Power BI Engineer (Power BI, DAX, Power Query) - Interview Q&A Guide
Basic Questions (5)
1. What is Power
BI and explain its main components?
Answer: Power BI is Microsoft’s business
intelligence platform for data visualization and analytics. Main components:
•
Power BI Desktop: Development environment for creating reports
•
Power BI Service: Cloud-based service for sharing and collaboration
•
Power BI Mobile: Mobile apps for consuming reports
•
Power Query: Data transformation and preparation tool
•
DAX: Data Analysis Expressions language for calculations
•
Power BI Gateway: Connects cloud service to on-premises data
2. What is the
difference between Power BI Desktop, Pro, and Premium?
Answer:
•
Power BI Desktop: Free tool for creating reports and models
•
Power BI Pro: $10/user/month, includes sharing, collaboration, and 10GB storage
•
Power BI Premium: Capacity-based licensing, advanced features, larger datasets,
paginated reports, and AI capabilities
3. Explain the
difference between Import, DirectQuery, and Live Connection modes.
Answer:
•
Import: Data copied to Power BI dataset, fastest performance, limited by
dataset size
•
DirectQuery: Queries sent to source in real-time, always current data, slower
performance
•
Live Connection: Direct connection to Analysis Services or Power BI datasets,
real-time data
4. What is DAX
and how does it differ from SQL?
Answer: DAX (Data Analysis Expressions)
is a formula language for Power BI, Excel, and Analysis Services. Unlike SQL:
•
DAX works with column-oriented
data models
•
Has filter context and row
context concepts
•
Designed for analytical
calculations and aggregations
•
Uses relationships between
tables automatically
5. What is Power
Query and its primary purpose?
Answer: Power Query is a data
transformation engine that:
•
Connects to various data
sources
•
Cleans and transforms data
before loading
•
Uses M language for
transformations
•
Provides a visual interface for
data preparation
•
Supports data profiling and
quality assessment
Intermediate Questions (20)
6. Explain
filter context and row context in DAX.
Answer:
•
Filter Context: Defines which rows are visible for calculation, influenced by
slicers, filters, and visual filters
•
Row Context: Iteration through table rows, occurs in calculated columns or
iterator functions
// Filter context
example
Total Sales =
SUM(Sales[Amount]) // Affected by
filters
// Row context
example
Profit Margin =
Sales[Revenue] - Sales[Cost] //
Calculated column
7. How do you
create and optimize relationships between tables in Power BI?
Answer:
1.
Relationship types: One-to-many (most common), many-to-many, one-to-one
2.
Cardinality: Choose correct cardinality for performance
3.
Cross-filter direction: Single or bi-directional
4.
Active vs inactive: Only one active relationship between tables
5.
Optimization: Use integer keys, avoid bi-directional when possible
8. What are
calculated columns vs measures in DAX?
Answer:
•
Calculated Columns: Computed at refresh time, stored in model, use row context
•
Measures: Computed at query time, not stored, use filter context
// Calculated
Column
Full Name =
Customers[First Name] & " " & Customers[Last Name]
// Measure
Total Revenue =
SUM(Sales[Amount])
9. How do you
handle slowly changing dimensions in Power BI?
Answer:
1.
Type 1 (Overwrite): Simply update the existing record
2.
Type 2 (Add new record): Create new record with effective dates
3.
Type 3 (Add new column): Add columns for current and previous values
4.
Power Query: Use merge queries to handle SCD logic
5.
Dataflows: Implement SCD in Power BI dataflows
10. Explain the
CALCULATE function and its importance in DAX.
Answer: CALCULATE modifies filter
context and is essential for complex calculations:
// Basic syntax
CALCULATE(<expression>,
<filter1>, <filter2>, ...)
// Examples
Sales Last Year =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR(Dates[Date]))
High Value Sales
= CALCULATE([Total Sales], Sales[Amount] > 1000)
11. How do you
implement security in Power BI?
Answer:
1.
Row-level Security (RLS): Filter data based on user identity
2.
Object-level Security: Control access to specific objects
3.
Dataset permissions: Manage who can view/edit datasets
4.
Workspace roles: Admin, Member, Contributor, Viewer
5.
App permissions: Control app distribution and access
// RLS filter
example
[Territory] =
USERNAME()
12. What are
Power Query M functions and how do you use them?
Answer: M is the functional language
behind Power Query:
// Basic M function
let
Source = Excel.Workbook(File.Contents("C:\data.xlsx")),
Sheet1 = Source{[Item="Sheet1",Kind="Sheet"]}[Data],
CleanData = Table.RemoveBlankRows(Sheet1)
in
CleanData
13. How do you
optimize Power BI report performance?
Answer:
1.
Data model optimization: Use appropriate data types, remove unnecessary columns
2.
DAX optimization: Use variables, avoid complex calculations in visuals
3.
Visual optimization: Limit number of visuals per page
4.
Import vs DirectQuery: Choose appropriate storage mode
5.
Aggregations: Use aggregation tables for large datasets
6.
Query reduction: Use performance analyzer
14. Explain time
intelligence functions in DAX.
Answer: Time intelligence functions work
with date tables:
// Year-to-date
YTD Sales =
TOTALYTD([Total Sales], Dates[Date])
// Previous
period
Previous Month =
CALCULATE([Total Sales], PREVIOUSMONTH(Dates[Date]))
// Same period
last year
SPLY Sales =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR(Dates[Date]))
15. How do you
handle data refresh and scheduling in Power BI?
Answer:
1.
Personal Gateway: For personal data sources
2.
On-premises Gateway: For organizational data sources
3.
Scheduled refresh: Configure automatic refresh schedules
4.
Incremental refresh: Refresh only new/changed data
5.
Dataflows: Centralize data preparation and refresh
6.
API refresh: Programmatic refresh using REST API
16. What are
Power BI dataflows and when would you use them?
Answer: Dataflows are reusable data
preparation components:
•
Centralized ETL: Common data transformations
•
Self-service BI: Business users can create data sources
•
Performance: Reduce load on source systems
•
Governance: Centralized data management
•
Reusability: Share common entities across datasets
17. How do you
implement drill-through and drill-down functionality?
Answer:
•
Drill-down: Navigate hierarchy levels (Year → Quarter → Month)
•
Drill-through: Navigate to detailed page with filtered context
•
Bookmarks: Save specific report states
•
Buttons: Custom navigation between pages
•
URL actions: Link to external resources
18. Explain the
concept of star schema and its benefits in Power BI.
Answer: Star schema consists of:
•
Fact tables: Contain measures and foreign keys
•
Dimension tables: Contain descriptive attributes
•
Benefits: Optimized for analysis, better performance, easier DAX
calculations
•
Relationships: Dimension tables connect to fact tables
19. How do you
handle many-to-many relationships in Power BI?
Answer:
1.
Bridge tables: Create intermediate table to resolve relationship
2.
Many-to-many cardinality: Use when appropriate (Power BI 2019+)
3.
CROSSFILTER function: Control filter direction in DAX
4.
Bidirectional filtering: Enable when necessary
// Many-to-many
measure
Sales by Category
=
CALCULATE(
[Total Sales],
USERELATIONSHIP(Products[ProductID],
Bridge[ProductID])
)
20. What are
composite models and when would you use them?
Answer: Composite models combine Import
and DirectQuery storage modes:
•
Benefits: Real-time data with cached aggregations
•
Use cases: Large datasets needing real-time updates
•
Dual storage mode: Tables can use either mode as needed
•
Aggregations: Pre-calculated summaries for performance
21. How do you
implement advanced DAX patterns like SUMX and CALCULATE?
Answer:
// SUMX -
Iterator function
Total Profit =
SUMX(Sales, Sales[Quantity] * Sales[Unit Price])
// CALCULATE with
complex filters
Filtered Sales =
CALCULATE(
[Total Sales],
Products[Category] =
"Electronics",
Dates[Year] = 2023,
Customers[Region] <>
"Internal"
)
22. Explain
Power BI’s data modeling best practices.
Answer:
1.
Use star schema design
2.
Create proper date tables
3.
Remove unnecessary columns
4.
Use appropriate data types
5.
Implement proper
relationships
6.
Use measures instead of
calculated columns
7.
Optimize for performance
8.
Document the model
23. How do you
handle error handling in Power Query?
Answer:
// Error handling in M
let
Source = try Excel.Workbook(File.Contents("data.xlsx"))
otherwise null,
Result = if Source = null
then #table({"Error"}, {{"File not
found"}})
else Source
in
Result
24. What are the
different ways to publish and share Power BI content?
Answer:
1.
Workspaces: Collaborate with team members
2.
Apps: Distribute content to broader audience
3.
Public web: Share publicly (limited features)
4.
SharePoint Online: Embed in SharePoint pages
5.
Teams integration: Share in Microsoft Teams
6.
Email subscriptions: Automated report delivery
7.
Export options: PDF, PowerPoint, Excel
25. How do you
implement custom visuals and formatting in Power BI?
Answer:
1.
Custom visuals: Download from AppSource or create custom
2.
Themes: Apply consistent formatting across reports
3.
Conditional formatting: Dynamic formatting based on values
4.
Custom formatting: Format specific visual elements
5.
Bookmarks: Save and apply formatting states
6.
Templates: Create reusable report templates
Complex Questions (20)
26. Design a
complex data model for a retail organization with multiple business units and
currencies.
Answer: Complex retail data model
design:
1.
Fact Tables: Sales, Inventory, Returns, Promotions
2.
Dimension Tables: Products, Customers, Stores, Time, Geography
3.
Currency Handling: Currency dimension with exchange rates
4.
Business Units: Separate fact tables or additional dimensions
5.
Hierarchies: Product categories, geographic hierarchies
6.
Slowly Changing Dimensions: Customer address changes
7.
Relationships: Proper cardinality and filter directions
8.
Performance: Aggregation tables for large datasets
27. Implement a
sophisticated time intelligence solution with fiscal calendar and multiple date
contexts.
Answer: Advanced time intelligence:
// Fiscal year
calculation
Fiscal Year =
VAR
FiscalMonthStart = 7 // July start
RETURN
IF(
MONTH(Dates[Date]) >= FiscalMonthStart,
YEAR(Dates[Date]) + 1,
YEAR(Dates[Date])
)
// Multiple date
context measure
Sales by Ship
Date vs Order Date =
CALCULATE(
[Total Sales],
USERELATIONSHIP(Sales[ShipDate],
Dates[Date])
)
28. How do you
implement complex security models with dynamic row-level security?
Answer: Dynamic RLS implementation:
1.
Security tables: User-permission mapping tables
2.
Dynamic filters: DAX expressions using LOOKUPVALUE
3.
Multiple security dimensions: Geography, product, time-based
4.
Inheritance models: Hierarchical security structures
5.
Testing framework: Validate security implementations
// Dynamic RLS
filter
Territory Filter
=
VAR CurrentUser =
USERPRINCIPALNAME()
VAR
UserTerritories =
CALCULATETABLE(
VALUES(UserSecurity[Territory]),
UserSecurity[UserEmail] = CurrentUser
)
RETURN
[Territory] IN UserTerritories
29. Design a
real-time analytics dashboard with DirectQuery optimization.
Answer: Real-time dashboard
optimization:
1.
Data source optimization: Optimize database for DirectQuery
2.
Model design: Minimize table count and relationships
3.
DAX optimization: Simple measures and avoid complex calculations
4.
Visual optimization: Limit visuals and data points
5.
Caching strategy: Use aggregations where possible
6.
Performance monitoring: Track query performance
7.
User education: Train users on performance implications
30. Implement a
comprehensive data governance framework in Power BI.
Answer: Data governance framework:
1.
Certification process: Validate and certify datasets
2.
Endorsement system: Promote trusted content
3.
Lineage tracking: Data source to report lineage
4.
Impact analysis: Understand downstream dependencies
5.
Sensitivity labels: Classify and protect sensitive data
6.
Usage monitoring: Track content usage and adoption
7.
Documentation standards: Consistent metadata and descriptions
31. How do you
handle complex calculations across multiple fact tables?
Answer: Multi-fact table
calculations:
// Cross-fact
table calculation
Budget vs Actual
=
VAR ActualSales =
SUM(Sales[Amount])
VAR BudgetAmount
=
CALCULATE(
SUM(Budget[Amount]),
TREATAS(VALUES(Sales[Date]),
Budget[Date]),
TREATAS(VALUES(Sales[ProductID]),
Budget[ProductID])
)
RETURN
ActualSales - BudgetAmount
32. Design a
machine learning integration with Power BI for predictive analytics.
Answer: ML integration approach:
1.
Azure ML integration: Connect to Azure Machine Learning models
2.
Cognitive Services: Built-in AI capabilities (sentiment, key phrases)
3.
Python/R integration: Custom ML models in Power BI
4.
Automated ML: Use AutoML for simple predictions
5.
Real-time scoring: Score data in real-time
6.
Model management: Version control and deployment
7.
Visualization: Present ML results effectively
33. Implement a
complex budgeting and forecasting solution in Power BI.
Answer: Budgeting solution design:
1.
What-if parameters: Interactive scenario modeling
2.
Variance analysis: Budget vs actual comparisons
3.
Forecasting models: Trend and seasonal forecasting
4.
Rolling forecasts: Dynamic forecast periods
5.
Version control: Track budget versions over time
6.
Approval workflows: Power Platform integration
7.
Drill-through analysis: Detailed variance explanations
34. How do you
implement advanced data quality monitoring in Power BI?
Answer: Data quality framework:
1.
Data profiling: Automated data quality checks
2.
Quality metrics: Completeness, accuracy, consistency measures
3.
Exception reporting: Highlight data quality issues
4.
Trend analysis: Monitor quality over time
5.
Alerting system: Notify stakeholders of quality issues
6.
Root cause analysis: Track quality to source systems
7.
Continuous monitoring: Real-time quality assessment
35. Design a
multi-tenant Power BI solution for a SaaS platform.
Answer: Multi-tenant architecture:
1.
Tenant isolation: Separate workspaces per tenant
2.
Shared datasets: Common data model with tenant filtering
3.
Dynamic security: Tenant-aware row-level security
4.
Automated provisioning: Programmatic workspace creation
5.
Performance isolation: Dedicated capacity planning
6.
Customization: Tenant-specific branding and features
7.
Cost allocation: Track usage per tenant
36. Implement a
sophisticated alerting and notification system.
Answer: Advanced alerting system:
1.
Data alerts: Threshold-based notifications
2.
Power Automate integration: Custom notification workflows
3.
Conditional logic: Complex alert conditions
4.
Escalation procedures: Multi-level alert routing
5.
Performance alerts: System performance monitoring
6.
Mobile notifications: Push notifications to mobile apps
7.
Alert management: Snooze, acknowledge, and track alerts
37. How do you
handle complex data transformations in Power Query?
Answer: Advanced Power Query
transformations:
// Custom function for data transformation
CleanAndTransform = (table as table) as table =>
let
CleanedData = Table.TransformColumns(table,
{{"Amount", each if _ < 0 then 0 else _}}),
AddedColumns = Table.AddColumn(CleanedData,
"Category", each if [Amount] > 1000 then "High" else "Low")
in
AddedColumns
38. Design a
comprehensive performance monitoring solution for Power BI.
Answer: Performance monitoring
framework:
1.
Query performance: Track query execution times
2.
Dataset refresh: Monitor refresh success and duration
3.
User activity: Track report usage and adoption
4.
Capacity monitoring: Monitor Premium capacity utilization
5.
Error tracking: Log and analyze error patterns
6.
Performance trends: Historical performance analysis
7.
Optimization recommendations: Automated performance suggestions
39. Implement a
complex financial consolidation model in Power BI.
Answer: Financial consolidation
design:
1.
Entity hierarchy: Parent-child relationships
2.
Elimination entries: Intercompany eliminations
3.
Currency conversion: Multi-currency consolidation
4.
Ownership percentages: Minority interest calculations
5.
Consolidation rules: Complex business rules
6.
Audit trails: Track all consolidation adjustments
7.
Regulatory reporting: Meet financial reporting standards
40. How do you
integrate Power BI with other Microsoft ecosystem tools?
Answer: Microsoft ecosystem
integration:
1.
Excel integration: Analyze in Excel, Excel data sources
2.
SharePoint: Embed reports, list data sources
3.
Teams: Embed reports and dashboards
4.
Power Platform: Power Apps and Power Automate integration
5.
Azure services: Azure SQL, Analysis Services, Data Factory
6.
Office 365: Outlook, OneNote integration
7.
Dynamics 365: CRM and ERP data integration
41. Design a
data lake analytics solution using Power BI and Azure.
Answer: Data lake analytics
architecture:
1.
Azure Data Lake: Store raw and processed data
2.
Azure Synapse: Data transformation and processing
3.
Power BI DirectQuery: Connect to Synapse pools
4.
Data modeling: Optimize for analytics workloads
5.
Performance tuning: Partition and index strategies
6.
Security: End-to-end data security
7.
Cost optimization: Balance performance and cost
42. Implement a
comprehensive metadata management solution.
Answer: Metadata management
framework:
1.
Data dictionary: Centralized metadata repository
2.
Lineage tracking: End-to-end data lineage
3.
Impact analysis: Change impact assessment
4.
Documentation standards: Consistent metadata practices
5.
Automated discovery: Scan and catalog data assets
6.
Business glossary: Business term definitions
7.
Governance workflows: Metadata approval processes
43. How do you
handle complex hierarchical data in Power BI?
Answer: Hierarchical data handling:
// Parent-child
hierarchy
PATH_Function =
PATH(Employees[EmployeeID], Employees[ManagerID])
// Hierarchy
aggregation
Total_Team_Sales
=
SUMX(
PATHCONTAINS(Employees[Path],
Employees[EmployeeID]),
[Individual_Sales]
)
44. Design a
disaster recovery and business continuity solution for Power BI.
Answer: DR/BC solution:
1.
Backup strategies: Export and backup Power BI content
2.
Version control: Git integration for source control
3.
Documentation: Comprehensive system documentation
4.
Recovery procedures: Step-by-step recovery processes
5.
Testing: Regular DR testing and validation
6.
Alternative access: Mobile and web accessibility
7.
Communication plans: Incident communication procedures
45. Implement a
sophisticated customer analytics solution.
Answer: Customer analytics framework:
1.
Customer 360: Unified customer view
2.
Behavioral analytics: Customer journey mapping
3.
Segmentation: Dynamic customer segmentation
4.
Predictive analytics: Churn and lifetime value prediction
5.
Real-time updates: Near real-time customer data
6.
Personalization: Tailored analytics per user segment
7.
Performance tracking: Customer metric monitoring
Project-Based Real
World Questions (5)
46. You’re
tasked with building a complete financial reporting solution for a
multinational corporation. Walk through your approach.
Answer: Financial reporting solution:
1.
Requirements analysis: Understand reporting needs and regulations
2.
Data architecture: Design consolidated data model
3.
Source integration: Connect to ERP, GL, and subsidiary systems
4.
Currency handling: Multi-currency conversion and reporting
5.
Consolidation logic: Implement complex consolidation rules
6.
Report development: P&L, Balance Sheet, Cash Flow statements
7.
Security implementation: Role-based access to financial data
8.
Audit trails: Comprehensive logging and documentation
9.
Performance optimization: Ensure fast report generation
10.
Training and adoption: Finance team training and support
47. Design and
implement a real-time operations dashboard for a manufacturing company.
Answer: Manufacturing operations
dashboard:
1.
Real-time data integration: Connect to SCADA, MES, and ERP systems
2.
KPI framework: OEE, quality metrics, production targets
3.
Alert system: Real-time alerts for production issues
4.
Trend analysis: Historical performance and forecasting
5.
Drill-down capabilities: From plant to line to machine level
6.
Mobile accessibility: Shop floor mobile dashboards
7.
Performance optimization: Handle high-frequency data updates
8.
Integration: Connect with maintenance and quality systems
9.
User training: Operations team dashboard training
10.
Continuous improvement: Iterative enhancement based on feedback
48. Build a
comprehensive customer analytics platform for an e-commerce company.
Answer: E-commerce customer
analytics:
1.
Data integration: Website, mobile app, CRM, and transaction data
2.
Customer 360: Unified customer profile and journey
3.
Behavioral analytics: Purchase patterns and preferences
4.
Segmentation: Dynamic customer segmentation and targeting
5.
Predictive analytics: Churn prediction and recommendation engine
6.
Real-time personalization: Dynamic content and pricing
7.
Performance tracking: Customer acquisition and retention metrics
8.
A/B testing: Experiment tracking and analysis
9.
Mobile analytics: Mobile app usage and performance
10.
Privacy compliance: GDPR and data privacy controls
49. Create a
supply chain analytics solution for a global logistics company.
Answer: Supply chain analytics
platform:
1.
End-to-end visibility: Track goods from origin to destination
2.
Real-time tracking: GPS and IoT sensor data integration
3.
Performance metrics: On-time delivery, cost per shipment
4.
Predictive analytics: Demand forecasting and route optimization
5.
Risk management: Supply chain risk assessment and mitigation
6.
Supplier analytics: Supplier performance and scorecards
7.
Cost optimization: Transportation and inventory cost analysis
8.
Compliance reporting: Regulatory and sustainability reporting
9.
Mobile access: Field team mobile dashboards
10.
Integration: ERP, WMS, and TMS system integration
50. Design a
complete healthcare analytics solution for a hospital network.
Answer: Healthcare analytics
platform:
1.
Clinical analytics: Patient outcomes and quality metrics
2.
Operational efficiency: Bed utilization and staff scheduling
3.
Financial performance: Revenue cycle and cost management
4.
Population health: Community health trends and outcomes
5.
Regulatory compliance: HIPAA compliance and reporting
6.
Real-time monitoring: Patient flow and emergency metrics
7.
Predictive analytics: Readmission risk and resource planning
8.
Quality improvement: Clinical pathway optimization
9.
Patient satisfaction: Survey analysis and improvement tracking
10.
Data security: Comprehensive security and audit controls
Optimization Questions (5)
51. A Power BI
report with 50+ visuals is performing poorly. How do you optimize it?
Answer: Report performance
optimization:
1.
Visual reduction: Consolidate or remove unnecessary visuals
2.
Page optimization: Spread visuals across multiple pages
3.
Data model: Optimize relationships and data types
4.
DAX optimization: Simplify measures and calculations
5.
Import optimization: Use import mode instead of DirectQuery
6.
Aggregations: Create aggregation tables for large datasets
7.
Performance analyzer: Use built-in performance tools
8.
User training: Educate users on performance best practices
52. How do you
optimize Power BI for a dataset with 10+ million rows?
Answer: Large dataset optimization:
1.
Import strategies: Use incremental refresh
2.
Data reduction: Remove unnecessary columns and rows
3.
Aggregation tables: Pre-calculate common aggregations
4.
Composite models: Combine import and DirectQuery
5.
Partitioning: Implement table partitioning
6.
Compression: Optimize data types for better compression
7.
Premium capacity: Use dedicated capacity for large datasets
8.
User education: Train users on large dataset implications
53. Your
DirectQuery reports are too slow. What optimization strategies do you
implement?
Answer: DirectQuery optimization:
1.
Data source optimization: Optimize underlying database
2.
Query folding: Ensure transformations can be pushed to source
3.
Relationship optimization: Minimize complex relationships
4.
DAX simplification: Use simple aggregations
5.
Visual optimization: Reduce data points in visuals
6.
Indexing: Ensure proper database indexing
7.
Aggregations: Use aggregation tables for performance
8.
Monitoring: Track query performance continuously
54. Optimize a
Power BI solution for global users across different time zones.
Answer: Global optimization strategy:
1.
Regional gateways: Deploy gateways closer to data sources
2.
Content distribution: Replicate content across regions
3.
Caching optimization: Leverage query result caching
4.
Time zone handling: Proper UTC and local time handling
5.
Language localization: Multi-language support
6.
Performance monitoring: Track performance by region
7.
Mobile optimization: Optimize for mobile access
8.
User training: Region-specific training and support
55. How do you
optimize Power BI for cost efficiency in a Premium capacity?
Answer: Cost optimization strategy:
1.
Capacity monitoring: Track CPU and memory usage
2.
Workload optimization: Balance workloads across capacity
3.
Refresh scheduling: Optimize refresh schedules
4.
Storage optimization: Manage dataset storage efficiently
5.
User training: Educate users on resource consumption
6.
Performance tuning: Optimize reports and datasets
7.
Scaling strategies: Scale capacity based on demand
8.
Cost monitoring: Track and alert on capacity costs
Cost Saving and
Best Practices Questions (5)
56. What are the
key strategies for minimizing Power BI licensing and infrastructure costs?
Answer: Cost minimization strategies:
1.
License optimization: Right-size Pro vs Premium licensing
2.
Capacity planning: Optimize Premium capacity utilization
3.
Content sharing: Use apps instead of individual sharing
4.
Dataset optimization: Reduce dataset size and complexity
5.
Refresh optimization: Schedule refreshes efficiently
6.
Storage management: Clean up unused content regularly
7.
User training: Educate users on cost implications
8.
Monitoring: Track usage and costs continuously
57. Describe
best practices for Power BI data modeling and report design.
Answer: Best practices framework:
1.
Star schema design: Use dimensional modeling principles
2.
Proper relationships: Implement correct cardinality
3.
Data type optimization: Use appropriate data types
4.
Measure vs calculated
columns: Use measures for aggregations
5.
Naming conventions: Consistent and descriptive naming
6.
Documentation: Comprehensive model documentation
7.
Version control: Implement source control processes
8.
Testing procedures: Validate model changes thoroughly
58. What are the
security best practices for Power BI implementations?
Answer: Security best practices:
1.
Row-level security: Implement appropriate RLS
2.
Workspace security: Use proper workspace roles
3.
Data source security: Secure underlying data sources
4.
Sensitivity labels: Classify and protect sensitive data
5.
Audit logging: Enable comprehensive audit logging
6.
Regular reviews: Periodic access reviews and cleanup
7.
Training: Security awareness training for users
8.
Incident response: Security incident procedures
59. How do you
implement effective governance and administration for Power BI?
Answer: Governance framework:
1.
Center of Excellence: Establish BI CoE
2.
Standards and guidelines: Development and design standards
3.
Certification process: Content certification procedures
4.
Monitoring and compliance: Usage monitoring and compliance
5.
Training programs: Comprehensive user training
6.
Support structure: Tiered support model
7.
Change management: Controlled change processes
8.
Performance monitoring: Continuous performance tracking
60. What are the
key considerations for Power BI capacity planning and scaling?
Answer: Capacity planning
considerations:
1.
Usage analysis: Understand current and projected usage
2.
Performance requirements: Define performance SLAs
3.
Growth planning: Plan for user and data growth
4.
Workload distribution: Balance workloads across capacity
5.
Peak load planning: Handle peak usage periods
6.
Cost optimization: Balance performance and cost
7.
Monitoring strategy: Continuous capacity monitoring
8.
Scaling procedures: Automated and manual scaling options
Additional Resources
Essential DAX
Functions for Interviews
// Time
intelligence
TOTALYTD([Sales],
Dates[Date])
SAMEPERIODLASTYEAR(Dates[Date])
DATEADD(Dates[Date],
-1, YEAR)
// Filter
functions
CALCULATE([Sales],
Products[Category] = "Electronics")
ALL(Table)
FILTER(Table,
Condition)
// Iterator
functions
SUMX(Table,
Expression)
AVERAGEX(Table,
Expression)
// Relationship
functions
RELATED(Table[Column])
RELATEDTABLE(Table)
USERELATIONSHIP(Table1[Column],
Table2[Column])
Power Query M Functions
// Data transformation
Table.TransformColumns(table, {{"Column", each Text.Upper(_)}})
Table.AddColumn(table, "NewColumn", each [Column1] + [Column2])
Table.SelectColumns(table, {"Column1", "Column2"})
// Data cleaning
Table.RemoveBlankRows(table)
Table.ReplaceValue(table, "Old", "New", Replacer.ReplaceText)
Performance
Optimization Checklist
•
Use appropriate data types
•
Remove unnecessary columns
•
Implement proper relationships
•
Use measures instead of
calculated columns
•
Optimize DAX calculations
•
Limit visuals per page
•
Use aggregation tables
•
Monitor query performance
Common Interview Scenarios
•
Financial reporting and
consolidation
•
Sales and marketing analytics
•
Operational dashboards
•
Customer analytics
•
Supply chain analytics
•
Real-time monitoring
•
Multi-tenant solutions
•
Global deployments
This comprehensive guide covers all aspects of Power BI, DAX, and
Power Query from basic concepts to advanced enterprise implementations. Focus
on understanding the underlying principles and be prepared to explain your
design decisions and optimization strategies.
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