Power Query interview questions

1. A.the case-sensitive functional formula language that records and powers data import and transformation logic
2. B.the distinction between a query that points at another query's output versus a standalone independent copy of its steps
3. C.the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
4. D.a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume

Answer + AI explanation with Pro

1. A.Unpivot
2. B.M language
3. C.Applied steps
4. D.Query folding

Answer + AI explanation with Pro

1. A.M language — the ordered, replayable list of transformations recorded for a query, each editable or removable individually
2. B.M language — the case-sensitive functional formula language that records and powers data import and transformation logic
3. C.M language — the distinction between a query that points at another query's output versus a standalone independent copy of its steps
4. D.M language — a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout

Answer + AI explanation with Pro

1. A.the ordered, replayable list of transformations recorded for a query, each editable or removable individually
2. B.the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
3. C.the distinction between a query that points at another query's output versus a standalone independent copy of its steps
4. D.a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume

Answer + AI explanation with Pro

1. A.M language
2. B.Unpivot
3. C.Query folding
4. D.Applied steps

Answer + AI explanation with Pro

1. A.Applied steps — the case-sensitive functional formula language that records and powers data import and transformation logic
2. B.Applied steps — the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
3. C.Applied steps — the ordered, replayable list of transformations recorded for a query, each editable or removable individually
4. D.Applied steps — a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout

Answer + AI explanation with Pro

1. A.a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume
2. B.the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
3. C.the distinction between a query that points at another query's output versus a standalone independent copy of its steps
4. D.the ordered, replayable list of transformations recorded for a query, each editable or removable individually

Answer + AI explanation with Pro

1. A.Query folding
2. B.M language
3. C.Reference vs Duplicate
4. D.Dataflow

Answer + AI explanation with Pro

1. A.Query folding — a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume
2. B.Query folding — the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
3. C.Query folding — the distinction between a query that points at another query's output versus a standalone independent copy of its steps
4. D.Query folding — a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout

Answer + AI explanation with Pro

1. A.a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout
2. B.the ordered, replayable list of transformations recorded for a query, each editable or removable individually
3. C.the distinction between a query that points at another query's output versus a standalone independent copy of its steps
4. D.a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume

Answer + AI explanation with Pro

1. A.Applied steps
2. B.M language
3. C.Reference vs Duplicate
4. D.Unpivot

Answer + AI explanation with Pro

1. A.Reference vs Duplicate — the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
2. B.Reference vs Duplicate — the ordered, replayable list of transformations recorded for a query, each editable or removable individually
3. C.Reference vs Duplicate — a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume
4. D.Reference vs Duplicate — the distinction between a query that points at another query's output versus a standalone independent copy of its steps

Answer + AI explanation with Pro

1. A.the ordered, replayable list of transformations recorded for a query, each editable or removable individually
2. B.the distinction between a query that points at another query's output versus a standalone independent copy of its steps
3. C.a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout
4. D.a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume

Answer + AI explanation with Pro

1. A.Dataflow
2. B.Applied steps
3. C.Reference vs Duplicate
4. D.Unpivot

Answer + AI explanation with Pro

1. A.Dataflow — the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
2. B.Dataflow — the ordered, replayable list of transformations recorded for a query, each editable or removable individually
3. C.Dataflow — a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume
4. D.Dataflow — the case-sensitive functional formula language that records and powers data import and transformation logic

Answer + AI explanation with Pro

1. A.the optimization where transformation logic is translated back into a native source query so work runs at the database instead of locally
2. B.a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout
3. C.the distinction between a query that points at another query's output versus a standalone independent copy of its steps
4. D.the case-sensitive functional formula language that records and powers data import and transformation logic

Answer + AI explanation with Pro

1. A.Query folding
2. B.Dataflow
3. C.Unpivot
4. D.Reference vs Duplicate

Answer + AI explanation with Pro

1. A.Unpivot — the case-sensitive functional formula language that records and powers data import and transformation logic
2. B.Unpivot — a transformation that turns columns into attribute-value row pairs to reshape wide data into a tall normalized layout
3. C.Unpivot — a reusable cloud-stored ingestion artifact that runs Power Query online and saves results for multiple datasets to consume
4. D.Unpivot — the ordered, replayable list of transformations recorded for a query, each editable or removable individually

Answer + AI explanation with Pro