diff --git a/docs/spark-connect-gotchas.md b/docs/spark-connect-gotchas.md
index f1973133d335..b60b0e4feaca 100644
--- a/docs/spark-connect-gotchas.md
+++ b/docs/spark-connect-gotchas.md
@@ -73,7 +73,7 @@ Unlike query execution, Spark Classic and Spark Connect differ in when schema an
 
 # Common Gotchas (with Mitigations)
 
-If you are not careful about the difference between lazy vs. eager analysis, there are four key gotchas to be aware of: 1) overwriting temporary view names, 2) capturing external variables in UDFs, 3) delayed error detection, and 4) excessive schema access on new DataFrames.
+If you are not careful about the difference between lazy vs. eager analysis, there are five key gotchas to be aware of: 1) overwriting temporary view names, 2) capturing external variables in UDFs, 3) delayed error detection, 4) excessive schema access on new DataFrames, and 5) DataFrame column references after a column is shadowed.
 
 ## 1. Reusing temporary view names
 
@@ -418,6 +418,46 @@ println(structColumnFields)
 
 This approach is significantly faster when dealing with a large number of columns because it avoids creating and analyzing numerous DataFrames.
 
+## 5. DataFrame column references after column shadowing
+
+In Spark Connect, a DataFrame column reference such as `df["c"]` is tagged with the plan id of `df`. At analysis time the server resolves the reference by looking for the tagged ancestor in the plan and pulling the matching attribute from it. Spark Classic does not use plan ids; it resolves column references against the immediate child's output by attribute id and name.
+
+The two resolution strategies diverge once a column has been shadowed by another operator that produces an attribute with the same name:
+
+```python
+import pyspark.sql.functions as sf
+
+df = spark.sql("SELECT 1 AS c")
+df.withColumn("c", sf.col("c").cast("string")).select(df["c"]).collect()
+```
+
+`withColumn("c", ...)` does not mutate `df`; it returns a new DataFrame whose `c` is a new attribute that hides the original. The trailing `df["c"]` still refers to the *original* `c` attribute, which is no longer in the projection list.
+
+* **Spark Classic** has always rejected this query at analysis time with `MISSING_ATTRIBUTES.RESOLVED_ATTRIBUTE_APPEAR_IN_OPERATION`, because the original attribute is not present in the operator's child output.
+* **Spark Connect** rejects it with `CANNOT_RESOLVE_DATAFRAME_COLUMN` by default. The plan-id-tagged reference does not match any attribute in the current plan. But when the SQL config `spark.sql.analyzer.strictDataFrameColumnResolution` (added in Spark 4.2.0, default `true`) is set to `false`, the analyzer still tries plan-id-based resolution first, and only when that fails does it fall back to name-based resolution: the tagged `df["c"]` is then resolved by name against the projected `c` from `withColumn`, and the query succeeds.
+
+### Recommended way
+
+If you hit any of the confusing failures mentioned above, it is recommended to switch to `sf.col` first. `sf.col("c")` is an untagged name reference that resolves against the most recent projection or `withColumn`, rather than `df["c"]` which is a tagged reference to `df`'s original column:
+
+```python
+import pyspark.sql.functions as sf
+
+df = spark.sql("SELECT 1 AS c")
+df.withColumn("c", sf.col("c").cast("string")).select(sf.col("c")).collect()
+```
+
+**Scala example:**
+
+```scala
+import org.apache.spark.sql.functions._
+
+val df = spark.sql("SELECT 1 AS c")
+df.withColumn("c", col("c").cast("string")).select(col("c")).collect()
+```
+
+If you cannot change the call sites, set `spark.sql.analyzer.strictDataFrameColumnResolution=false` to opt into the lenient name-based fallback. This is intended as an escape hatch and is not the default.
+
 # Summary
 
 | Aspect                | Spark Classic | Spark Connect                                       |
@@ -428,5 +468,6 @@ This approach is significantly faster when dealing with a large number of column
 | **Schema access**     | Local         | Triggers RPC, and caches the schema on first access |
 | **Temporary views**   | Plan embedded | Name lookup                                         |
 | **UDF serialization** | At creation   | At execution                                        |
+| **DataFrame column references** | Eagerly resolved | Lazily resolved against plan id                     |
 
 The key difference is that Spark Connect defers analysis and name resolution to execution time.