Difference between Seek Predicate and Predicate
Let's throw one million rows into a temp table along with a few columns:

CREATE TABLE #174860 (
PK INT NOT NULL, 
COL1 INT NOT NULL,
COL2 INT NOT NULL,
PRIMARY KEY (PK)
);

INSERT INTO #174860 WITH (TABLOCK)
SELECT RN
, RN % 1000
, RN % 10000
FROM 
(
    SELECT TOP 1000000 ROW_NUMBER () OVER (ORDER BY (SELECT NULL)) RN
    FROM   master..spt_values v1,
           master..spt_values v2
) t;

CREATE INDEX IX_174860_IX ON #174860 (COL1) INCLUDE (COL2);


Here I have a clustered index (by default) on the PK column. There's a nonclustered index on COL1 that has a key column of COL1 and includes COL2.

Consider the following query:

SELECT *
FROM #174860
WHERE PK &gt;= 15000 AND PK &lt; 15005
AND COL2 = 5000;


Here I'm not using BETWEEN because Aaron Bertrand is hanging around this question.

How should SQL Server optimizer that query? Well, I know that the filter on PK will reduce the result set to five rows. SQL server can use the clustered index to jump to those five rows instead of reading through all million rows in the table. However, the clustered index only has the PK column as a key column. Once the row is read into memory we need to apply the filter on COL2. Here, PK is a seek predicate and COL2 is a predicate.



SQL server finds five rows using the seek predicate and further reduces those five rows to one row with the normal predicate.

If I define the clustered index differently:

CREATE TABLE #174860 (
PK INT NOT NULL, 
COL1 INT NOT NULL,
COL2 INT NOT NULL,
PRIMARY KEY (COL2, PK)
);


And run the same query I get different results:



In this case, SQL Server can seek using both columns in the WHERE clause. Exactly one row is read from the table using the key columns.

For one more example consider this query:

SELECT *
FROM #174860
WHERE COL1 = 500
AND COL2 = 3545;


The IX_174860_IX index is a covering index because it contains all of the columns needed for the query. However, only COL1 is a key column. SQL Server can seek with that column to find the 1000 rows with a matching COL1 value. It can further filter down those rows on the COL2 column to reduce the final result set to 0 rows.