গাইড লিঙ্ক ক্যুইজ · মাঝারি স্তর
খুচরা কুইজে AI
সিদ্ধান্তের প্রেক্ষাপট, ভোক্তা প্রভাব, উপগোষ্ঠীর ফলাফল, গোপনীয়তা, সংশোধনের পথ এবং মিথ্যা সিদ্ধান্তের সম্পূর্ণ খরচ দ্বারা খুচরা AI মূল্যায়ন করুন।
প্রশ্ন 1 এর 8
Why define data available at the decision moment for a retail model?
প্রতিটি প্রশ্নের পূর্বরূপ দেখুন8 প্রশ্ন
আপনি কুইজ নেওয়ার সাথে সাথে উত্তর এবং ব্যাখ্যাগুলি উপস্থিত হয়।
- Why define data available at the decision moment for a retail model?
- Recommendations, pricing, forecasts, and fraud reviews have different inputs and error costs
- Every decision can use later purchase data
- Data timing never matters
- All retail models optimize the same outcome
- A recommender increases basket size but customers complain about irrelevant offers. What should be reviewed?
- Only the basket-size increase
- Customer outcomes and whether the proxy metric harms service
- The color of the recommendation card
- The number of products in inventory
- What did the false-decline worked example ask retailers to measure?
- The average basket size
- Only the number of blocked fraud attempts
- Legitimate customers declined, correction time, and safer verification options
- The model’s response time
- Why evaluate retail outcomes by subgroup?
- It removes the need to monitor returns
- All customers have identical histories
- Segmentation guarantees better revenue
- Averages can hide less useful service or unfair treatment for some groups
- Which outcomes should accompany a recommendation model’s sales results?
- Only click-through
- Useful discovery, returns, complaints, and stock availability
- Only the count of messages
- মডেল পরামিতি সংখ্যা
- Which retail information does the guide identify as needing protection?
- Only store hours
- Only public product names
- Purchase history, location, and identity information
- The model’s display color
- What should an automated system provide when it cannot answer or a transaction is wrong?
- An alternative route and a way for staff to correct the issue
- A higher price
- A hidden score only
- No explanation or recourse
- Why monitor new products, promotions, and seasons?
- They prevent the need for versioning
- They make all recommendations more accurate
- They eliminate false declines
- They can change behavior and cause model or policy drift