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ACAMS CAMS7 Exam Syllabus Topics:

SectionWeightObjectives
Tools, Technologies and Investigations22%- Technology and systems
  • 1. Cryptoasset and virtual asset compliance
  • 2. Monitoring systems, screening tools, data analytics
- Remediation and response
  • 1. Corrective actions and regulatory engagement
- Investigation processes and evidence handling
  • 1. Cooperation with authorities and information sharing
  • 2. Case management and escalation
Building and Managing Anti-Financial Crime Compliance Programs28%- Audit, testing and continuous improvement
- Monitoring, reporting and record keeping
  • 1. Legal record retention requirements
  • 2. Transaction monitoring and alert management
  • 3. Suspicious activity reporting (SAR/STR)
- Training, awareness and internal communication
- Customer due diligence and know-your-customer
  • 1. CDD, EDD, beneficial ownership identification
  • 2. politically exposed persons and high-risk clients
- Program framework and policies
  • 1. Policies, procedures and controls design
  • 2. Risk assessment and risk-based approach
Global Anti-Financial Crime Frameworks, Governance and Regulations24%- Governance, accountability and ethics
  • 1. Roles of board, senior management and compliance function
  • 2. Ethical obligations and professional standards
- Regional and national regulations
  • 1. EU AML Directives
  • 2. USA PATRIOT Act and extraterritorial laws
- International standards and bodies
  • 1. Basel Committee, Wolfsberg Group, UN conventions
  • 2. FATF 40 Recommendations
Understanding Risks and Methods of Financial Crime26%- Sanctions regimes and compliance risks
  • 1. Risk identification and mitigation
  • 2. UN, EU, OFAC frameworks
- Money laundering stages and techniques
  • 1. Methods across financial sectors and businesses
  • 2. Placement, layering, integration
- Terrorism financing and proliferation financing
  • 1. Distinctions from money laundering
  • 2. Sources and mechanisms
- Financial crime risks and consequences
  • 1. Institutional, individual, economic and social impacts

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ACAMS Certified Anti-Money Laundering Specialist (CAMS7 the 7th edition) Sample Questions (Q57-Q62):

NEW QUESTION # 57
Which of the following attributes would enhance an AML program's effectiveness?

Answer: B

Explanation:
Effective AML programs promote aculture of compliance, which includes an environment where staff are empowered to raise concerns, challenge decisions, and continuously improve through cross-functional learning.Providing effective challengeis a supervisory expectation and best practice in institutions that value AML governance integrity.
While AML officer authority (option B) supports oversight, it is not as impactful on daily operational effectiveness as fostering a challenging and adaptive compliance culture.
Reference: ACAMS CAMS Study Guide - 6th Edition, Chapter: AML Program Components - Section:
Culture of Compliance


NEW QUESTION # 58
Money laundering can have a profound impact on the global economy that includes:

Answer: A

Explanation:
Money laundering has far-reaching consequences that extend beyond individual institutions and directly affect national and global economic stability. FATF and international bodies consistently highlight that large-scale money laundering can undermine political and economic systems.
One of the most significant impacts is the destabilization of legitimate governments. Money laundering enables organized crime, corruption, and terrorist financing, which can weaken public institutions, distort policy-making, and erode public trust. In extreme cases, criminal organizations gain influence over governments, law enforcement, or key economic sectors.
While money laundering can indirectly affect inflation and growth, it does not directly cause declining money supplies. "Sectoral polarization" is not a standard or widely recognized economic impact in AML literature. The most consistently recognized systemic consequence is the erosion of governance, rule of law, and institutional integrity.
Therefore, destabilization of legitimate governments is the most accurate and regulator-supported answer.


NEW QUESTION # 59
There are three types of factors that can be used to authenticate someone ownership, knowledge, and inherent factors. Which of the following factors falls under inherent factors?

Answer: B

Explanation:
Fingerprint is an inherent factor because it is a biometric trait - something the user is. Inherent factors rely on physical or behavioral characteristics such as fingerprints, facial recognition, or voice, which are unique to the individual.


NEW QUESTION # 60
A subset of financial technology (FinTech) that focuses on technologies designed to facilitate the delivery of regulatory requirements more efficiently and effectively than existing capabilities is known as:

Answer: D

Explanation:
Regulatory Technology (RegTech) is a specialized subset of FinTech that focuses on improving how organizations meet regulatory and compliance obligations, including AML/CFT requirements. RegTech solutions leverage technologies such as automation, data analytics, artificial intelligence, and machine learning to enhance efficiency, accuracy, and scalability.
In the AML context, RegTech supports functions such as customer due diligence (CDD), transaction monitoring, sanctions screening, regulatory reporting, and record-keeping. These tools help financial institutions reduce manual processes, improve detection of suspicious activity, and respond more quickly to regulatory changes.
SupTech, by contrast, refers to supervisory technology used by regulators, not regulated institutions. The other options are not recognized regulatory terms.
Global regulators, including FATF and national supervisory authorities, actively encourage the responsible adoption of RegTech as part of a risk-based approach, provided appropriate governance, data quality, and model oversight are in place.


NEW QUESTION # 61
Which of the following is a benefit of an institution implementing machine learning to identify suspicious transactions?

Answer: D

Explanation:
A key benefit of machine learning in identifying suspicious transactions is its adaptability to new anti- financial crime typologies through continuous model training, enabling the system to evolve with emerging threats and patterns.


NEW QUESTION # 62
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