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| Section | Objectives |
|---|---|
| Topic 1: Zero Trust Architecture | - NIST SP 800-207 principles - Zero Trust implementation in Falcon Identity Protection - Identity-based risk model |
| Topic 2: Identity Protection Tenets | - Human vs programmatic identities - Identity threat detection concepts - Identity-based attack mitigation |
| Topic 3: Falcon Identity Protection Fundamentals | - Platform components and architecture - Identity risk scoring and baseline behavior - Monitoring, enforcing, exploring, configuring functions |
| Topic 4: Policy & Configuration | - Policy rules enforcement - Authentication and MFA integration - Domain and connector configuration |
| Topic 5: Risk Management & Investigation | - Threat hunting and investigation workflows - User risk assessment - Detection and incident response in identity context |
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NEW QUESTION # 32
Falcon Identity Protection can continuously assess identity events and associate them with potential threats WITHOUTwhich of the following?
Answer: C
Explanation:
Falcon Identity Protection is architected as alog-free identity security platform, a core tenet emphasized throughout the CCIS curriculum. Unlike traditional SIEM- or log-based solutions, Falcon Identity Protection doesnot require string-based queriesto continuously assess identity events or associate them with threats.
Instead, the platform relies onmachine-learning-powered detection rules,real-time authentication traffic inspection, andAPI-based connectorsto collect and analyze identity telemetry directly from domain controllers and identity providers. This approach eliminates the operational complexity of building, tuning, and maintaining query logic.
String-based queries are commonly associated with legacy log aggregation tools and SIEM platforms, where analysts must manually search logs to identify suspicious behavior. Falcon Identity Protection replaces this model withbehavioral baselining and automated correlation, enabling continuous identity risk assessment without human-driven query execution.
Because Falcon does not require string-based queries to operate,Option Dis the correct and verified answer.
NEW QUESTION # 33
Which CrowdStrike documentation category would you search to find GraphQL examples?
Answer: B
Explanation:
GraphQL is the underlying query technology used by multiple CrowdStrike platforms, including Falcon Identity Protection. According to the CCIS curriculum,GraphQL examples are documented under the broader "CrowdStrike APIs" documentation category, not limited to a single product.
The CrowdStrike APIs section includes:
* Authentication and API key usage
* GraphQL schema references
* Example GraphQL queries and mutations
* Pagination, filtering, and response handling
While Identity Protection uses GraphQL for identity-specific queries, the examples themselves are centralized underCrowdStrike APIsto provide consistency across Falcon modules. Product-specific use cases are then layered on top of these core examples.
The other options are incorrect:
* Threat Intelligence focuses on adversary data.
* XDR covers detection and correlation concepts.
* Identity Protection APIs describe endpoints and permissions, not general GraphQL usage examples.
Therefore,Option Ais the correct and verified answer.
NEW QUESTION # 34
Where would a Falcon administrator enable authentication traffic inspection (ATI) for Domain Controllers?
Answer: B
Explanation:
Authentication Traffic Inspection (ATI) is a foundational capability of Falcon Identity Protection that enables the platform to analyze authentication traffic from domain controllers. According to the CCIS documentation, ATI is enabled throughIdentity configuration policies.
Identity configuration policies define how the Falcon sensor captures and inspects authentication-related traffic, including Kerberos, NTLM, LDAP, and other identity protocols. Enabling ATI at this level ensures that domain controllers provide the necessary telemetry for identity risk analysis, detections, and behavioral profiling.
The other options are incorrect because:
* Identity management settings focus on identity governance and administration.
* Identity detection configuration controls detection logic, not traffic inspection.
* Identity protection settings manage high-level configuration but do not directly enable ATI.
Because ATI must be explicitly enabled viaIdentity configuration policies,Option Ais the correct and verified answer.
NEW QUESTION # 35
To enforce conditional access policies with Identity Verification, an MFA connector can be configured for different authentication methods such as:
Answer: C
Explanation:
Falcon Identity Protection integrates with third-party MFA providers throughMFA connectorsto support conditional access and identity verification. The CCIS documentation explains that these connectors allow organizations to enforce MFA challenges based on identity risk, authentication behavior, or policy conditions.
One of the supported MFA authentication methods isPush, where a notification is sent to a registered device or application for user approval. Push-based MFA is widely used due to its balance of usability and security and is fully supported by Falcon Identity Protection when integrated with compatible MFA providers.
The other options are not valid MFA authentication methods within Falcon:
* Page and Pull are not recognized MFA mechanisms.
* Alarm is related to alerting, not authentication.
By enabling push-based MFA through an MFA connector, organizations can dynamically enforce identity verification in alignment with Zero Trust principles. Therefore,Option Bis the correct and verified answer.
NEW QUESTION # 36
How long does it typically take Falcon Identity to develop a baseline of a user?
Answer: C
Explanation:
Falcon Identity Protection establishes auser baselineby observing authentication behavior over time, including login frequency, endpoints used, access patterns, and protocol usage. According to the CCIS curriculum, Falcon typically requiresapproximately one weekof consistent activity to develop an initial, reliable baseline for a user.
This baseline allows Falcon to distinguish normal behavior from anomalies and to calculate accurate risk scores. While the baseline continues to mature over time and becomes more precise with additional data, the first usable behavioral model is generally formed within a week.
Longer timeframes such as one or three months are not required to begin detecting abnormal behavior.
Conversely, periods shorter than a week may not provide sufficient behavioral data to accurately model normal usage patterns.
Because Falcon can rapidly establish a functional baseline while continuously refining it,Option C (One week)is the correct and verified answer.
NEW QUESTION # 37
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