Additional SQL double query (or sub-query) questions (practical or theory, its up to you) based on the 2020 assignment "Car Services" database. Marking Instructions and the original Access file (with and without answers) are included.
Additional questions (practical or theory, its up to you) based on the 2019 assignment "Flights" database. Marking Instructions and the original Access file (with and without answers) are included.
Created as another tutoring task, I've written some SQL tasks to accompany the 2022 assignment database, WestFifeWalkers.
This set of questions all require one of the aggregate functions (MIN, MAX, AVG, SUM & COUNT) to be used.
Finally got round to completing this. Solutions for every assignment from the specimen(2018) to 2022 using both parallel arrays and arrays of records (dataclass).
Note that 2020 does not have a dataclass equivalent because I felt the data flow of the task isn't easily converted to an array of records.
Three refinement tasks that continue the scenarios in the previous data flow tasks. The refinements focus on the standard algorithms with the occasional twist to provide some A type marks.
Two assessment/homework/class exercises covering data flow in the top level of an algorithm. This is a topic that historically isn't done as well as it could be in the assignment and exam paper.
A couple of homework/class test exercises for binary to floating-point conversion and back. Started tutoring again so other tasks for Higher may follow.
Higher exam style qs
I couldn't find ppts fopr the Higher SDD booklet so I made some and have a past paper ppt based on SDD
Same tasks as posted by Greig earlier with some more corrections to the MI, and exemplar code in Visual Basic!
I've created this resource based off of one of the recent SDD coursework assignments. Feel free to modify it or make suggestions on it.
Software questions based around writing code questions. I've included the Python file that I wrote to produce the screenshots in the marking instructions. The code starts by generating data for the four arrays in the questions. For other languages you'd have to write code to replace the Python in the MIs. The questions can be used with any language. The questions could easily be reworded to become design questions.
Six more questions this time using alias, wildcards and calculations. Note that you'll need the data sheet from task 2 to complete this.
SELECT with multiple tables
This task revisits N5 output but with the complexity of more tables (and joins).
As there is a large amount of sample table data I've separated this out into a single sheet that should be used for tasks 2 to 6.
Note that dates on the sample data are in SQL format. These should be changed if you use MS Access or similar.
Students are told of further analysis of the problem and flaws with the original design (from the N5 tasks). They are required to draw a new ERD to correct the flaws.
This is quite an involved task. If anyone spots a flaw in the marking instructions and solutions I'd appreciate if you let me know.
The attached zip file contains the database for the Higher specimen courswork task in SQLite format and all the .SQL commands and CSVs to recreate it, if necessary.
Tutoring Task 3 - This is a bit a repeat of number 2 but using dataclass to simulate an array of records as required by the Higher course. The zipped file includes an example of dataclass and the task files. The titanic file has been fixed if you wish to use it in Tutoring Task 2.
Tutoring task number 2 - Three tasks (with lots of sub standard algorithm tasks) around file handling using parallel arrays (lists).
Some of the sub-tasks are straight forward standard algorithm tasks, some are more complex twists on the standard algorithms that would usually end up being A marks in the assignment.
I've started tutoring again. Doing some Python with the son of a previous colleague of my wife's. As such I'll probably produce a few daft wee tasks. This one reads in details about powerplants into four parallel arrays. The task is to use the four parallel arrays (lists), used to store the file data, to produce a variety of output.
Details are in the Python file as comments.