Palo Alto Networks PSE-Prisma Cloud Exam Dumps & Practice Test Questions

Question 1: 

Which two prominent cloud-native infrastructure providers are officially supported by Prisma Cloud for comprehensive security and compliance management? (Select two options.)

A. DigitalOcean 

B. Azure 

C. IBM Cloud 

D. Oracle Cloud

Answer: B, D

Explanation: 

Prisma Cloud, a leading Cloud-Native Security Platform (CNSP) developed by Palo Alto Networks, is designed to offer robust security and compliance capabilities across diverse cloud environments. Its architecture emphasizes comprehensive multi-cloud support, allowing organizations to establish and maintain a consistent and unified security posture irrespective of the underlying cloud service provider. This extensive compatibility ensures that enterprises can secure their workloads, applications, and data as they expand their presence across different cloud platforms.

Among the options provided, Prisma Cloud offers robust and well-established integrations with two major cloud-native providers: Azure (Microsoft Azure): Prisma Cloud provides deep and comprehensive integration with Microsoft Azure. This includes granular visibility into Azure resources, such as virtual machines, storage accounts, networking components, and resource hierarchies. It also facilitates continuous compliance monitoring against various regulatory benchmarks and custom security policies specific to Azure environments. This integration allows organizations to effectively monitor, protect, and manage the security posture of their Azure deployments from a centralized platform.

Conversely, some cloud providers are not currently listed as directly supported by Prisma Cloud for native, deep integration at the same level as Azure or OCI. For instance: DigitalOcean: While a popular cloud service provider, there is no public documentation or common knowledge indicating that Prisma Cloud offers direct, out-of-the-box integration or comprehensive support for DigitalOcean's infrastructure within its native CNSP capabilities. IBM Cloud: Similarly, IBM Cloud is not typically featured among the primary hyperscale cloud service providers for which Prisma Cloud offers native, deep security and compliance monitoring.

Given this information, the two cloud-native providers that Prisma Cloud actively supports are Microsoft Azure and Oracle Cloud Infrastructure, enabling consistent security management across these environments.

Question 2: 

Considering these configurations, what specific outcome should be expected when this image is attempted to be deployed from the CI tool into the sock-shop namespace?

A. The image will pass the CI policy but will be blocked by the deployed policy; therefore, it will not be deployed. 

B. The CI policy will fail the build; therefore, the image will not be deployed. 

C. The image will be deployed successfully, and all vulnerabilities will be reported. 

D. The image will be deployed successfully, but no vulnerabilities will be reported.

Answer: C. The image will be deployed successfully, and all vulnerabilities will be reported.

Explanation: 

In a typical enterprise environment leveraging Prisma Cloud for vulnerability management, policies are meticulously configured to manage how container images with identified vulnerabilities are handled at various stages of the CI/CD pipeline, including build time (CI tool) and deployment time (registry and runtime). These policies allow for granular control, such as triggering alerts, blocking builds, or preventing deployments, based on the severity of the vulnerabilities detected.

In the described scenario, the image contains medium-severity vulnerabilities with no available fixes. Organizations often adopt pragmatic security policies to balance security with operational velocity, especially when immediate remediation is not possible. For medium-severity vulnerabilities, and particularly when no fixes are available, a common best practice is to configure CI policies to alert rather than to outright block the build. This approach ensures that development teams are made aware of potential issues without completely halting the development or deployment process. 

Furthermore, the deployed policy, which governs the behavior of running containers and often acts as a last line of defense, is frequently configured with a similar pragmatic approach for medium-severity issues. It typically allows deployment while continuing to monitor and report on these vulnerabilities. This strategy ensures continuous visibility into the security posture of running applications.

Therefore, given these common configurations and best practices for managing medium-severity vulnerabilities without available fixes:

• The CI policy will not fail the build. Instead, it will likely generate an alert, allowing the build to pass.

• Consequently, the image will be deployed successfully into the sock-shop namespace.

• Crucially, Prisma Cloud will report all detected vulnerabilities. This reporting mechanism provides essential visibility to both security and development teams, allowing them to track the vulnerability, assess its actual risk in the deployed environment, and plan for potential future remediation or mitigation strategies when fixes become available.

This approach aligns with the principle of "fail-fast, but don't stop everything," ensuring that critical security information is disseminated while maintaining operational continuity for issues that are not immediately critical or addressable.

Question 3: 

Which statement accurately describes the strategic benefit and application of optimizing registry scans by utilizing version pattern matching within Prisma Cloud?

A. It requires Linux images to rely on optimizing registry scans due to various Linux elements. 

B. It is only necessary in registries with tens of thousands of repositories and millions of images. 

C. It is best practice to always optimize registry scans for faster results. 

D. It is rarely successful in the Windows Operating System (OS).

Answer: C

Explanation: 

Registry scanning is an indispensable component of an effective vulnerability management strategy within cloud-native security platforms like Prisma Cloud. As container registries continue to grow exponentially in terms of both the number of repositories and the sheer volume of image versions, the efficiency of these scans becomes paramount. Prisma Cloud addresses this challenge by supporting various optimization techniques, one of which is version pattern matching. This technique allows for highly targeted scanning of container image versions that adhere to specific naming conventions or tags, thereby streamlining the entire scanning process.

What is Version Pattern Matching? Version pattern matching is a sophisticated filtering mechanism that enables administrators to define precisely which images within a registry should be scanned, based on their version strings or tagging conventions (e.g., v1.*, release-*, *-latest). This intelligent filtering prevents Prisma Cloud from undertaking resource-intensive and often redundant scans of every single version of every image, many of which might be historical, superseded, or simply unused. This functionality can be seamlessly integrated with both cron-based scheduled scans and event-driven scanning within continuous integration/continuous delivery (CI/CD) pipelines, or when monitoring vast image repositories such as Docker Hub, Amazon ECR, or Google Container Registry.

In summary, version pattern matching is a strategic optimization tool for container image registry scans, enabling selective and efficient scanning of relevant image versions. Its utility is not confined by operating system type, nor should it be reserved exclusively for massive environments; it is a universally recommended best practice for enhancing scan performance and precision across any deployment size.

Question 4: 

Within Prisma Cloud Enterprise, what specific and required configuration method should be employed to accurately identify Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances that have been systematically tagged with the key-value pair indicating "Private" (e.g., Visibility:Private or Environment:Private)?

A. Open the Asset Dashboard, filter on tags, and choose "Private." 

B. Generate a CIS compliance report and review the "Asset Summary." 

C. Create an RQL config query to identify resources with the tag "Private." 

D. Create an RQL network query to identify traffic from resources tagged "Private."

Answer: C

Explanation: 

In the Prisma Cloud Enterprise Edition, the most effective and programmatic method for identifying cloud resources based on custom metadata attributes, such as AWS EC2 instances tagged with "Private," is by leveraging the Resource Query Language (RQL). RQL serves as the fundamental language for querying and analyzing the configuration state of cloud assets across various providers. Specifically, when the objective is to filter or search for resources based on their associated tags, a config query within RQL is the appropriate type of query to construct.

Understanding RQL and Tags: RQL empowers cloud security teams to compose highly specific and custom policies, alerts, and search queries that meticulously analyze the configuration metadata of cloud services. AWS EC2 instance tags, which are essentially key-value pairs (e.g., Environment:Private, Visibility:Private), are ingested by Prisma Cloud as crucial metadata attributes. This ingestion allows users to formulate powerful RQL queries to effectively search for, alert on, or enforce conditions based on these specific tag.

Why the Other Options Are Incorrect:

A. Open the Asset Dashboard, filter on tags, and choose "Private.": While the Asset Dashboard in Prisma Cloud offers some basic filtering capabilities, including filtering by tags, it is primarily designed for ad-hoc inspection and visualization. It does not provide the granular querying capability, automation, or continuous policy enforcement that RQL offers. This method is useful for a quick check but is not scalable for continuous monitoring or policy configuration.

B. Generate a CIS compliance report and review the "Asset Summary.": The CIS compliance report (e.g., AWS CIS Foundations Benchmark) focuses on evaluating cloud configurations against predefined industry security benchmarks. It is not designed to provide custom tag-based filtering or to identify assets based on arbitrary custom tags like "Private." Its purpose is to report on adherence to specific security standards, not custom metadata.

D. Create an RQL network query to identify traffic from resources tagged "Private.": RQL network queries are specifically tailored to evaluate network activity, such as traffic flows, connections, and network configurations (e.g., security group rules). While network policies might, in highly advanced use cases, reference tags, the primary objective of this question is to identify resources based on their configuration tags, not to analyze their network traffic. Resource identification based purely on configuration metadata, including tags, unequivocally falls under the domain of config queries, not network queries.

Therefore, to accurately and programmatically identify AWS EC2 instances tagged with a specific value like “Private,” Prisma Cloud Enterprise mandates the use of its config RQL engine, which inspects cloud configuration data, including tags. This capability allows users to build flexible, automated policies that can trigger alerts or enforce conditions based on these crucial metadata attributes.

Question 5: 

Which two types of Infrastructure as Code (IaC) templates are directly supported by the Prisma Cloud Code Security scan service for vulnerability and misconfiguration detection? (Choose two.)

A. Azure Resource Manager (ARM) 

B. Hyper Text Markup Language (HTML) 

C. GitHub D. Terraform

D. Terraform

Answer: A. Azure Resource Manager (ARM) 

Explanation: 

Prisma Cloud's Code Security module is specifically engineered to integrate security early into the development lifecycle by scanning Infrastructure as Code (IaC) templates. This proactive approach helps identify potential misconfigurations, vulnerabilities, and compliance violations before infrastructure is provisioned in the cloud. By analyzing IaC, organizations can prevent insecure configurations from ever reaching their cloud environments, thereby reducing their attack surface and improving their overall security posture. The service supports a variety of popular IaC frameworks widely used by developers for defining and provisioning cloud infrastructure.

The two templates directly supported by Prisma Cloud's Code Security scan service are:

A. Azure Resource Manager (ARM) templates: These are declarative JSON files used by Microsoft Azure to define the infrastructure and configuration for Azure deployments. ARM templates allow developers to provision entire environments consistently and repeatedly. Prisma Cloud's Code Security can parse these templates to identify misconfigurations that might lead to security vulnerabilities (e.g., publicly exposed storage accounts, insecure network security group rules, weak access policies) or compliance issues relevant to Azure resources.

D. Terraform: Developed by HashiCorp, Terraform is a widely adopted open-source IaC tool that enables users to define and provision datacenter infrastructure using a high-level configuration language (HCL). Terraform supports a multitude of cloud providers (including AWS, Azure, GCP, etc.). Prisma Cloud's Code Security can analyze Terraform configurations (.tf files) to detect security risks, such as insecure resource settings, overly permissive IAM roles, or non-compliant resource definitions, across any cloud environment that Terraform can manage.

Let's clarify why the other options are not correct:

B. Hyper Text Markup Language (HTML): HTML is a standard markup language primarily used for creating web pages and web applications. It is entirely unrelated to defining or provisioning infrastructure. Therefore, HTML files are not within the scope of Prisma Cloud's IaC scanning capabilities, as they do not represent infrastructure configurations.

C. GitHub: While Prisma Cloud has strong integrations with version control systems like GitHub (and GitLab, Bitbucket, etc.) to scan repositories, GitHub itself is a platform for hosting and managing code repositories. It is not an Infrastructure as Code template format. Prisma Cloud would integrate with GitHub to pull and scan IaC templates (like Terraform or ARM templates) that reside within GitHub repositories. Thus, GitHub is an integration point, not a template type.

In summary, Prisma Cloud's Code Security scan service is designed to support the scanning of popular Infrastructure as Code formats to identify security risks at the source. The two explicitly supported template types among the given choices are Azure Resource Manager (ARM) templates and Terraform configurations, which are critical for securing cloud deployments across their respective ecosystems.

Question 6: 

Among the various alert types generated by Prisma Cloud Enterprise, which specific category of alerts possesses the capability to trigger and execute automated remediation actions?

A. network

B. audit 

C. anomaly 

D. config

Answer: D

Explanation: 

In Prisma Cloud Enterprise, the ability to automatically remediate security and compliance violations is a powerful feature that significantly enhances an organization's cloud security posture. This functionality aims to correct or reverse configuration changes that deviate from predefined security policies without requiring manual intervention. Among the various types of alerts generated by Prisma Cloud, only one category is inherently designed to support auto-remediation: config alerts.

Config alerts are directly tied to Resource Query Language (RQL) policies that continuously evaluate the configuration state of cloud resources across major cloud service providers (such as AWS, Azure, GCP). These policies scrutinize configurations for adherence to security best practices and compliance requirements. Examples include checking if S3 buckets are publicly accessible, if logging is appropriately enabled, or if encryption is applied to data at rest. When a non-compliant or insecure configuration is detected, Prisma Cloud generates a config alert. Crucially, if auto-remediation is configured for that specific policy, Prisma Cloud can then trigger automated corrective actions. 

Let's examine why the other alert types do not typically support auto-remediation:

A. Network alerts: These alerts focus on monitoring network traffic flows and identifying suspicious or unauthorized communications, such as unexpected ingress from the internet or unusual internal network activity. While network alerts are critical for identifying active threats, the appropriate response (e.g., modifying security group rules, isolating resources) usually requires human analysis and decision-making before any potentially disruptive changes are made. Automated remediation is generally not applied directly to network flow alerts.

B. Audit alerts: These alerts are generated from cloud audit trails and activity logs (e.g., AWS CloudTrail, Azure Activity Logs) and relate to user or system actions. They are invaluable for forensic analysis, compliance auditing, and monitoring user behavior. However, audit alerts merely report on what has already occurred; they are not inherently linked to immediate, automated configuration changes. Remediation actions typically involve investigating the logged activity and taking administrative steps based on the findings.

C. Anomaly alerts: These alerts are generated by Prisma Cloud's machine learning models, which detect unusual patterns of behavior that deviate from established baselines (e.g., atypical login times, data exfiltration attempts). Due to the inherent uncertainty and potential for false positives with behavioral anomalies, these alerts typically require human review or further validation before any automated remediation could occur. Automatically acting on an anomaly could potentially interrupt legitimate activity or cause unintended operational impact.

Because config alerts deal with the static, declarative state of cloud resources, their violations are often clear-cut and less ambiguous, making them the safest and most predictable type of alert to auto-remediate. This makes config alerts a cornerstone of proactive cloud security posture management within Prisma Cloud Enterprise.

Question 7: 

To effectively initiate a security scan of container images within the Prisma Cloud Compute (PCC) edition using its command-line interface, which specific subcommand correctly invokes the PCC image scanner?

A. > twistcli images scan 

B. > twistcli project scan 

C. > twistcli scan projects 

D. > twistcli scan images

Answer: D

Explanation: 

In the Prisma Cloud Compute Edition (PCC), twistcli is the primary command-line interface (CLI) tool that administrators and developers use to interact with the platform's various security functionalities, including vulnerability scanning, compliance checks, and policy enforcement. Understanding the correct syntax for twistcli commands is crucial for automating security tasks within CI/CD pipelines or executing ad-hoc scans.

To specifically invoke the container image scanning functionality, twistcli adheres to a common CLI pattern: the primary action (verb) precedes the object (noun) on which the action is performed. Following this convention, the correct subcommand structure to initiate an image scan is:

A. > twistcli images scan: This option reverses the correct command order. In twistcli syntax, the action (e.g., scan) typically comes before the specific type of asset being acted upon (e.g., images). "images scan" is not a recognized or valid command format for this purpose.

B. > twistcli project scan: This subcommand would be used in the context of scanning Infrastructure as Code (IaC) files or source code repositories, often referred to as "projects" in the context of Code Security. It is not used for scanning compiled container images. The term "project" here refers to a collection of source files or a codebase, not a container artifact.

C. > twistcli scan projects: Similar to option B, this syntax is also associated with scanning code repositories or IaC templates using Prisma Cloud's Code Security features. It is not the correct command for scanning deployed or built container images. While "scan" is correct, "projects" as the target object is incorrect for image scanning.

Therefore, the only valid and correct subcommand structure to trigger a container image scan using the twistcli utility in Prisma Cloud Compute Edition is twistcli scan images. This ensures that the command is properly interpreted by the PCC system to perform the intended security assessment on your container images.

Question 8: 

Within the Prisma Cloud Compute (PCC) interface, where specifically can rules and policies be configured and subsequently viewed for the purpose of defining and managing "trusted images"?

A. Monitor > Compliance > Trusted Images 

B. Monitor > Compliance > Images 

C. Defend > Compliance > Trusted Images 

D. Defend > Compliance > Images

Answer: C

Explanation: 

In the Prisma Cloud Compute Edition (PCC), establishing and managing trusted image policies is a foundational element of a robust container security strategy. These policies enable organizations to explicitly define which container images are authorized and considered safe to run within their environments. This proactive control is vital for mitigating risks associated with deploying vulnerable, outdated, or unapproved images into production systems, thereby enforcing a strong security posture from the earliest stages of deployment.

The Prisma Cloud Console is logically segmented into different modules, each serving a specific purpose. Policy definition, enforcement rules, and security controls that directly impact runtime behavior and scanning are primarily handled within the Defend module. Conversely, the Monitor section is typically reserved for observing current security posture, viewing historical data, and analyzing alerts and incidents.

Given this architectural design, the correct navigation path to configure and view rules for trusted images is:

A. Monitor > Compliance > Trusted Images: This is incorrect. The "Monitor" section is designed for visibility and reporting – you can view historical scan results, compliance posture, and security events here. However, it does not provide the interface for defining or modifying policies like trusted image rules. Policies are defined in "Defend."

B. Monitor > Compliance > Images: This is also incorrect for the same reason as option A. While you might see reports related to image compliance in the "Monitor" section, the actual configuration of rules, including those for trusted images, does not take place here.

D. Defend > Compliance > Images: While "Defend > Compliance" is the correct parent category for policy definition, "Images" is a more general sub-page that might show overall image compliance status or provide general image policy settings. However, for the specific and granular configuration of "trusted images" (i.e., defining explicit allowlists/denylists for image deployment), the dedicated "Trusted Images" sub-page is the precise location. The distinction matters as "Trusted Images" offers specialized controls for image authorization.

By navigating to Defend > Compliance > Trusted Images, security teams can effectively enforce critical best practices, such as ensuring that only pre-approved, scanned, and signed images from verified registries are allowed to run, thereby significantly enhancing the security posture across the software delivery lifecycle.

Question 9: 

When Prisma Cloud analyzes for anomalous user activity, which two specific elements does its powerful anomaly detection engine primarily monitor to establish behavioral baselines and identify deviations? (Select two options.)

A. Operating System (OS) 

B. browser 

C. location 

D. time

Answer: C, D

Explanation: 

Prisma Cloud incorporates a sophisticated anomaly detection engine, a core component of its User Behavior Analytics (UBA) capabilities. This engine is specifically designed to identify unusual or suspicious user activity within cloud environments by continuously analyzing user actions against established historical patterns. The primary goal of UBA is to detect potential insider threats, compromised accounts, or unauthorized access attempts that might otherwise go unnoticed by traditional rule-based security systems. To achieve this, Prisma Cloud focuses on key behavioral indicators that, when combined, can highlight deviations from a user's typical activities.

The two primary elements that Prisma Cloud's anomaly detection engine monitors for analyzing unusual user activity are:

1. Location: Prisma Cloud rigorously tracks the geographical origin of user access to cloud resources. By building a baseline profile of a user's typical login locations (e.g., consistently accessing from North America, specifically from a corporate office IP range), the system can detect and flag accesses originating from unexpected or uncharacteristic geographical locations (e.g., a login attempt from a country not typically associated with the user, or from a known high-risk IP address) as anomalous. This capability is crucial for identifying potential credential theft, account compromise, or malicious actors attempting unauthorized access from an unusual network.

2. Time: Prisma Cloud also meticulously monitors the temporal patterns of a user's access and activity. It establishes a baseline of when a user typically logs in and performs actions (e.g., consistently active during standard business hours from 9 AM to 5 PM). Any significant deviation from this established pattern, such as a user suddenly logging in at 3 AM on a weekend when they are normally inactive, can be flagged as suspicious. Time-based anomaly detection is highly effective for identifying potential misuse of accounts, unauthorized activity during off-hours, or activities initiated by compromised credentials outside of normal operational windows.

These two elements—location and time—are universally recognized as fundamental indicators in modern cloud security and threat detection tools for establishing behavioral baselines and pinpointing anomalies that could signify a security incident or a compromised account.

Let's clarify why the other options are not primary elements for user activity anomaly detection in this context:

A. Operating System (OS): While Prisma Cloud might collect information about the operating system of the endpoint from which a user is accessing resources for other monitoring or inventory purposes (e.g., host-level insights), the OS itself is generally not a primary dimension used in user behavioral anomaly detection. The focus for UBA is more on the pattern of access rather than the specific device internals.

B. Browser: Similarly, the specific browser type or version used for access, although potentially logged for auditing or forensic correlation, is typically not a core behavioral signal for detecting anomalous user activity within Prisma Cloud's UBA engine. The key is where and when the activity occurs, not necessarily what browser is used.

In essence, by continuously monitoring the location and time of user activities, Prisma Cloud can effectively establish behavioral norms and promptly generate alerts when these activities deviate from the expected patterns, which is critical for identifying and responding to security threats stemming from compromised credentials or insider risks.

Question 10: 

How does Prisma Cloud Enterprise primarily execute its auto-remediation capabilities to correct or reverse unwanted security and compliance violations detected within public cloud infrastructure?

A. It inspects the application program interface (API) call made to public cloud and blocks the change if a policy violation is found. 

B. It makes changes after a policy violation has been identified in monitoring. 

C. It locks all changes to public cloud infrastructure and stops any configuration changes without prior approval. 

D. It uses machine learning (ML) to identify unusual changes to infrastructure.

Answer: B

Explanation: 

Prisma Cloud Enterprise's auto-remediation capability is a cornerstone of its Cloud Security Posture Management (CSPM) functionality, designed to automatically correct security and compliance violations detected in public cloud environments. This powerful feature significantly enhances an organization's security posture by ensuring that misconfigurations are not only identified but also automatically brought back into a secure and compliant state. The mechanism through which Prisma Cloud achieves this auto-remediation is fundamentally reactive, meaning it acts after a violation has occurred.

The correct behavior describing how Prisma Cloud Enterprise performs auto-remediation is:

B. It makes changes after a policy violation has been identified in monitoring. This statement accurately captures Prisma Cloud's approach. Prisma Cloud operates by continuously monitoring public cloud environments through their respective APIs (e.g., AWS CloudWatch/CloudTrail, Azure Activity Logs). When a predefined RQL-based policy is violated (e.g., an S3 bucket is inadvertently made public, or an overly permissive IAM role is created), Prisma Cloud detects this change during its ongoing monitoring cycles. Upon detection, if auto-remediation is enabled for that specific policy, Prisma Cloud then triggers a predefined automated corrective action. These actions can include a variety of responses, such as:

• Reverting the specific configuration change (e.g., removing public access from a storage bucket).

• Revoking insecure permissions.

• Disabling exposed services or ports.

• Notifying relevant security teams.

• Executing a custom script or serverless function (like AWS Lambda) to perform more complex remediation. This "detect-and-remediate" model ensures that deviations from security policies are swiftly corrected, reducing the window of exposure.

Let's examine why the other options are incorrect:

A. It inspects the application program interface (API) call made to public cloud and blocks the change if a policy violation is found: This describes a proactive, "inline" enforcement mechanism, often found in Cloud Access Security Brokers (CASBs) or specialized API gateways that intercept and validate API calls before they are executed. Prisma Cloud, in its auto-remediation context, primarily functions as a post-facto monitoring and remediation tool. It observes changes that have already occurred via API logs, rather than intercepting and blocking them in real-time.

C. It locks all changes to public cloud infrastructure and stops any configuration changes without prior approval: This statement is inaccurate. Prisma Cloud does not operate by locking down entire cloud infrastructures or implementing an approval workflow for every configuration change. Such a stringent approach would severely hinder agility and development velocity in dynamic cloud environments. Instead, it enforces policies through continuous detection and automated correction of unwanted changes.

D. It uses machine learning (ML) to identify unusual changes to infrastructure: While Prisma Cloud does incorporate Machine Learning (ML) for certain functions, particularly in anomaly detection (e.g., user behavior analytics or network traffic anomalies), the auto-remediation of infrastructure misconfigurations is fundamentally driven by rules and policies defined using RQL. These policies explicitly state what constitutes a violation (e.g., "S3 bucket is public"). ML helps identify unusual behavior, but auto-remediation for configuration drift is based on defined policy violations.

In summary, Prisma Cloud's auto-remediation capabilities are based on a "detect-and-respond" model, where it identifies policy violations through continuous monitoring of cloud configurations and then automatically executes predefined corrective actions after the unwanted change has been detected.