Step 1: What the question asks.
We find what a chained call of isnull() and any() gives on a DataFrame.
Step 2: Think in two stages.
Stage one is a Boolean mask of missing values. Stage two reduces the mask. The reduction in Pandas works column by column unless told otherwise.
Step 3: Stage one.
df.isnull() keeps the shape of df. A cell is True if it holds NaN or None.
Step 4: Stage two.
any() asks, for each column, whether any cell is True. The answer for a column is one Boolean. All columns together make a Series of Booleans.
Step 5: Compare with similar calls.
df.isnull().sum() would give counts. df.isnull().any().any() would give one single Boolean for the full frame. df.isnull().all() would ask if every cell in a column is missing.
Step 6: Match with the options.
Only option 3 describes a Boolean per column.
Step 7: Why each wrong option fails.
Option 1 and option 2 each describe one single message for the whole table, but any() applied once returns a separate answer for every column. Option 4 describes counts, which are not Booleans. A count would come only from sum(). So only option 3 fits the output.
Final Answer:
The result is a True or False for each column, so option 3.
\[ \boxed{3} \]