Probability Exam Summary

Exam Format:

$\bullet \quad$ Length: 3-hour exam

$\bullet \quad$ Question Type: 30 multiple-choice questions 

$\bullet \quad$ Administration: Computer-based test (CBT)

Exam Purpose:

$\bullet \quad$ Develop fundamental probability knowledge: The primary goal is to build a strong foundation in probability concepts and their application in actuarial science.

$\bullet \quad$ Quantitative risk assessment: The exam emphasizes the use of probability tools for assessing risk in actuarial contexts.

Prerequisites:

$\bullet \quad$ Calculus knowledge: A solid understanding of calculus, including series, differentiation, and integration, is assumed.

$\bullet \quad$ “Risk and Insurance” knowledge: Familiarity with the concepts introduced in the “Risk and Insurance” course is expected.

Exam Resources:

$\bullet \quad$ Normal Distribution Table: A table of values for the normal distribution will be provided during the exam, accessible via an “Exhibit” button in the CBT environment or in hard copy for paper-based exams. Candidates cannot bring their own copies of the table.

Chapters

$\bullet \quad$ Review of Algebra and Calculus – Refreshes essential mathematical concepts needed for probability and statistics.

$\bullet \quad$ Basic Probability Concepts – Introduces fundamental probability concepts like sample spaces, events, and basic probability rules.

$\bullet \quad$ Conditional Probability and Independence – Explores conditional probability and the concept of independent events.

$\bullet \quad$ Combinatorial Principles – Covers counting techniques like permutations and combinations crucial for probability calculations.

$\bullet \quad$ Random Variables and Probability Distributions – Introduces random variables and their associated probability distributions (discrete and continuous).

$\bullet \quad$ Expectation and Other Distribution Parameters – Focuses on expected value, variance, and other key characteristics of distributions.

$\bullet \quad$ Frequently Used Discrete Distributions – Explores several common discrete probability distributions like binomial, Poisson, and geometric.

$\bullet \quad$ Frequently Used Continuous Distributions – Covers important continuous distributions such as normal, exponential, and gamma distributions.

$\bullet \quad$ Joint, Marginal, and Conditional Distributions – Examines the joint behavior of multiple random variables, including concepts like joint, marginal, and conditional distributions.

General Information:

$\bullet \quad$ Weight Ranges: The weights shown in the syllabus are intended to be representative, but the actual weight of each topic on a given exam may vary.

$\bullet \quad$ Multiple Learning Outcomes: Some questions may cover multiple learning objectives.

$\bullet \quad$ Answer Choices: Each multiple-choice question has five answer choices (A-E), with only one correct answer.

Remember: The above blog was made on the basis of information available as of January 2025. Always refer to the official exam guidelines and syllabus for the most up-to-date information.