Operating System Concepts: Processes, Threads, and Scheduling
CH 3 – Processes
Definition:
Process = program in execution (active); program = passive file.
Process layout:
Text (code) | Data (globals) | Heap (dynamic alloc.) | Stack (function frames).
States:
new → ready → running → waiting → terminated.
PCB (Process Control Block):
state, PC, registers, priority, memory info, I/O status, accounting.
Schedulers:
Long-term (job) → admit to memory (degree of multiprogramming); Short-term (CPU) → choose next ready process; Medium-term → swap in/out
You are notified” and “failing which” and “legal proceedings
DOMAIN 4 28%
🔐 4.1 Security Administration on Computing Resources
🔹 Secure Baselines
A baseline is a predefined configuration for a system, meant to represent a “known good state”.
For the exam, understand that this is often created during system deployment and updated after major patches or config changes.
Example questions:
May ask about what’s considered a baseline or how it’s used in hardening.
🔹 Hardening Systems
Key concept:
Reduce attack surface
Disable anything not needed.Common missed
Essential Security Practices for Computing Resources
🧩 4.1 Security Administration on Computing Resources
🔹 Secure Baselines
Define secure configurations (disable unused ports, enforce strong passwords, patch systems).
Deploy via configuration tools (Group Policy, Ansible, SCCM).
Maintain through regular audits and updates.
🔹 System Hardening
| Target | Key Techniques |
|---|---|
| Workstations/Servers | Disable Telnet/FTP; patch OS/apps; use AV + firewall; limit admin privileges. |
| Mobile Devices | Use MDM, enforce encryption, PIN/biometrics, remote wipe, containerization. |
Machine Learning Model Types and Data Preprocessing
Machine Learning Model Types and Descriptions
1. Geometric Models
Geometric models represent data as points in a multidimensional space. Learning involves finding geometric structures like hyperplanes, clusters, or nearest neighbors that can separate or classify the data. These models rely on the distance between data points, vector spaces, and geometric transformations.
- Examples: Linear Classifiers (Perceptron, Logistic Regression, SVM), Nearest Neighbor Classifiers (k-NN), Clustering Models (K-means)
Understanding Independent Component Analysis and Particle Filters
Independent Component Analysis (ICA)
ICA is a statistical technique used to separate a multivariate signal into independent, non-Gaussian components. It assumes the observed data are linear mixtures of independent source signals.
Mathematically, X = A S
where X is the observed mixed signals, A is the unknown mixing matrix, and S is the statistically independent source signals.
The aim is to estimate A and S such that S = W X, where W is the separating matrix.
Steps in ICA
- Centering: Subtract mean to make
Understanding Decision Trees and Ensemble Learning Techniques
Decision Tree: A decision tree is a supervised learning algorithm that splits data into branches based on feature values to make predictions or classifications.
Gini Impurity: Gini impurity measures how often a randomly chosen element would be incorrectly classified; it indicates node impurity in a decision tree.
Nearest Neighbor Method: Classifies a sample based on the majority class of its closest training samples.
Difference Between Boosting and Bagging: Bagging reduces variance by averaging multiple
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