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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Real Exam Qty: | 90 |
| Exam Duration: | 150 minutes |
| Exam Price: | US$459 (members), US$599 (non-members) |
| Related Certifications: | CISM CISSP |
| Exam Format: | Multiple-choice, Scenario-based |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Passing Score: | 450 (out of 800) |
| Recommended Training: | ISACA Official AAISM Training |
| Exam Registration: | ISACA AAISM Registration |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Computer-based; available at authorized PSI testing centers or via remote proctoring |
| Pre Condition: | Hold an active CISM or CISSP certification |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline |
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NEW QUESTION # 173
Which AI data management technique involves creating validation and test data?
Answer: A
Explanation:
AAISM describes data splitting as the process of dividing datasets into:
* training
* validation
* test sets
This is essential for reducing overfitting and ensuring robust evaluation.
Learning (A) refers to model training. Annotating (D) labels data. Training (C) does not create validation/test data.
References: AAISM Study Guide - AI Data Preparation & Dataset Splitting.
NEW QUESTION # 174
An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk team?
Answer: A
Explanation:
AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testing and validation help evaluate risks but do not directly minimize hallucinations. Training on larger datasets may improve generalization but does not guarantee accuracy. Developer training in AI risk supports governance but is not a technical control against hallucinations. The best mitigation is fine-tuning to align the chatbot with trusted, context-specific knowledge.
NEW QUESTION # 175
Which of the following is an information security manager's BEST recommendation when the new release of a proprietary AI model is generating inconsistent output?
Answer: B
Explanation:
Reverting to a prior stable version is the best immediate recommendation because inconsistent output from a new model release indicates potential instability or unintended behavior. Restoring the previously validated version minimizes operational and security risk while the issue is investigated and corrected.
NEW QUESTION # 176
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:
Answer: A
Explanation:
AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing.
Automated rules are a signature-based paradigm and do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.
NEW QUESTION # 177
Embedding unique identifiers into AI models would BEST help with:
Answer: D
Explanation:
The AAISM framework explains that embedding unique identifiers--such as digital watermarks or model fingerprints--enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.
NEW QUESTION # 178
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