Quiz bank
A reusable bank of questions covering all six modules, organized by topic rather than by module so instructors can assemble a quiz for any single module, a review session, or a randomized pool. Contains 30 multiple choice questions and 12 short answer questions across seven topics: access and schema, SQL and filters, pricing power, sell through, demand index, brand to ticker, and data literacy and caveats.
Section A: Multiple choice
Topic: Access and schema
Q1. Which surface should a student use to run ad hoc SQL queries without writing any code or requesting an API key? A. api.altfndata.com B. docs.altfndata.com C. sandbox.altfndata.com D. None; a key is always required
Q2. How does a student typically gain access to the sandbox? A. By emailing the ALT/FNDATA team and waiting for manual approval B. By self-registering with a work or school email address, which is auto-approved C. By purchasing a Membership plan D. Sandbox access is granted only to instructors, who then share credentials with students
Q3. Where would a student find the downloadable Python client and a runnable tutorials notebook? A. sandbox.altfndata.com B. api.altfndata.com C. docs.altfndata.com D. The coverage browser
Q4. Roughly how far back does ALT/FNDATA's transaction history extend? A. The early 2010s B. The late 1990s C. Only the past five years D. The entire dataset is real-time only, with no historical archive
Q5. A student wants to confirm the exact column names on a category table before writing a query against the production API. Which endpoint should they call? A. POST /v1/tables/{name}/query B. GET /v1/tables/{name}/schema C. GET /v1/pricing D. POST /v1/auth/token
Topic: SQL and filters
Q6. Which SQL clause would a student use to keep only records where status equals sold? A. GROUP BY status = 'sold' B. ORDER BY status C. WHERE status = 'sold' D. LIMIT status = 'sold'
Q7. In the sandbox SQL editor, which clause restricts a query's output to the 10 rows with the highest usd_price_decimal? A. WHERE usd_price_decimal = MAX B. ORDER BY usd_price_decimal DESC LIMIT 10 C. GROUP BY usd_price_decimal LIMIT 10 D. TOP 10 usd_price_decimal
Q8. A query needs to count sold records per vendor. Which clause is required alongside COUNT(*)? A. HAVING vendor B. GROUP BY vendor C. ORDER BY vendor ASC D. WHERE vendor IS NOT NULL only
Q9. On the production API, which filter operator performs a case-insensitive substring match? A. eq B. in C. like D. is_null
Q10. A student wants records for either "Rolex" or "Omega" in a single filter, without writing two separate filter objects for designer. Which operator fits best? A. eq B. in C. gt D. like
Topic: Pricing power
Q11. Pricing power is defined as: A. The average usd_price_decimal for a designer B. The median, across sold records, of usd_price_decimal divided by sale_estimates_high_usd_price C. The count of sold records for a designer D. The ratio of vendor count to designer count
Q12. A designer shows a pricing power figure of 1.36. What does this mean? A. Buyers typically paid 36 percent below the auction house's high estimate B. Buyers typically paid 36 percent above the auction house's high estimate C. 136 percent of lots for that designer sold D. The designer's average price grew 36 percent year over year
Q13. Which records should be excluded before computing a pricing power ratio? A. Records where status is sold B. Records where sale_estimates_high_usd_price is null or zero C. Records where designer is populated D. All records from before 2020
Q14. A pricing power figure below 1.0 for a given designer most directly suggests: A. The brand's items typically sell below the auction house's own high estimate B. The brand has a low sell-through rate C. The brand is not tracked by ALT/FNDATA D. The dataset contains a data quality error
Topic: Sell through
Q15. Sell-through rate is calculated as: A. Sold count divided by total offered count (sold plus unsold) B. Sold count divided by pricing power C. Realized price divided by high estimate D. Total record count divided by number of designers
Q16. Which SQL mistake would most directly corrupt a sell-through rate calculation? A. Ordering results by sale_date B. Filtering the entire query to status = 'sold' before computing the denominator C. Using a LIMIT clause D. Selecting more fields than needed
Q17. A sell-through rate of 0.85 for a designer means: A. 85 percent of offered lots for that designer actually sold B. The designer's pricing power is 0.85 C. 85 percent of the designer's lots were unsold D. The designer sold for 85 percent of the high estimate on average
Q18. Which two status values does a correctly scoped sell-through rate query need to include in its denominator? A. sold and pending B. sold and unsold C. unsold and cancelled D. Only sold
Topic: Demand index
Q19. Building a demand index for a designer typically involves: A. Grouping sold records by quarter of sale_date and computing a count or average price per quarter B. Grouping all records by vendor C. Computing pricing power for every designer at once D. Filtering to unsold records only
Q20. Why should the most recent one to two quarters of a demand index be treated with caution? A. They are always fabricated B. Ingestion of new auction results lags behind sale_date, so recent quarters are typically under-represented C. The sandbox deletes old data automatically D. Recent quarters always show inflated prices
Q21. A demand index built for learning purposes in this course should be framed as: A. A definitive claim about whether a brand's market value is rising or falling B. A methodology exercise in bucketing cleared prices by quarter, with an explicit caveat about recency C. A forecast of next quarter's prices D. Proof of year over year appreciation
Q22. Which field is essential for correctly bucketing sales into calendar quarters? A. vendor B. sale_date C. stock_ticker D. item_title
Topic: Brand to ticker
Q23. The stock_ticker field is best described as: A. The internal ALT/FNDATA record identifier B. The public trading ticker of the parent company that owns a brand, where a mapping exists C. A synonym for designer D. The auction house's internal lot number
Q24. Roughly how many public and private companies does ALT/FNDATA's company mapping cover? A. About 200 B. More than 2,000, with S&P 500 sector mapping C. Exactly 500 D. Fewer than 50
Q25. Richemont's ticker, CFR.SW, could appear on records in which combination of categories? A. Only watches B. Only jewelry C. Both watches and jewelry, since Richemont owns brands in both D. Only automobiles
Q26. Why is the ability to roll demand signals up to a single stock_ticker across multiple categories valuable to an equity research analyst? A. It lets them ignore individual brands entirely B. It produces a more complete, company-level demand read for a diversified conglomerate than any single category alone C. It replaces the need for a company's earnings report D. It only works for private companies
Topic: Data literacy and caveats
Q27. Why must an analyst always use sale_date, not an ingestion or pull date, for time-based analysis? A. Ingestion date is not stored in the dataset B. sale_date anchors each record to when the transaction actually happened, which is what makes the series point-in-time correct C. sale_date is only available for sold records D. It does not matter which date is used
Q28. A quarterly series shows a sharp drop in the most recent quarter. What is the most responsible next step? A. Immediately publish a claim that demand for the brand is falling B. Consider ingestion lag, and either exclude or clearly caveat the most recent quarter before drawing any conclusion C. Delete the most recent quarter permanently from the dataset D. Assume the drop reflects fraud
Q29. ALT/FNDATA's AI natural language search feature should be treated, for course purposes, as: A. The primary way students should query the data B. Fully released and production ready C. Still in development and not featured in coursework D. A replacement for the sandbox SQL editor
Q30. Which of the following is the most responsible way to describe a single large auction result (for example one record-breaking sale) in an analysis? A. As definitive proof of a market-wide trend B. As one data point that should be corroborated with a broader sample, such as pricing power or sell-through across many records, before drawing a conclusion C. As irrelevant and not worth mentioning D. As equivalent to a company's full quarterly earnings
Section B: Short answer
S1. Name the three ALT/FNDATA access surfaces and one thing each is used for.
S2. What must a developer do before writing their first query against an unfamiliar table on the production API, and which two endpoints support this step?
S3. Write the SQL WHERE clause needed to filter a query to only sold records for a designer called "Cartier".
S4. Name three of the documented API filter operators and briefly describe what each one does.
S5. In your own words, define pricing power and state what a ratio above 1.0 indicates about buyer behavior.
S6. Why must sale_estimates_high_usd_price be checked for null or zero values before it is used in a pricing power calculation?
S7. Define sell-through rate and identify the two status values that must both appear in its denominator.
S8. Explain why filtering an entire query to status = 'sold' before computing sell-through rate would produce an incorrect result.
S9. Describe, in two to three sentences, how to build a simple quarterly demand index for one designer using SQL.
S10. Explain the recency caveat that applies to any time-bucketed metric built from this dataset, and state what an analyst should do about it before drawing a conclusion.
S11. Explain what the stock_ticker field allows an analyst to do that the designer field alone does not.
S12. Describe one concrete step a reader should take before trusting a single number pulled from this dataset (for example a single quarter's average price, or a single sale's realized price versus estimate).