dq01
Perform an aggregation on an explicit column

dq02
Perform an aggregation with an explicit table but not an inferred column

dq03
Perform an aggregation with an implicit table and implicit column

dq04
Find and compare information across tables without joins

dq05
Work with non-literal values

dq06
Work with non-literal values and non-SQL data manipulation

dq07
Deal with common acronymns and more advanced aggregations

dq08
Multi-step queries

dq09
Aggregations with numeric predicates to filter

dq10
Aggregations with categorical predicates to filter

dq11
Schema review

dq12
Aggregate records that are filtered with a predicate requiring a join

dq13
Recognizes truly ambiguous queries.

dq14
Can handle boolean features

dq15
Handles ambiguous column names

dq16
Understands set operates require consideration of overlap

dq17
Finds relevant values inside a Column to answer questions

dq18
Lookup a single record by ID

dq19
Perform an aggregation by a different name and a second query from that

dq20
Perform an aggregation based on a very different question name

dq21
Perform a filter and an unusually-phrased aggregation in the correct order

dq23
Can handle incorrect column names well

dq24
Schema review

dq25
Work with non-literal values

dk01
Column Relevance Determination

fe1
Make a boolean indicator feature for a criteria set

fe2
Make a categorical feature from a criteria set

fe3
Minmax normalization

fe4
Combining two input columns

fe5
Sentiment

fe6
Phrase Identification in Text

fe7
Advanced NLQ

fe8
Advanced NLQ

ii1

ii2
Compare an aggregation for two distinct subsets of data

ii3

ii5
Identifying basic trends on short timelines

ii6
Understands statistical significance

ii7
Understands derivitives

ii8
Can use NLQ feature engineering as part of an insight request

ii9a

ii9b

l1
Can remember the meanings of oddly-named columns

l2
Can remember criteria sets under a single name

v1
Basic Charting

v2
Charting with two series

v3
Categorical Charts