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[General] Zscaler ZDTE Exam Made Easy: UpdateDumps's 3 User-Friendly Questions Formats

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【General】 Zscaler ZDTE Exam Made Easy: UpdateDumps's 3 User-Friendly Questions Formats

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Zscaler Digital Transformation Engineer Sample Questions (Q14-Q19):NEW QUESTION # 14
What are the four distinct stages in the Cloud Sandbox workflow?
  • A. Pre-Filtering # Behavioral Analysis # Post-Processing # Cloud Effect
  • B. Pre-Filtering # Cloud Effect # Behavioral Analysis # Post-Processing
  • C. Behavioral Analysis # Post-Processing # Engage your SOC Team for further investigation
  • D. Cloud Effect # Pre-Filtering # Behavioral Analysis # Post-Processing
Answer: D
Explanation:
Zscaler Cloud Sandbox is described in Zscaler threat-protection training as following a four-stage workflow.
The documented order is: Cloud Effect, Pre-Filtering, Behavioral Analysis, and Post-Processing.
* Cloud Effect - Before detonation, files are checked against global threat intelligence and prior sandbox verdicts so that known malicious objects can be immediately blocked, and known benign files can be allowed without re-analysis.
* Pre-Filtering - Static and signature-based checks (antivirus, file heuristics, and related engines) quickly discard clearly malicious or clearly safe files, reducing load on deep analysis.
* Behavioral Analysis - Suspicious or unknown samples are executed in a virtual environment to observe behavior such as process spawning, registry changes, or C2 activity.
* Post-Processing - Final verdicts are generated, policies are enforced (block, quarantine, allow), and new indicators are fed back into threat intelligence for future Cloud Effect decisions.
This exact ordered sequence-Cloud Effect # Pre-Filtering # Behavioral Analysis # Post-Processing-is what appears in ZDTE study material, so option C is correct.

NEW QUESTION # 15
What capabilities within Zscaler External Attack Surface Management (EASM) are specifically designed to uncover and assess domains that are intentionally created to resemble your legitimate brand or websites?
  • A. Mimic Domains
  • B. Spoofing Domains
  • C. Lookalike Domains
  • D. Fake Domains
Answer: C
Explanation:
Zscaler External Attack Surface Management (EASM) includes a dedicated capability called Lookalike Domains. Zscaler defines lookalike domains as fraudulent or fake domains intentionally created by threat actors to mimic your legitimate domains and brand presence, often for phishing, credential theft, or brand abuse.
Within the EASM portal, the Lookalike Domains pages and widgets present a curated list of suspicious domains that closely resemble your seed or official domains. Analysts can review exposure scores, registrar details, hosting information, and other attributes to determine which of these domains pose the highest risk and warrant takedown or additional monitoring.
This feature is specifically designed for external risk and brand-protection use cases: it highlights where attackers are impersonating your organization on the public internet, which is a core component of digital-risk and external-attack-surface management. While words such as "fake," "mimic," or "spoofing" may be used generically in security discussions, "Lookalike Domains" is the exact term and feature name Zscaler uses in the EASM product and documentation. Options A, B, and C do not correspond to a named EASM capability and therefore are not correct in the ZDTE context.

NEW QUESTION # 16
How can Zscaler ThreatParse, in conjunction with information about the MITRE ATTandCK framework, assist security analysts in determining the attacker's objectives?
  • A. It provides suggestions on risk management strategies provided by the framework.
  • B. It conducts natural language reconstruction of attacks by summarizing and translating log information into plain English.
  • C. It prioritizes the log information according to the latest campaign in the MITRE ATTandCK framework.
  • D. It maps into the framework to evaluate the probability of a financial loss.
Answer: B
Explanation:
ThreatParse is part of Zscaler's advanced cyberthreat analysis capabilities, used primarily within Zscaler Deception and related SecOps workflows. Zscaler describes ThreatParse as an investigative engine that takes raw attack or event logs and "reconstructs" the attack sequence, summarizing what happened and translating the data into plain, human-readable language so even junior analysts can quickly understand the incident.
In addition, ThreatParse enriches these reconstructed attacks with structured information tied to the MITRE ATTandCK framework, including tactic and technique identifiers plus an associated risk score. This linkage helps analysts recognize why the attacker is performing certain actions (for example, credential access, lateral movement, or data exfiltration) rather than just what they did.
By combining natural-language reconstruction with MITRE ATTandCK context, ThreatParse effectively turns low-level events into a clear narrative aligned with attacker tactics and objectives. Analysts can quickly see which stage of the kill chain the adversary is in, the severity of the behavior, and which threats demand immediate attention. Options B and C are incorrect because ThreatParse does not perform financial-loss modeling or generic risk-management recommendations; option D is inaccurate because its primary value is narrative reconstruction plus ATTandCK mapping and risk scoring, not simply prioritizing logs by "latest campaign."

NEW QUESTION # 17
A customer requires 2 Gbps of throughput through the GRE tunnels to Zscaler. Which is the ideal architecture?
  • A. Two primary and two backup GRE tunnels from internal routers with NAT disabled
  • B. Two primary and two backup GRE tunnels from internal routers with NAT enabled
  • C. Two primary and two backup GRE tunnels from border routers with NAT disabled
  • D. Two primary and two backup GRE tunnels from border routers with NAT enabled
Answer: C
Explanation:
Zscaler design guidance for GRE connectivity emphasizes three key principles: terminate GRE on border (edge) devices, avoid NAT on GRE source addresses, and scale bandwidth by using multiple tunnels. In Zscaler documentation and engineering training, each GRE tunnel is typically sized for up to about 1 Gbps of throughput. For a 2 Gbps requirement, customers are advised to deploy at least two primary GRE tunnels, with two additional backup tunnels for redundancy and failover.
These tunnels should terminate on border routers that own public IP addresses, ensuring optimal routing and simplifying troubleshooting. Zscaler specifically recommends that the public source IPs used for GRE must not be translated by NAT, because the Zscaler cloud must see the original, registered public IP to associate tunnels with the correct organization and enforce policy. Enabling NAT on GRE traffic can break tunnel establishment and lead to asymmetric or unpredictable routing.
Using internal routers introduces extra hops and complexity and often requires NAT or policy-based routing, which goes against recommended best practices. Similarly, any architecture with NAT enabled on GRE traffic conflicts with Zscaler's published requirements. Therefore, the ideal and recommended design for 2 Gbps via GRE is two primary and two backup GRE tunnels from border routers with NAT disabled.

NEW QUESTION # 18
What is Zscaler Deception?
  • A. A set of decoys representing users and server elements used to identify an attacker accessing our infrastructure.
  • B. An early detection system supported via servers located inside our corporate infrastructure.
  • C. A set of decoys representing network elements used to identify an attacker accessing our infrastructure.
  • D. A simple and more effective targeted threat detection solution built on the Zscaler Zero Trust architecture.
Answer: D
Explanation:
In the Zscaler Digital Transformation Engineer material, Zscaler Deception is introduced as an advanced threat-detection capability that is tightly integrated with the Zero Trust Exchange. The official description emphasizes that it is a simple, cloud-delivered, and highly effective targeted threat detection solution built on Zscaler's Zero Trust architecture, which is almost word-for-word reflected in option C.
Deception works by deploying high-fidelity decoys, lures, and credentials-designed to be indistinguishable from real assets-from the attacker's point of view. Any interaction with these decoys is inherently suspicious, yielding high-confidence, low-noise alerts that help security teams quickly identify lateral movement, credential theft, and post-compromise activity. The key point in the training is that this capability is delivered from the Zscaler cloud, leveraging the existing Zero Trust platform; it does not require additional on-premise detection servers or traditional network-centric sensors.
Options A and B reduce the concept to "sets of decoys" and ignore the integrated Zero Trust detection value and cloud-native delivery model. Option D incorrectly suggests on-prem server infrastructure as the foundation. The exam materials clearly frame Zscaler Deception as a Zero Trust-based targeted threat detection solution, making option C the correct choice.

NEW QUESTION # 19
......
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