The data governance team is evaluating code for GDPR compliance regarding record deletion. The following logic is used to propagate delete requests from the user_lookup table to the user_aggregates table:

(spark.read
    .format("delta")
    .option("readChangeData", True)
    .option("startingTimestamp", '2021-08-22 00:00:00')
    .option("endingTimestamp", '2021-08-29 00:00:00')
    .table("user_lookup")
    .createOrReplaceTempView("changes"))

spark.sql("""
    DELETE FROM user_aggregates
    WHERE user_id IN (
        SELECT user_id
        FROM changes
        WHERE _change_type = 'delete'
    )
""")

(spark.read
    .format("delta")
    .option("readChangeData", True)
    .option("startingTimestamp", '2021-08-22 00:00:00')
    .option("endingTimestamp", '2021-08-29 00:00:00')
    .table("user_lookup")
    .createOrReplaceTempView("changes"))

spark.sql("""
    DELETE FROM user_aggregates
    WHERE user_id IN (
        SELECT user_id
        FROM changes
        WHERE _change_type = 'delete'
    )
""")

Assuming user_id is a unique key and all users requesting deletion have been removed from user_lookup, does successfully executing this logic ensure that the records deleted from user_aggregates are no longer accessible? Explain why.