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CMA Category-Manager Exam Syllabus Topics:

SectionObjectives
Topic 1: Fact-Based Selling and Storytelling- Communication and Insights
  • 1. Category Review Presentation
  • 2. Actionable Shopper Insights
  • 3. Storytelling with Fact-Based Insights
Topic 2: Retail Economics and Supply Chain- Business Operations
  • 1. Supply Chain Fundamentals
  • 2. Retailer Economics
  • 3. Category Manager Role in Supply Chain
Topic 3: Pricing and Promotion Analytics- Commercial Strategy
  • 1. Promotion Analysis Techniques
  • 2. Predictive Analytics
  • 3. Pricing Strategy and Analysis
Topic 4: Assortment and Space Management- Retail Optimization
  • 1. Geodemographic Data Usage
  • 2. Store Clustering
  • 3. Efficient Assortment Process
  • 4. Space Management Fundamentals
Topic 5: Shopper and POS Data Analytics- Building Data Competency
  • 1. Panel Data Analysis
  • 2. POS Data Analytics
  • 3. Shopper Data Analytics
Topic 6: Category Health and Assessment- Category Performance Evaluation
  • 1. Root Cause Analytics
  • 2. Baseline and Incremental Drivers
  • 3. Category Health Assessment

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CMA Certified Professional Category Manager (CPCM) Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which primary data sources are used to answer the 'How' and 'Who' questions in category management?

Answer: C

Explanation:
The correct answer is D because Loyalty Card Data and Household Panel Data are the data sources most directly tied to shopper identity, household behavior, trip behavior, repeat purchase, switching, loyalty, and demographics. The CPCM/CMKG material states that household panel data is "one of the primary data sources required to do category management work" and that it provides "a clear picture of consumer behaviour" so strategies can focus on the consumer dynamics driving category and brand performance.
This question is specifically asking about the "How" and "Who" questions. POS data is very strong for answering what sold, where, when, and how much , but it is weaker for answering who the shopper is unless it is connected to household or loyalty information. Loyalty card data identifies known shopper behavior at the retailer level. Household panel data adds broader consumer behavior across trips, baskets, brands, retailers, and demographics.
Option A is wrong because social media and web traffic data may support digital insight, but they are not the core CPCM shopper data sources here. Option B is wrong because POS data is sales-performance data, not the best source for shopper identity. Option C is qualitative research, useful for context, but not the primary data-source pair tested in CPCM shopper analytics.


NEW QUESTION # 14
Which of the following metrics is used to evaluate space productivity in retail environments?

Answer: A

Explanation:
The correct answer is D .
Sales per Square Foot is the standard retail productivity metric that evaluates how efficiently physical selling space generates revenue. The CPCM program includes Space Management Fundamentals as part of the official CPCM curriculum, and CMKG explains that planograms become more analytical when product performance data such as unit movement, price, and cost are added.
Sales per square foot directly connects sales output to the amount of retail space used. Square explains the calculation as sales divided by the store's sales space and states that it helps evaluate how efficiently sales space is being used.
Option A, customer foot traffic, measures store visits, not space productivity. Option B, inventory turnover, measures how quickly stock sells through. Option C, net profit margin, measures profitability percentage.
Only option D directly evaluates productivity of retail space.


NEW QUESTION # 15
What is the Dollar Sales per $MMACV for the Product Group in Store 478?

Answer: B

Explanation:
The correct answer is D .
Dollar Sales per $MMACV measures sales productivity normalized by store or market selling power. CPG Data Insights defines Sales per $MM ACV as a velocity measure calculated by dividing sales by the market's All Commodity Volume expressed in millions, and explains that it helps compare productivity across markets, retailers, or products with different distribution levels.
For Store 478 :
Product Group Actual Dollar Sales = $10,000
Store 478 ACV $ Sales = $2,000,000
ACV expressed in millions = $2,000,000 ÷ $1,000,000 = 2
Calculation:
$10,000 ÷ 2 = $5,000
So the Dollar Sales per $MMACV for the Product Group in Store 478 is $5,000 .
Option C, $4,000, is the total-store benchmark calculation: $400,000 ÷ 100 = $4,000. The question asks specifically for Store 478 , not the total store benchmark.


NEW QUESTION # 16
What does the metric 'Household Penetration' measure in market-level shopper dynamics?

Answer: C

Explanation:
The correct answer is D .
Household penetration measures how many households bought the product, brand, category, or product group during the measured period. CMKG explains the panel-data formula as Total Number of Buying Households, or Penetration, multiplied by Spend per Buying Household equals Dollar Sales . It further explains that penetration relates to the number of households purchasing the product.
Option A describes purchase quantity or items per household, not penetration. Option B describes share of wallet or share of requirements-type spending allocation, not household penetration. Option C describes category dollar sales, not the breadth of the buyer base.
Household penetration is a reach measure. It tells whether the category is bought by many households or only by a narrow group of households.


NEW QUESTION # 17
What is the primary purpose of regression analysis?

Answer: C

Explanation:
The correct answer is B .
Regression analysis is used to understand how a dependent variable changes in relation to one or more independent variables. In pricing analytics, that usually means analyzing how sales, units, profit, or demand respond to price or other business drivers. The CPCM pricing material identifies correlation and price regression analysis as methods used to evaluate historical pricing and project future sales and profit at specific price points. CMKG also lists advanced pricing analytics as including breakeven point, correlation, price regression, ABC, and slope.
Option A is wrong because calculating an average is descriptive statistics, not regression. Option C is too strong because regression can show relationships or associations, but it does not automatically prove causation. NIST's regression explanation specifically warns that cause-and-effect cannot necessarily be inferred from regression alone. Option D is wrong because classification belongs to classification models or supervised learning classification tasks, not standard regression analysis.


NEW QUESTION # 18
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