Practice Questions - Perform Analytics in Power BI

Based on Module 18: PL-300 Exam Standard

Section 1: Statistical Summary and Top N Analysis

Question 1

You are creating a Power BI report for the Supply Chain team. They want to analyze order quantities and identify the distribution pattern. Which combination of techniques should you use to create a histogram showing order quantity distribution?

A) Create a bar chart with Order Qty on the X-axis and use the AVERAGE function

B) Create a clustered column chart, create a bin group for Order Qty, add the binned field to the X-axis and Order Qty to the Y-axis

C) Use a scatter chart with Order Qty on both axes and apply clustering

D) Create a line chart with Order Qty and use the Analyze feature

Answer: B

Explanation: To create a histogram in Power BI, you need to: (1) Create a bin group for the continuous numerical field (Order Qty) by right-clicking the field and selecting "New Group", (2) Set the Group type to "Bin" and configure the bin size/count, (3) Use a clustered column chart, (4) Place the binned field on the X-axis and the original field on the Y-axis. This creates equal-sized buckets that show the distribution of data.

Question 2

A data analyst needs to identify the top 10 products by sales revenue. The analyst wants to show what percentage of total sales these top 10 products represent. Which DAX formula would accomplish this?

A) Top 10 = TOPN(10, Product, [Total Sales])

B) Top 10 Products = SUMX(TOPN(10, 'Product', 'Product'[Total Sales]), [Total Sales])

C) Top 10 = CALCULATE([Total Sales], TOPN(10, Product))

D) Top 10 = FILTER(Product, [Total Sales] > 10)

Answer: B

Explanation: The correct formula uses SUMX with TOPN to calculate the sum of sales for the top 10 products. The TOPN function returns the top 10 rows from the Product table based on Total Sales, and SUMX iterates through those rows to sum up the Total Sales values. This measure can then be compared against total sales to calculate the percentage contribution.

Question 3

You need to create a statistical analysis showing the average order quantity. To avoid performance issues, what is the recommended approach?

A) Right-click the field in the visual and select "Average"

B) Create a DAX measure: Average Qty = AVERAGE(Sales[Order Qty])

C) Use the Quick Measure feature for all statistical calculations

D) Create a calculated column with AVERAGE function

Answer: B

Explanation: While Power BI provides quick statistical functions by right-clicking fields, it's better to create explicit DAX measures to avoid performance issues. Creating a measure like Average Qty = AVERAGE(Sales[Order Qty]) gives you more control, improves performance, and makes the calculation reusable across multiple visuals.

Section 2: Identifying Outliers

Question 4

You are analyzing shipping warehouse data and need to identify products with unusually high order quantities. Which visual type is BEST suited for identifying outliers in numerical data?

A) Clustered bar chart

B) Pie chart

C) Scatter chart

D) Table visual

Answer: C

Explanation: Scatter charts are the best visuals for identifying outliers because they show the relationship between two numerical values and make it easy to spot data points that are separated from the main cluster. Outliers appear as dots isolated from the main grouping of data points.

Question 5

A data analyst creates the following DAX measure to identify outliers:

Outliers = CALCULATE(
    [Order Qty],
    FILTER(
        VALUES('Product'[Product Name]),
        COUNTROWS(
            FILTER(
                Sales,
                [Order Qty] >= [Min Qty]
            )
        ) > 0
    )
)

What is the primary purpose of this measure?

A) To calculate the minimum order quantity across all products

B) To identify products where order quantities meet or exceed the minimum threshold

C) To count the total number of orders

D) To filter products by category

Answer: B

Explanation: This measure filters products based on whether they have orders with quantities greater than or equal to the minimum quantity threshold. It uses nested FILTER functions to first check if orders meet the minimum quantity, then counts those rows, and finally filters products that have at least one order meeting this criterion.

Question 6

After identifying outliers in a scatter chart, which THREE techniques can you use to highlight and investigate these outliers? (Choose 3)

A) Add a legend to identify outliers among other data

B) Use slicers to filter and highlight outlier data

C) Apply filters to isolate outlier data

D) Use the TOPN function to remove outliers

E) Create a calculated table for outliers

Answer: A, B, C

Explanation: After identifying outliers: (1) Adding a legend helps visually distinguish outliers from regular data points, (2) Slicers can be used to interactively filter and highlight outlier data, (3) Filters can isolate outlier data for detailed analysis. These are the primary interactive techniques for investigating outliers in Power BI visuals.

Section 3: Grouping and Binning

Question 7

What is the PRIMARY difference between grouping and binning in Power BI?

A) Grouping is for text fields; binning is for numerical fields

B) Grouping categorizes data manually or based on selections; binning groups continuous fields like numbers and dates into equal-sized intervals

C) Grouping is automatic; binning is manual

D) There is no difference; they are the same feature

Answer: B

Explanation: Grouping is a technique for categorizing data where you can manually select multiple data points and group them together. Binning is specifically for grouping continuous fields (numerical and time-based data) into bins of equal size. For example, you might group states manually, but you would bin order quantities into ranges like 1-10, 11-20, etc.

Question 8

You need to create bins for the Order Qty field with the following specifications:

• Bin Type: Number of bins
• Bin count: 5
• Min value: 1
• Max value: 44

What is the correct process to create this bin group?

A) Right-click Order Qty in the Data pane → New Group → Set Group type to "Bin" → Configure bin settings

B) Select Order Qty in a visual → Right-click → Create Bins

C) Use DAX: Bins = GROUPBY(Sales, Sales[Order Qty])

D) Go to Modeling tab → New Column → Create bin formula

Answer: A

Explanation: To create bins: (1) Right-click the field in the Data pane, (2) Select "New Group", (3) In the Groups window, set Group type to "Bin", (4) Set Bin Type to "Number of bins", (5) Configure the bin count, min value, and max value. The new binned field will appear in the Data pane with "(bins)" appended to its name.

Question 9

After creating a group of states with sales greater than $500,000, you notice a new field appears in the Legend well and Data pane. What happens to new states added to your data source after a refresh?

A) They are automatically added to the appropriate group based on their sales value

B) They appear in the ungrouped values list and go into the "Other" group

C) They are excluded from the report until manually added to a group

D) They create a new group automatically

Answer: B

Explanation: When you create groups in Power BI, new items that appear after data refresh are placed in the ungrouped values list and automatically go into the "Other" group. This ensures all data is visible, but you need to manually edit the groups if you want to reorganize the new items into specific groups.

Section 4: Clustering Techniques

Question 10

What is the fundamental difference between clustering and grouping in Power BI?

A) Clustering is manual; grouping is automated

B) Clustering uses machine learning to automatically identify similar data segments; grouping is a manual categorization process

C) Clustering works only with text; grouping works with numbers

D) Clustering and grouping are identical features with different names

Answer: B

Explanation: Clustering uses Power BI's built-in machine learning algorithm to analyze your semantic model and automatically identify segments of data that are similar to each other but dissimilar to other data. Grouping, on the other hand, is a manual process where you select and categorize data points yourself. Clustering analyzes similarities and dissimilarities in attribute values to create optimal groupings.

Question 11

You create a scatter chart with Order Qty on the X-axis, Sales on the Y-axis, and Unit Price in Values. You want to apply clustering to identify natural customer segments. What is the correct process?

A) Right-click the visual → Analyze → Apply Clustering

B) Select More options (...) in the upper-right corner of the visual → Automatically find clusters → Configure cluster settings

C) Go to the Modeling tab → Create Clusters → Select fields

D) Use DAX: Clusters = CLUSTER(Sales)

Answer: B

Explanation: To apply clustering to a scatter chart: (1) Select the More options (...) icon in the upper-right corner of the visual, (2) Select "Automatically find clusters", (3) In the Clusters window, configure the name, field, description, and number of clusters. Power BI then runs the clustering algorithm and creates a new categorical field containing the cluster groups, which is added to the Legend well.

Question 12

When you leave the "Number of clusters" box blank in the Clusters window, what happens?

A) Power BI creates exactly 3 clusters by default

B) The clustering operation fails

C) Power BI automatically determines the optimal number of clusters based on the data

D) Power BI creates one cluster for each data point

Answer: C

Explanation: When the "Number of clusters" box is blank (default setting), Power BI uses its machine learning algorithm to automatically analyze the data and determine the optimal number of clusters that makes the most sense based on the data patterns and distributions. This is useful when you're unsure how many natural segments exist in your data.

Question 13

You want to find clusters using more than two measures. Which approach should you use?

A) Create multiple scatter charts with different measure combinations

B) Use a table visual with all desired fields, then apply the clustering algorithm

C) Scatter charts can only use two measures; clustering with more measures is not possible

D) Use the Key Influencers visual instead

Answer: B

Explanation: While scatter charts limit you to two measures (X-axis and Y-axis), you can use a table visual to include multiple fields/measures and then apply the clustering algorithm using the same process (More options → Automatically find clusters). This allows Power BI to analyze patterns across multiple dimensions simultaneously.

Section 5: Time Series Analysis

Question 14

Which THREE visual types are best suited for conducting time series analysis in Power BI? (Choose 3)

A) Line charts

B) Pie charts

C) Area charts

D) Scatter charts

E) Card visuals

Answer: A, C, D

Explanation: Time series analysis involves analyzing data over time to identify trends and patterns. The best visuals for this are: (1) Line charts - show trends and changes over continuous time periods, (2) Area charts - similar to line charts but emphasize magnitude of change, (3) Scatter charts - especially useful with the play axis feature to show how relationships between variables change over time.

Question 15

You import the Play Axis custom visual to show quarterly sales trends. After adding the Quarter field to the Play Axis visual, what THREE formatting options are available? (Choose 3)

A) Animation Settings (autostart, looping, speed)

B) Data point radius

C) Colors (overall color or individual button colors)

D) Enable Caption On (toggle text display)

E) Axis font size

Answer: A, C, D

Explanation: The Play Axis visual has three key formatting categories: (1) Animation Settings - controls play functionality including autostart, looping, and animation speed, (2) Colors - adjusts the overall color or individual control button colors, (3) Enable Caption On - toggles text display next to the visual and allows caption formatting adjustments.

Question 16

Your organization has security policies that restrict the use of custom visuals. What alternative can you use to create animated time series analysis?

A) Use the Decomposition Tree visual

B) Use the scatter chart visual with its built-in play axis feature

C) Create multiple line charts with bookmarks

D) Time series animation is not possible without custom visuals

Answer: B

Explanation: If custom visuals are not allowed in your organization, you can use the built-in scatter chart visual which has a play axis feature. This provides similar animation capabilities to show how data changes over time without requiring a custom visual import.

Section 6: Analyze Feature

Question 17

When does the Analyze feature NOT work in Power BI? (Choose 2)

A) When you have non-numeric filters applied to your visual

B) When you have measure filters applied

C) When using a scatter chart

D) When the visual has more than 10 data points

E) When working with calculated tables

Answer: A, B

Explanation: The Analyze feature has two key limitations: (1) It doesn't work if you have non-numeric filters applied to your visual, (2) It doesn't work if you have measure filters applied. These limitations are important to remember when using the Analyze feature to get automated insights.

Question 18

You right-click a data point showing increased help tickets for the Northeast region. The Analyze feature displays two options. What are these options?

A) "Show details" and "Drill through"

B) "Explain the increase" and "Find where the distribution is different"

C) "Filter data" and "Group data"

D) "Create measure" and "Add to favorites"

Answer: B

Explanation: When you right-click a data point and hover over the Analyze option, Power BI displays two analysis options: (1) "Explain the increase" - uses AI to analyze what factors contributed to the increase, (2) "Find where the distribution is different" - identifies how the distribution differs across dimensions. The available options depend on the data point selected.

Question 19

After using the Analyze feature and finding useful insights, how can you add the generated visual to your report?

A) Copy and paste the visual manually

B) Select the plus (+) icon in the upper-right corner of the insight visual

C) Right-click the visual and select "Add to report"

D) The visual is automatically added to the report

Answer: B

Explanation: When the Analyze feature generates useful insights in a new visual, you can add it to your report by selecting the plus (+) icon in the upper-right corner of the insight visual. This allows other users to benefit from the AI-generated analysis.

Section 7: What-If Parameters

Question 20

You need to create a what-if parameter to forecast sales growth. The parameter should range from 100% (no growth) to 150% (50% growth) in 5% increments. Which settings are correct?

A) Minimum: 1, Maximum: 1.5, Increment: 0.5, Data type: Decimal

B) Minimum: 1.00, Maximum: 1.50, Increment: 0.05, Data type: Fixed decimal number

C) Minimum: 100, Maximum: 150, Increment: 5, Data type: Whole number

D) Minimum: 0, Maximum: 50, Increment: 5, Data type: Percentage

Answer: B

Explanation: For a what-if parameter representing percentages from 100% to 150%: (1) Set Minimum to 1.00 (representing 100%), (2) Maximum to 1.50 (representing 150%), (3) Increment to 0.05 (representing 5% steps), (4) Data type to "Fixed decimal number" for currency/percentage forecasts. Note: Always precede decimal values with zero (0.05, not .05).

Question 21

After creating a what-if parameter called "Sales Forecast Percentage", what TWO items are automatically created? (Choose 2)

A) A new table in the data model

B) A measure showing the current parameter value

C) A calculated column with forecasted values

D) A slicer visual on the current report page (if option is selected)

E) A new data source connection

Answer: B, D

Explanation: When you create a what-if parameter, Power BI automatically creates: (1) A measure that represents the current value of the parameter, (2) A slicer visual on the current report page (if "Add slicer to this page" is selected). The parameter also creates a new table in the data model containing the parameter field.

Question 22

You create a what-if parameter and the measure:

Gross Sales Forecast = [Gross Sales] * [Sales Forecast Percentage Value]

You then create a clustered column chart with MonthName, Gross Sales, and Gross Sales Forecast. What happens when you move the parameter slider?

A) Only the Gross Sales bar changes

B) Both bars change proportionally

C) Only the Gross Sales Forecast bar changes to reflect the new percentage

D) Nothing happens; you need to refresh the data

Answer: C

Explanation: When you move the what-if parameter slider, only the measure that uses the parameter (Gross Sales Forecast) changes dynamically because it multiplies the fixed Gross Sales value by the changing parameter value. The original Gross Sales bar remains constant, allowing you to compare actual vs. forecasted values.

Question 23

You add a constant line at $2 million to your forecast visual. As you move the what-if slider, you determine sales must increase by 140%. What does this mean?

A) Sales must be multiplied by 1.40 (140% of current value) to reach the threshold

B) Sales must increase by an additional 40% beyond current levels

C) Sales must reach exactly $140,000

D) Sales are currently at 140% of the target

Answer: A

Explanation: When the parameter shows 1.40 (or 140%), it means sales must be multiplied by 1.40 to reach the threshold. In other words, current sales need to grow to 140% of their current value (a 40% increase) to meet the $2 million target. The parameter value represents the multiplier, not the increase percentage.

Section 8: Specialized AI Visuals

Question 24

Which AI visual should you use when you want to understand the factors that drive a specific metric, such as what causes sales opportunities to be won?

A) Decomposition Tree

B) Key Influencers

C) Q&A

D) Scatter Chart

Answer: B

Explanation: The Key Influencers visual is specifically designed to help understand the factors that drive a particular metric. It uses AI to analyze data, rank the factors that matter, and present them as key influencers. For example, it can show that when the discount rate increases by 2%, the likelihood of a won sales opportunity increases by a factor of 2.76.

Question 25

The Key Influencers visual includes which TWO views? (Choose 2)

A) Key influencers view showing individual factors

B) Top segments view showing highest-ranking segments

C) Time series view showing trends

D) Distribution view showing data spread

E) Correlation matrix view

Answer: A, B

Explanation: The Key Influencers visual contains two main views: (1) Key influencers view - shows individual factors that influence the metric, with an adjacent visual displaying a comparison of the influencer against the remainder of the data, (2) Top segments view - displays the highest-ranking segments that contribute to a particular metric.

Question 26

You need to create a visual that allows report consumers to drill down into data across multiple dimensions in any order and provides AI-guided exploration. Which visual should you use?

A) Matrix visual

B) Decomposition Tree

C) Hierarchy Slicer

D) Drill-through page

Answer: B

Explanation: The Decomposition Tree visual allows report consumers to visualize data across multiple dimensions and drill down in any order. As an AI visual, it provides guided exploration by suggesting the next best dimension to drill into (indicated by a light bulb icon), making it valuable for ad-hoc analysis and root cause investigation.

Question 27

In a Decomposition Tree visual, what does the lock icon next to a split indicate?

A) The data is locked and cannot be modified

B) The report author added the split and set it so it cannot be removed

C) The split requires authentication to view

D) The split is currently being edited by another user

Answer: B

Explanation: In a Decomposition Tree visual, a lock icon next to a split indicates that the report author intentionally added that split and configured it so that report consumers cannot remove it. This ensures certain important drill-down paths are always available. In contrast, a light bulb icon indicates an AI-suggested split.

Question 28

What is the PRIMARY capability of the Q&A visual?

A) Creating automated reports

B) Allowing report consumers to ask natural language questions and receive visual responses

C) Performing statistical analysis

D) Creating calculated measures automatically

Answer: B

Explanation: The Q&A visual allows report consumers to ask questions using natural language (in English) and receive responses as data visualizations. This provides a simple, interactive way for users to explore data without needing to know Power BI or create visuals themselves.

Question 29

Which THREE actions can optimize the Q&A experience? (Choose 3)

A) Ensure data field names are user-friendly

B) Add synonyms and terms to enhance the semantic model

C) Hide fields used only in relationships to restrict their use

D) Increase the visual size to maximum dimensions

E) Use complex DAX measures

Answer: A, B, C

Explanation: To optimize Q&A: (1) Use user-friendly field names that match how users naturally describe the data, (2) Add synonyms and terms to the semantic model so Q&A recognizes different ways of asking about the same data, (3) Hide fields that are only used for relationships (like foreign keys) to prevent confusion and restrict their use in Q&A queries.

Question 30

Your organization wants to create an interactive sales analysis report. The Sales team wants to ask their own questions about the data without creating visuals. Which approach is BEST?

A) Create multiple report pages covering all possible questions

B) Add a Q&A visual to the report so users can ask questions independently

C) Train all users on Power BI Desktop

D) Create a detailed user manual with instructions

Answer: B

Explanation: The Q&A visual is designed exactly for this scenario. It allows users to quickly and independently get answers by asking questions in natural language, without requiring prior knowledge of Power BI or the need to create visuals. This saves time for both report creators and consumers while empowering users to explore data on their own terms.

Scenario-Based Questions

Question 31

Scenario: You work for a retail company. The Product team reports that a specific product category suddenly showed a 300% increase in orders in the Northeast region compared to the previous month. They want to understand if this is an isolated incident or part of a broader pattern.

Which analytical approach should you use?

A) Create a histogram to show order distribution

B) Use a scatter chart to identify the category as an outlier, then use slicers to filter by region and time period to investigate the pattern

C) Apply clustering to group similar products

D) Create a what-if parameter to forecast future orders

Answer: B

Explanation: This scenario involves identifying an anomaly (outlier) and investigating its causes. The best approach is to: (1) Use a scatter chart to visualize and confirm the outlier in the data, (2) Add slicers for region and time period to filter and investigate whether the spike was isolated to one region and timeframe or part of a broader trend, (3) Potentially add a legend to highlight the outlier category among other categories.

Question 32

Scenario: The Customer Service team wants to analyze help ticket data. They specifically want to:

1. See tickets segmented by priority level (High, Medium, Low)
2. Identify cities with the highest number of tickets
3. Understand which factors drive ticket resolution times

Which THREE visuals/features would you use? (Choose 3)

A) Create groups to segment tickets by priority

B) Use Top N filter to identify top cities by ticket count

C) Apply the Key Influencers visual to analyze resolution time factors

D) Use what-if parameters

E) Apply clustering to group tickets

Answer: A, B, C

Explanation: For this scenario: (1) Use grouping to manually categorize tickets into High, Medium, and Low priority segments, (2) Apply a Top N filter (or use TOPN DAX function) to identify the top cities by ticket count, (3) Use the Key Influencers AI visual to understand what factors (priority, category, agent assigned, etc.) drive faster or slower resolution times.

Question 33

Scenario: The Finance team wants to forecast revenue under different growth scenarios. They want to see:

• Current revenue by month
• Projected revenue if growth ranges from 5% to 25% in 5% increments
• Clear visualization showing when projected revenue meets the $5 million quarterly target

What is the complete solution?

A) Create calculated columns for each growth percentage

B) Create a what-if parameter (Min: 1.05, Max: 1.25, Increment: 0.05), create a measure for projected revenue using the parameter, use a clustered column chart showing actual vs. projected revenue, add a constant line at $5 million

C) Use the Key Influencers visual to predict revenue

D) Create multiple report pages, one for each growth scenario

Answer: B

Explanation: This scenario requires: (1) What-if parameter with Min: 1.05 (105%), Max: 1.25 (125%), Increment: 0.05 (5% steps), (2) Measure like Projected Revenue = [Current Revenue] * [Growth Parameter Value], (3) Clustered column chart with Month, Current Revenue, and Projected Revenue, (4) Constant line at the $5 million threshold so the team can use the slider to find the growth percentage needed to hit the target.

Question 34

Scenario: Your organization has customer data with fields including Age, Income, Purchase Frequency, and Average Order Value. Marketing wants to identify natural customer segments for targeted campaigns. They don't know how many segments exist but want Power BI to determine the optimal groupings.

What is the best approach?

A) Manually create age groups and income brackets

B) Create a scatter chart with relevant fields, apply the clustering feature with the number of clusters blank (letting Power BI auto-determine), analyze the resulting segments

C) Use the Decomposition Tree to break down customers

D) Create what-if parameters for segment definitions

Answer: B

Explanation: This scenario is ideal for clustering: (1) Create a scatter chart (or table visual if using more than 2 measures) with the relevant customer attributes, (2) Apply "Automatically find clusters" and leave the "Number of clusters" blank so Power BI's algorithm determines the optimal number of segments, (3) Analyze the resulting clusters to understand the characteristics of each segment for targeted marketing.

Question 35

Scenario: You create a sales report with order quantity data. Your manager asks: "Why did orders suddenly spike in Q3?" You want to use an AI-powered feature to automatically analyze possible explanations without manually creating multiple visuals.

Which feature should you use?

A) Create a Decomposition Tree and manually drill through all dimensions

B) Right-click the Q3 data point and select Analyze → Explain the increase

C) Use what-if parameters to model different scenarios

D) Apply clustering to group orders

Answer: B

Explanation: The Analyze feature is designed exactly for this scenario. By right-clicking the data point showing the increase (Q3) and selecting "Analyze" → "Explain the increase", Power BI's AI automatically analyzes your data to identify factors that contributed to the spike and presents them in a new visual, saving you time compared to manual exploration.

Advanced Application Questions

Question 36

You need to display a 12-month sales trend that animates automatically when the report page loads, loops continuously, and shows how different product categories perform relative to each other over time. Which combination is required?

A) Line chart + slicer with Month

B) Scatter chart + Play Axis visual (custom or built-in) configured with autostart enabled, looping enabled, and Month field in the play axis

C) Area chart + what-if parameter

D) Decomposition Tree + time hierarchy

Answer: B

Explanation: For animated time series analysis: (1) Use a scatter chart to show relationships between metrics (like sales vs. margin) for different categories, (2) Add the Play Axis visual (custom or built-in scatter chart play axis) with the time field (Month), (3) Configure animation settings with autostart and looping enabled so it plays automatically and continuously when the page loads.

Question 37

You create a DAX measure:

Top 10 Contribution = 
DIVIDE(
    SUMX(TOPN(10, 'Product', [Total Sales]), [Total Sales]),
    SUM(Sales[Total Sales])
)

What does this measure calculate?

A) The average sales of the top 10 products

B) The total sales of exactly 10 products

C) The percentage of total sales contributed by the top 10 products

D) The number of products in the top 10

Answer: C

Explanation: This measure calculates what percentage of total sales is contributed by the top 10 best-selling products. The numerator (SUMX(TOPN(...))) calculates the sum of sales for the top 10 products, and the denominator (SUM(Sales[Total Sales])) represents total sales. DIVIDE returns the ratio as a percentage when formatted appropriately.

Question 38

Your report contains 5 AI visuals: 2 Key Influencers, 2 Decomposition Trees, and 1 Q&A visual. Users complain that the visuals are difficult to interact with. What is the MOST important action to improve the user experience?

A) Reduce the number of AI visuals

B) Size each AI visual as large as possible on the page to allow full interaction and exploration

C) Replace AI visuals with standard visuals

D) Add more detailed titles

Answer: B

Explanation: AI visuals (Key Influencers, Decomposition Tree, Q&A) require significant space for full interactivity. They contain multiple views, drill-down capabilities, and interactive elements. Making them as large as possible ensures report consumers can fully interact with and explore the data. This is a key best practice mentioned in the module.

Question 39

A report page contains multiple visuals. You want users to find specific insights independently without you creating a visual for every possible question. However, your organization doesn't allow custom visuals. Which solution works within these constraints?

A) Add a Q&A visual (it's a built-in AI visual, not a custom visual)

B) Create multiple report pages with bookmarks

C) This is not possible without custom visuals

D) Use drill-through pages only

Answer: A

Explanation: The Q&A visual is a built-in Power BI AI visual, not a custom visual from AppSource. It doesn't require importing or security exceptions. It allows users to ask natural language questions and get visual answers independently, making it perfect for this scenario even with custom visual restrictions.

Question 40

You create bins for Product Price with these settings: Bin Type: Size of bins, Bin size: 10, Min: 0, Max: 100. How many bins are created, and what are their ranges?

A) 10 bins ranging from 0-10, 10-20, ..., 90-100

B) 10 bins of equal size based on data distribution

C) 100 bins of size 10 each

D) The number of bins varies based on the data

Answer: A

Explanation: When you set "Size of bins" to 10 with Min: 0 and Max: 100, Power BI creates bins of size 10, resulting in 10 bins: 0-10, 10-20, 20-30, ..., 90-100. This differs from "Number of bins" where you specify how many bins you want and Power BI calculates the size.

Summary

This practice question set covers all major topics from Module 18:

✓ Statistical Summary (Questions 1-3)

✓ Identifying Outliers (Questions 4-6)

✓ Grouping and Binning (Questions 7-9)

✓ Clustering Techniques (Questions 10-13)

✓ Time Series Analysis (Questions 14-16)

✓ Analyze Feature (Questions 17-19)

✓ What-If Parameters (Questions 20-23)

✓ Specialized AI Visuals (Questions 24-30)

✓ Scenario-Based Applications (Questions 31-35)

✓ Advanced Applications (Questions 36-40)

Exam Preparation Tips:

1. Understand the difference between grouping, binning, and clustering
2. Know which visuals work best for specific analysis types
3. Remember when the Analyze feature does NOT work
4. Practice creating what-if parameters with correct decimal notation
5. Understand the three AI visuals and their specific use cases
6. Know how to optimize AI visuals and Q&A for best user experience
7. Practice interpreting DAX formulas for statistical analysis