIONSHIP in DAX
Check if visual-level filters override page or report filters unintentionally
Use “Show as a table” in visuals to inspect row‑level data impacted by filters
Ensure slicers are from lookup tables, not fact tables, for proper behavior
Confirm on test report to isolate where the issue stems from
Fix relationships or filter logic, then retest user scenario for expected behavior.

Question 13: How would you approach a project where stakeholders ask for multiple similar visuals with minor differences?

I ask what the key decision each visual supports to avoid duplication
Often, a dynamic visual with slicers or toggle buttons handles multiple views
Build a single flexible visual instead of 5 nearly identical ones
Use what-if parameters or bookmarks to let users swap view modes easily
Reduces development time and avoids future maintenance headache
It also keeps dashboard performance snappy and clean
Share a prototype to show flexibility before developing full set
Users love simplicity and reduced visual clutter.

Question 14: Stakeholders want both detailed and summary analysis in one report. How do you balance this effectively?

Discuss what granularity users really need on the main page
Use summary KPIs and aggregates at top, with drill‑through to detail pages
Only load detailed data on-demand, not always in the main view
Use collapsible visuals or toggle between Summary and Detail views
Manage model size by importing summaries and linking detail via dataflows
This reduces complexity on the main dashboard surface
Satisfies different user needs without slowing everything down
Makes navigation intuitive and fast.

Question 15: Imagine your organization wants to centralize multiple Power BI reports. What would you suggest?

Propose building a shared semantic layer (approved dataset with defined KPIs)
Move report logic out of individual pbix files into that centralized dataset
Certified datasets ensure consistency, governance, and single version of truth
Use workspace and permission model so only dataset owners can change it
Reports become lean, consuming the central dataset, not owning data logic
Enables update once and reflect across all reports instantly
Reduces redundancy and upgrade work when logic changes
Builds trust and reduces confusion across teams.

Question 16: You observe high memory usage in your Power BI model despite optimization. What real‑world solution would you try?

Check cardinality of columns—high unique values inflate columnstore size
Remove unnecessary columns or split out high-cardinality fields to separate tables
Convert text fields to numeric keys where possible to reduce storage
Implement calculated tables sparingly—do heavy logic outside Power BI
Use aggregations and import only what’s needed for core visuals
Switch off Auto Detect Relationships if unnecessary to reduce metadata overhead
Refresh model and measure memory impact after each change
Iterative tuning helps control memory footprint reliably.

Question 17: A client requests data lineage tracking for audit purposes. How would you deliver that within Power BI constraints?

Power BI Desktop doesn’t natively show full lineage—so I use external tools
Use Power BI lineage view in Service combined with documentation tracking
For deeper coverage, export documentation using Tabular Editor or Power BI APIs
Keep a separate metadata repository like an internal spreadsheet or Power BI dataflow
Include source, transformation, model, and report layer lineage
Share this documentation transparently with audit or compliance teams
Update it any time a dataflow or dataset changes
This fills the gap since built‑in lineage is limited.

Question 18: During a project review, you find inconsistent calculations across reports. How would you address this conceptually?

I audit key measures across reports to highlight mismatched logic
Identify where teams developed similar KPIs independently
Propose consolidating those into shared measures in a certified dataset
Train report developers to reference central KPIs instead of custom DAX
Document the definition of each KPI so everyone agrees on semantics
Set up governance to control changes to core metrics going forward
Version control helps track changes and maintain consistency
Ultimately brings alignment and trust to reported numbers.

Question 19: How would you handle requests for complex custom visual behaviors beyond Power BI limits?

For behaviors unsupported in Power BI, I examine custom visuals or scripting options
Assess cost of third-party visuals or embedding with Power BI Embedded
Where custom code is needed, evaluate external applications or paginated reports
Educate stakeholders on trade-offs: maintenance, support, refresh frequency
Sometimes restructuring visuals or simplifying layout is enough
Propose alternative interfaces like Power Apps integrated in the report
Always test performance and compatibility in advance
Deliver solution that meets needs without risking stability.

Question 20: You deliver a report but users work in varied time zones and need timezone‑aware metrics. How do you manage this?

I store all timestamps in UTC at ingestion for consistency
Use Power Query or DAX to convert UTC to user-specific local time
Include user‑timezone context, either via user profile or slicer selection
Apply time intelligence functions based on user‑converted local time
Ensure visual summaries and filters respect the timezone offset
Document how conversion logic works for future teams
Test with users from different zones to validate accuracy
This ensures everyone sees correct metrics regardless of location.

Question 21: When dataset refresh time becomes too long during business hours, what real-world improvement would you propose?

I assess refresh schedule and timing to shift heavy loads to off‑peak hours
Introduce incremental refresh to only update changed or new data
Archive historical data that isn’t used in active reports
Push heavy transformations upstream into SQL or Azure dataflows
Use partitioned tables to parallelize refresh operations
Test smaller refresh windows and monitor performance impact
Share refresh logs and expected times with stakeholders
Helps keep live reporting available while maintaining responsiveness.

Question 22: A user complains a calculated measure returns unexpected blank values. How would you troubleshoot?

I inspect DAX logic for potential context filters or missing data
Check if any table relationships are breaking filter propagation
Use DAX functions like ISBLANK or COALESCE to handle nulls explicitly
Verify underlying data for missing values in critical columns
Use “Show as table” to see row-level data evaluation results
Ensure slicers or page filters aren’t filtering out relevant data
Simplify the measure temporarily to debug step by step
After fixing, document the DAX logic and edge-case handling.

Question 23: How would you manage version control for Power BI reports in a team environment?

Although Power BI doesn’t support Git natively, I export PBIX files for storage
Use OneDrive, SharePoint, or Azure DevOps to track PBIX history changes
Encourage developers to use Tabular Editor and external tools for schema versioning
Standardize naming conventions including date or version in file name
Keep change logs: what changed and why, within report documentation
Review versions with peers before deployment to production workspace
Document rollback process in case of errors
Ensures traceability and safe collaboration.

Question 24: Stakeholders ask why some visuals show totals that don’t sum row values correctly. What’s your explanation?

This often results from DAX measures using context evaluation or ALL functions
Totals can differ due to filter context ignoring row-level granularity
I demonstrate how SUMX or CALCULATE changes context for aggregated rows
Use “Show as table” to illustrate the difference between row and total values
Explain how relationships and groupings affect grand totals
Suggest adjusting DAX or switching measure logic for consistent results
Educates business on how Power BI computes totals versus raw sums
Helps manage expectations and improves calculation transparency.

Question 25: A report uses many images and custom visuals, and it’s now sluggish. How would you optimize it?

Evaluate which visuals or images cause slow rendering using Performance Analyzer
Replace heavy custom visuals with simpler native Power BI visuals where possible
Compress images and store them in shared location instead of embedding
Leverage bookmarks rather than multiple report pages to avoid loading overhead
Limit custom visuals usage and review performance logs regularly
Train users on lightweight report design principles
Monitor page load times across desktop and mobile clients
Makes dashboards responsive and more reliable.

Question 26: You’re assigned to build a proof-of-concept (POC) in a tight timeline. What approach would you take?

Focus on core business need and show just enough for decision-making
Use sample or aggregated data to build visuals quickly without full modeling
Avoid over-engineering during POC—skip advanced DAX until confirmed
Use built-in visuals and templates to speed up development
Prepare annotations or tooltips to explain assumptions and mock logic
Gather feedback early to align expectations before final build
Reuse reusable queries or measure patterns where possible
Makes POC fast, flexible, and informative for business review.

Question 27: A user reports inconsistent numbers between Power BI service and Desktop. What might cause this and how do you resolve it?

Differences often arise when Desktop isn’t refreshed with the latest dataset
Service might be using a different parameter or dataflow version
Check if row-level filters or RLS roles differ between environments
Confirm both environments use same data source credentials and query logic
Refresh the model in Desktop and re-publish to align versions
Use “View app as end user” in service to see matching view
Document environment differences and refresh steps
Ensures consistency across Desktop and Service reporting.

Question 28: How would you handle a request to integrate Power BI with other business tools like Teams or SharePoint?

Power BI supports embedding dashboards in Teams tabs or SharePoint pages
I assess user access and permissions across both platforms before embedding
Use secure embed links or publish to web depending on security requirements
Use data alerts and subscriptions to trigger notifications in Teams
Benefit: improves visibility and collaboration in familiar tools
Caveat: verify data refresh schedules and permissions propagate correctly
Also document embed URLs and access logic for audit purposes
This drives adoption by making insights accessible in daily workflow.

Question 29: A dataset has many calculated tables that slow down refresh. How would you optimize it?

Review necessity of calculated tables—often they can be replaced with measures or queries
Move logic into Power Query or upstream data transformations instead
If calculated tables are static, consider converting to imported static tables
Schedule those once and refresh less often to reduce overhead
For dynamic cases, only calculate minimal required data slices
Consider using query folding to push transforms to source databases
After each change, monitor refresh duration and model size
Balances flexibility with refresh performance effectively.

Question 30: You need to present a cost‑benefit analysis comparing Import vs DirectQuery modes. How do you frame that to stakeholders?

Present Import mode benefits: fast query response, full DAX, offline model access
Explain DirectQuery: live data but slower performance and limited DAX capabilities
Use metrics: average refresh time, query response time, and user satisfaction
Highlight impact on database workload and cost if many users hit the source live
Show hybrid option: import aggregates with DirectQuery for detail data
Frame decision in terms of accuracy needs vs performance vs business criticality
Use examples from past proyectos showing speed vs. flexibility trade-offs
Helps stakeholders choose wisely based on business priorities.

Question 31: How would you troubleshoot a Power BI report that’s showing outdated data even after a refresh?

Verify the data source connection and credentials are valid and updated
Confirm if refresh succeeded in the Power BI Service refresh history
Check if reports rely on cached visuals or import from stale dataflows
Review gateway logs if using on-premises data sources for errors
Open Desktop and compare previewed data vs published report data
Clear dataset cache or rebuild dataset if needed
Communicate refresh schedule and expected data lag to users
Ensures reports always reflect live and accurate data.

Question 32: Users want predictive insights but with limited technical budget. How can Power BI deliver value?

Use built-in AI visuals like Forecast, Decomposition Tree, Key Influencers
These require minimal setup and can show pattern-driven insights quickly
Leverage Power BI Premium Per User if available for cognitive services
Combine with basic statistical analysis in Power Query (like moving average)
Communicate caveats: they’re not true ML models but useful trend signals
This approach costs less and delivers initial predictive view to stakeholders
Users see immediate ROI without heavy infrastructure
Later, you can enhance with advanced ML if needed.

Question 33: In a shared dataset environment, one report update affects others. How would you control unintended breakage?

Implement versioning and change approval before updating shared datasets
Create dev/test workspaces to validate changes before deploying to production
Use deployment pipelines in Power BI Premium or workspace backup process
Notify report owners beforehand about upcoming logic or schema changes
Maintain documentation of measure definitions to avoid surprises
Use dataset endorsement/certification to control trusted version changes
Rollback quickly if errors occur using older PBIX versions or backups
Keeps consumption reports stable and reliable.

Question 34: How would you ensure your Power BI model supports multiple languages or locales?

Store all displayed labels and measures in a translation table by locale
Use DAX SWITCH or user locale logic to present the correct language
Manage date and number formats according to user region settings
Detect user locale in Service or use a slicer to select language preference
Document translation logic and update process for future languages
Ensure slicers and visuals adapt when locale context changes
Test scenarios in different user regions to validate display accuracy
Helps global teams view data in their preferred language and format.

Question 35: A report downloads slowly due to huge data export options. How do you optimize exporting?

Limit exported rows to necessary detail, not full dataset by default
Provide summarized tables or paginated report formats for large exports
Use RLS to restrict data volume per user when exporting
Explain to users export limits and better use of report visuals instead
Where needed, generate data via Azure dataflows or dedicated ETL jobs
Offer offline summaries or CSV downloads instead of full PBIX exports
Document export best practices and constraints clearly
Reduces wait times and improves user satisfaction.

Question 36: You inherit a dataset with inconsistent date handling across visuals. How do you standardize it?

Create a single official date/calendar table with all required calendar columns
Enforce all visuals to link through that central date table
Use consistent time intelligence functions across measures like SAMEPERIODLASTYEAR
Remove ad-hoc date logic embedded in visuals or queries
Educate developers on always using the certified date table
Validate cross-report consistency through testing and sample visuals
A unified calendar improves reliability of trend and period comparisons
Users can trust that dates always align across dashboards.

Question 37: Business users complain the report layout is confusing. How would you propose improvements?

Conduct a walkthrough with users to identify pain points and navigation issues
Reorganize visuals by priority and reduce cognitive overload on pages
Apply consistent page structure, fonts, and visual spacing
Add tooltips or info icons to explain complex metrics or charts
Use bookmarks or buttons for guided storytelling through report pages
Provide a dashboard legend or key to explain color and layout logic
Roll changes back with a pilot group before full release
Improves usability and encourages deeper adoption.

Question 38: You need to implement row-level security for users across multiple org units. How would you manage it at scale?

Create a mapping table of users to each org unit or department in data model
Use DAX USERPRINCIPALNAME() to filter data based on user mapping
Automate mapping updates via Azure AD groups or authorized sync feeds
Test role logic with “View as role” for representative user accounts
Maintain a security log and document each role’s filter logic
Review access periodically to adjust for org changes or promotions
Keep fallback groups to avoid accidental data blackout
Ensures scalable, secure, and traceable access control.

Question 39: Stakeholders request very high-fidelity visual styles that Power BI doesn’t support natively. How do you deliver?

Identify core business need: visual fidelity or functionality?
Explore third-party certified visuals from AppSource cautiously
If that’s insufficient, consider exporting visuals to PowerPoint or HTML
Or use Power BI embedded with custom visuals developed externally
Explain longer maintenance and update challenges with custom visuals
Keep default visuals clean and fallback where necessary
Provide mock-ups to get early stakeholder feedback
Ensures design excellence without risking report stability.

Question 40: A client wants a dashboard that adapts based on user role, not just filters. How do you architect that?

I use RLS to control which data each user can access by role
Combine RLS with dynamic DAX measures that change behavior per user role
Use USERNAME() or USERPRINCIPALNAME() to personalize visuals or metrics
Sometimes build alternative navigation or home pages per user role via bookmarks
Use switch measures to show different KPIs where appropriate
Document mapping logic for roles and visual behavior clearly
Test variability with sample accounts before deployment
Dynamic adaptation increases relevance and user satisfaction.

Question 41: How would you approach explaining Power BI’s value to non‑technical executives during an interview?

Start by defining business intelligence: turning raw data into insights for decisions
Explain how Power BI connects diverse data sources for unified view
Show examples of dashboards illustrating KPIs and alerts driving action
Emphasize ease of self-service and drill-down for non-technical users
Describe how adoption increases ROI and decision speed
Mention governance and security control as enabler of trust

Question 42: If asked to model multiple fact tables in Power BI, what would you say and why would it be important?

I explain fact tables represent different event types like sales, inventory, or returns
Highlight that multiple facts avoid overloading a single table with unrelated metrics
Use shared dimension tables to maintain coherence across different facts
Emphasize performance and clarity benefits in reporting
This modeling approach makes filtering and analytics more accurate
It also mirrors real-world business process separation

Question 43: How do you explain the difference between SUM and SUMX without coding?

I’d say SUM simply adds up a column of values directly
Whereas SUMX evaluates row-by-row expression before summing result
Example: calculating revenue per row before summing instead of summing raw units
SUMX handles calculated logic; SUM is simpler and faster
Choose based on complexity of calculation needed
Shows understanding of context transition and evaluation

Question 44: Describe how you’d guide a non-technical stakeholder through your dashboard development process.

I’d start with intake to understand their business questions clearly
Do data quality checks and validation early in process
Design draft visuals and walk them through initial mock-ups
Iterate based on feedback until they approve layout and logic
Explain when the project is “done” by defined requirements
This ensures alignment and trust with non-technical stakeholders

Question 45: What’s a typical DAX pitfall when using CALCULATE or REMOVEFILTERS, and how would you explain its impact?

I’d mention that using REMOVEFILTERS inside CALCULATE can break filter context unexpectedly
This could cause totals to ignore slicers or page filters
A real-time issue: managers thought they saw filtered data but got full data
I’d test using “Show as table” to illustrate row vs total differences
Then adjust logic or use ALL or ALLEXCEPT carefully
Helps explain query context and calculation controls in human terms

Question 46: How would you resolve a scenario where a dashboard test task fails due to missing logic like toggling between metrics?

I’d clarify requirements such as switching between sales and unit quantity
Use a dynamic measure controlled by a parameter or slicer
Demonstrate toggle button using DAX SWITCH or what-if parameter
Explain this approach avoids multiple visuals and simplifies UX
In real interviews, candidates usually miss requirement specificity
Adds credibility by showing awareness of dynamic reporting needs

Question 47: If asked what data source is toughest to handle in Power BI and how you’d overcome it, what would you say?

I’d cite sources like nested JSON or unstructured web APIs as tough.
Parsing messy data requires advanced Power Query transformations
Use query folding where possible to push transformations upstream
Clean data externally in SQL or ETL if performance is impacted
Document transformation logic so users understand any limitations
Helps show problem-solving with real messy enterprise data

Question 48: How would you handle scenario-based visual critique in an interview?

I’d expect to be shown a poor dashboard and asked to suggest improvements
I’d point out clutter, unclear KPIs, inefficient visuals like pie charts
Recommend cleaner layouts, prioritized key metrics, removal of irrelevant items
Emphasize storytelling and user workflow through bookmarks/buttons
Highlight consistency in colors, fonts, alignment for usability
Explains ability to discuss design and user experience credibly

Question 49: How would you communicate the lifecycle of a Power BI dashboard development project?

Explain phases: intake → data prep → model design → visual layout → feedback loops (Reddit)
Describe iterations with stakeholders until first working version
Include data quality validation and testing before deployment
Detail deployment process: publish, review, manage refresh and permissions
Emphasize iteration, review, and knowing when to consider the work done
Shows maturity in process and people-facing skills

Question 50: How would you present value of data modeling (star schema) in an interview without coding?

I describe star schema as central fact joined to lookup dimensions (Reddit, Reddit)
Explain it improves performance and makes filtering intuitive
Avoids many‑to‑many relationships which can cause confusion
Enables clearer relationships for DAX and slicer logic
Simplifies troubleshooting and enhances scalability
Stakeholders appreciate cleaner, faster reporting

Question 51: If you needed to calculate contribution to parent total (like subcategory % of category), how would you explain it conceptually?

I’d explain dividing subcategory sales by parent category sales
Use REMOVEFILTERS on subcategory to get the entire category total (Reddit, Reddit)
Clarify filter context: subcategory filter stays for numerator only
This shows relative performance clearly in visuals
Use simple real-world example: one product’s share of overall sales
Helps interviewer see understanding of DAX context and percentage logic

Question 52: How do you explain calculating active customers over last few months in an interview?

Define what “active” means: made purchase in past X months
Use DATESINPERIOD in DAX to shift timeframe relative to last date (Reddit, Reddit)
Then count distinct customers within that period
This dynamic method updates as data grows without manual date changes
Illustrate with business scenario: monthly loyalty tracking
Demonstrates ability to handle time-based analytics

Question 53: What real-life lesson did you learn managing SCD (Slowly Changing Dimensions) in Power BI ETL?

I explain how ignoring SCD led to wrong historical metrics
Use methods like Type 2 tracking via surrogate keys in dataflow or ETL
Capture changes history instead of overwriting dimension values
Ensures past reports remain accurate even after business changes
Share validation strategy and versioning for dimension data
Shows understanding of data governance and accuracy needs

Question 54: Describe a project where you needed to manage role-based navigation and content in dashboards.

Implemented different startup pages based on user roles via bookmarks
Combined with dynamic measures to show relevant KPIs per role
Showed that RLS filters data and bookmarks control navigation
Ensured non-technical users only saw what applied to their function
Tested with sample accounts to validate experience
In interviews, emphasizing user-focused design stands out professionally

Question 55: How would you approach a stakeholder wanting drill-through across disconnected tables?

Explain drill-through requires valid relationships or filter context
If impossible, suggest bridge tables or flattened summary tables
Propose alternate navigation using bookmarks or tooltips
Avoid creating forced or invalid joins that break model logic
Provide design options with pros/cons transparently
Helps stay user-centric without compromising data integrity

Question 56: In an interview, if asked how many report iterations you expect, what would you say?

I’d say initial draft is just version one—expect multiple feedback cycles (Reddit, Reddit, Reddit, Reddit)
Each iteration refines visuals, data logic, and usability
Ask stakeholders for change process and sign-off criteria
Set expectations that final version emerges after testing and user review
This shows understanding that iteration is key to delivering value
Demonstrates professionalism and communication skills

Question 57: If asked about lessons learned customizing visuals vs using built-in ones, what scenarios do you highlight?

Custom visuals can look better but often slow report performance
Built-in visuals offer native support and faster rendering
Use third-party visuals only if business need justifies it
Always test cross-platform compatibility before implementing
Document fallback options if visual breaks in future updates
Decisions guided by balance of style vs stability

Question 58: How would you explain the impact of using high-cardinality columns in the Power BI model?

High-cardinality columns inflate memory and slow storage engine
Cardinality means number of unique values per column
I reduce cardinality by splitting dimensions or converting text to numeric keys
Use grouping or hashing when appropriate to limit uniqueness
Shows real-world tuning for performance and refresh reliability
Interviewers appreciate performance-aware design thinking

Question 59: Scenario: Many people publish their own PBIX files. How do you prevent version chaos?

I’d centralize logic in certified shared datasets or dataflows
Limit editing rights so only owners control master dataset
Educate teams to build reports from shared sources, not copies
Use workspace permissions and endorsement to enforce governance
Maintain change logs and version snapshots if rollback needed
Encourages consistency and reliable report quality

Question 60: If you had to choose between a fully import model versus DirectQuery for a 100M-row dataset, how would you evaluate it?

Assess performance: import is fast but requires memory and refresh time
DirectQuery avoids memory but slower for large queries and filters
Consider need for near real-time data access and DAX capabilities
Hybrid model often best: import aggregates, DirectQuery for detail
Factor in backend load and licensing costs for Premium capacity
Frame decision based on business latency tolerance and cost sensitivity

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