Week of 13 July
Baseline reflection, Alex.
Rate each reflection point once using the exam readiness scale. After each section, choose how confident you are that the ratings are accurate.
26/26 readiness ratings · 6/6 module confidence ratings
32of 32
Saved selectionsVisible baseline complete5 points flagged as uncertain
Exam Readiness score
What is the highest level you could currently demonstrate without help?
Use the same 0-4 scale for every point. Choose the highest level you could demonstrate today without hints, notes or teacher support.
I cannot demonstrate this yet.
I can recognise the idea and describe it with support.
I can complete a familiar task without help.
I can transfer this to an unfamiliar exam stimulus.
I can design, refine, evaluate or justify a response.
Reflection overview
Digital Solutions reflection focuses on secure data, networks, algorithms, data systems and unseen problem-solving for the external assessment.
Section 1
Secure Data
Authentication, encryption, hashing, secure transmission and privacy impacts.
Module average3.8 / 4
1
Authentication and MFA
Explain authentication, 2FA, MFA, verification codes and biometrics.
2
Symmetric and Asymmetric Encryption
Compare DES, Triple DES, AES, Blowfish, Twofish and RSA, and recommend an approach.
3
Hashing, Checksums and Compression
Explain their purposes, limitations and roles in data transmission.
4
CIA and Secure Transmission
Explain how authentication, encryption, hashing and checksums work together.
5
Privacy, Data Ownership and Impacts
Apply privacy principles and evaluate personal, social and economic impacts.
Section 2
Classical Ciphers
Classical encryption processes, tracing, comparison and refinement.
Module average3.3 / 4
6
Caesar Cipher
Encrypt, decrypt, trace and identify errors.
7
Vigenere and Gronsfeld Ciphers
Use keys, calculate shifts, trace and refine algorithms.
8
One-Time Pad
Explain its process, requirements, strengths and vulnerabilities.
9
Cipher Comparison and Refinement
Compare security, identify weaknesses and justify an appropriate cipher.
Section 3
Networks and APIs
Network performance, secure protocols, packet delivery, APIs and data exchange.
Module average3.0 / 4
10
Network Performance and QoS
Apply latency, jitter, timeliness, reliability and QoS to scenarios.
11
Protocols and Secure Connections
Explain and compare TCP/IP, HTTP, FTP and VPN.
12
Packet Delivery and Network Control
Explain packet switching, routing, forwarding, error control, flow control and congestion control.
13
REST APIs and Data Exchange
Explain requests, responses, endpoints and external data sources.
14
JSON and XML
Interpret structures, identify fields and identify invalid or missing data.
15
Front-End and Back-End Data Processing
Explain how subsystems receive, process, store and display data.
Section 4
Algorithms and Code Quality
Programming constructs, tracing, validation, debugging, testing and quality.
Module average3.2 / 4
16
Variables, Types and Operators
Use assignment, types, conversion, arithmetic, comparison and logical operators.
17
Selection and Iteration
Trace and design simple, compound and nested control structures.
18
Collections, Scope and Modularisation
Use arrays, lists, local and global variables, functions, procedures and parameters.
19
Tracing, Validation and Debugging
Desk check code, predict output, identify errors and add validation.
20
Code Quality and Testing
Evaluate efficiency, reliability, maintainability, accuracy, portability and simplicity.
Section 5
Data Systems
Data-flow diagrams, SQL schemas, modification and queries.
Module average3.0 / 4
21
DFD Interpretation
Identify entities, processes, stores and correctly labelled data flows.
22
DFD Construction and Refinement
Construct context and detailed DFDs from unseen stimulus.
23
SQL Schemas and Data Modification
Interpret relationships and use CREATE, DROP, ALTER, INSERT and UPDATE.
24
SQL Queries
Use SELECT, WHERE, GROUP BY, HAVING, ORDER BY, joins, aggregates and subqueries.
Section 6
Unseen Problem Solving
Problem analysis, low-fidelity design, evaluation and recommendations.
Module average3.0 / 4
25
Problem Analysis and Low-Fidelity Design
Identify scope, constraints, risks, data requirements, components and success criteria.
26
Evaluation, Emerging Technologies and Recommendations
Evaluate solutions, recommend refinements and explain AI-related applications and impacts.