PL-300 Prepare the data Practice Question
You are merging two queries in Power Query. Query 'Orders' contains columns: OrderID, CustomerID, OrderDate. Query 'Customers' contains columns: CustomerID, CustomerName, Segment. You need to add the CustomerName to the Orders query. The relationship between Orders and Customers is many-to-one. Which join kind should you use?
⚠ Common exam trap
Candidates often confuse Left Outer with Inner join, thinking they must discard non-matching rows to avoid nulls, but the requirement explicitly says to add CustomerName to the Orders query, which implies preserving all orders even if a customer record is missing.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Left Outer
The goal is to retain all rows from the Orders table while adding CustomerName from the Customers table. A Left Outer join returns all rows from the first (left) table and only matching rows from the second (right) table, filling non-matches with null. Since the relationship is many-to-one, each OrderID may have a matching CustomerID, and you want to keep every order even if a customer is missing — exactly what Left Outer does.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Inner
Why it's wrong here
An Inner join in Power Query returns only rows where a CustomerID match exists between the orders and customers queries; any order with a missing or invalid CustomerID is silently discarded. Since the requirement is to preserve the complete list of orders and simply enrich them with customer name, this filter would cause data loss and understate total orders. Thus, Inner cannot satisfy the task because it turns the merge into an intersection rather than an enrichment operation.
- ✓
Left Outer
Why this is correct
Left Outer join (Join Kind = Left Outer) keeps every row from the first query—orders—as the left table, and appends columns from customers only when the join key (CustomerID) matches. For orders that lack a matching customer record, the added customer name column is null, but the order row remains intact. This is the correct choice because the business need is an order-centric view where all orders must appear regardless of whether customer reference data exists.
- ✗
Right Outer
Why it's wrong here
Right Outer join preserves all rows from the second query—customers—and attaches matching order columns; any customer with no orders would appear with null order fields. That direction is the opposite of the requirement, because it would produce a list of all customers, not all orders, and could introduce numerous empty rows for inactive or unmatched customers. Since the goal is to keep all orders and only enrich them with customer details, Right Outer is fundamentally flawed.
- ✗
Full Outer
Why it's wrong here
Full Outer join combines unmatched rows from both sides, so it would not only keep all orders but also create rows for customers who have never placed an order, with nulls in all order fields. These extra customer-only rows are irrelevant to an order-level analysis and would inflate the record count, making subsequent aggregations like order totals incorrect. Additionally, it imposes unnecessary processing overhead by bringing a superset of the data instead of the minimal order-centric subset.
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