ServiceNow CIS-EM Exam Dumps & Practice Test Questions
Question 1
What is the term used to describe the duplicate versions of checks that are part of Agent Client Collector (ACC) policies?
A Check definitions
B Check models
C Check clones
D Check mirrors
E Check instances
Correct Answer: D
Explanation:
In the context of the Agent Client Collector (ACC) within a monitoring or policy management framework, the concept of duplicating or replicating checks is essential for ensuring consistent monitoring across different systems or policy configurations. These duplicated checks are typically used to mirror the behavior or configuration of an original check without altering its source, allowing for controlled replication within a policy.
Option D, "Check mirrors," is the correct term. A check mirror is essentially a duplicated version of a check that reflects the configuration of the original. These mirrors are not separate definitions or entirely independent checks; rather, they act as references or copies that inherit the properties of the primary check. This ensures consistency in monitoring across multiple clients while maintaining a centralized definition. Mirroring is crucial when the same monitoring logic needs to be applied uniformly across various ACC policies without manually recreating or reconfiguring checks.
Option A, "Check definitions," refers to the original setup or blueprint of a check. It is the primary configuration that outlines what a check monitors, how it behaves, and how it reports results. Definitions are not duplicates — they are the originals from which other instances or mirrors may be derived.
Option B, "Check models," is a misleading term in this context. "Model" might imply a template or abstract form but is not typically used to describe duplicated operational checks within ACC. This is not standard terminology in most systems employing ACC.
Option C, "Check clones," while it might seem plausible, is not the accepted terminology. Cloning generally implies a copy that may be altered independently of the original. In contrast, mirrors reflect the state of the original check and update in sync, which is more precise to the intended function.
Option E, "Check instances," usually refers to individual executions or deployments of a check, but not necessarily ones that are mirrored from a primary definition. An instance could be an individual runtime or applied version, but it lacks the dependency relationship implied in mirroring.
To summarize, check mirrors maintain a synchronized relationship with a master check configuration and are used to extend the original check’s reach across policies. They ensure consistent application of monitoring logic while minimizing administrative overhead. Therefore, the correct answer is D, as it most accurately represents the concept of duplicated but dependent checks in ACC policies.
Question 2
What is the typical frequency at which baseline event connectors gather event data?
A Every 30 seconds
B Every 2 minutes
C Every 10 minutes
D Every 1 minute
E Every 5 minutes
Correct Answer: E
Explanation:
Event connectors are components in monitoring systems that collect and forward data from various sources — such as logs, metrics, or performance indicators — into a centralized monitoring or analytics platform. In baseline configurations, which refer to the default or standard settings applied when no custom policies are introduced, the data collection interval plays a crucial role in balancing performance with data granularity.
Option E, every 5 minutes, is the correct answer and reflects the standard default polling interval for most baseline event connectors. This interval is carefully chosen to provide reasonably up-to-date information while avoiding excessive overhead on the system being monitored. It strikes a balance between data freshness and resource efficiency, making it a practical default in most enterprise monitoring scenarios.
Option A, every 30 seconds, would result in a much higher frequency of polling. While this might be useful in high-performance environments or where real-time monitoring is essential, it is not typically set as the default because it increases system load and network usage significantly.
Option B, every 2 minutes, is more frequent than the standard default and may be used in tuned configurations for systems requiring faster responsiveness. However, it is not the typical baseline frequency.
Option C, every 10 minutes, would reduce the system's load but comes at the cost of slower detection of issues or anomalies. While acceptable in low-priority environments, it is too infrequent for general-purpose monitoring.
Option D, every 1 minute, is more aggressive than the 5-minute interval and may be used in advanced or custom configurations, but it is not the baseline default.
In summary, the default configuration of baseline event connectors collects event data every 5 minutes, as it provides a good trade-off between timeliness and performance. It ensures regular updates without overwhelming the monitoring system or the endpoints, and it fits well within most operational use cases. Therefore, the correct answer is E.
Question 3
Which attribute allows you to combine several related events into a single alert?
A Additional_info
B Message_key
C Metric_name
D Short_description
Correct Answer: B
Explanation:
In event correlation systems — whether part of observability platforms, monitoring tools, or incident management frameworks — the ability to reduce noise and avoid alert fatigue is critical. One of the most effective techniques used is event deduplication or correlation, where multiple related events are grouped into a single alert. This prevents the system from overwhelming administrators with redundant notifications and helps streamline issue triaging.
Option B, Message_key, is the correct answer. This attribute acts as a correlation identifier that links multiple incoming events together based on shared characteristics. When several events share the same message_key, they are treated as variations or repeats of the same underlying issue and are grouped into a single alert. This mechanism is key for suppressing duplicate alerts while still capturing all relevant activity associated with an incident.
Option A, Additional_info, typically includes metadata or contextual information about an event, such as hostnames, application versions, or environment tags. While it adds value to the alert content, it is not used as a basis for grouping or correlating events into a single alert.
Option C, Metric_name, refers to the specific performance indicator or metric (e.g., CPU_Usage, Memory_Free) involved in the event. Although it can help classify the event, multiple events sharing the same metric_name are not automatically considered related unless they also share the same message_key.
Option D, Short_description, provides a brief summary of the event or condition, often used for quick reference or display in dashboards. While two events may have the same short_description, this field is not a deterministic way to correlate events, since it is not necessarily unique or consistent across sources.
To summarize, Message_key is the key attribute that determines whether multiple events are instances of the same issue. By sharing this identifier, events are effectively grouped into a unified alert, reducing noise and simplifying incident resolution. Therefore, the correct answer is B.
Question 4
What attribute is responsible for grouping related events so they appear as one alert?
A Event Rules
B Message Key
C Alert Priority
D Severity
Correct Answer: B
Explanation:
In most observability and event management platforms, handling high volumes of data is only effective if the system can intelligently correlate related information. This correlation is vital to preventing a situation where administrators are bombarded with dozens or hundreds of event messages related to the same underlying issue. To address this, platforms use specific attributes to logically group events into a single actionable item, typically referred to as an alert.
Option B, Message Key, is again the correct attribute. This unique identifier links events that originate from similar conditions or components, allowing the platform to aggregate them into one alert. When multiple events have the same message key, the system recognizes them as pertaining to the same issue. This deduplication or aggregation reduces alert fatigue and increases the signal-to-noise ratio, enabling faster incident detection and resolution.
Option A, Event Rules, is incorrect. While event rules define how events are handled — such as filtering, routing, or transforming events — they do not inherently group events. Event rules may apply logic based on fields like message key, but they are not the field used to perform the grouping.
Option C, Alert Priority, indicates the urgency or importance of an alert (e.g., High, Medium, Low). While this field helps in triaging and response prioritization, it plays no role in determining which events should be grouped together.
Option D, Severity, is similar to priority in that it helps classify how critical an event is. Though useful for decision-making and alert escalation, severity does not influence how events are grouped. Multiple events with the same severity level can still be unrelated unless they share a common message key.
In conclusion, the message key is the primary attribute used to consolidate related events into a single alert. This attribute is central to efficient incident management workflows, enabling monitoring tools to reduce noise, consolidate insights, and focus attention on actionable issues. Therefore, the correct answer is B.
Question 5
Which attribute must match across multiple events for the system to treat them as duplicates?
A Metric Name
B Message Key
C Type & Node
D Description
E Correlation ID
Correct Answer: B
Explanation:
In modern event management and alerting systems, a major objective is to minimize redundant alerts that overwhelm operations teams. To achieve this, platforms often use a deduplication mechanism. Deduplication refers to the system's ability to recognize when multiple incoming events describe the same underlying condition or issue. Once identified, these duplicate events can be grouped or suppressed, ensuring that the alert remains manageable and relevant.
The central component in deduplication is the message key, which is the unique attribute that the system evaluates to determine whether an incoming event is a duplicate of an already-processed one. If two or more events share the same message key, the system interprets them as different occurrences of the same event and typically updates the existing alert with new information (such as a new timestamp or updated count), rather than generating a new alert.
Option B, therefore, is correct. The message key acts as a consistent identifier that links together events generated by the same condition. It is often derived from a combination of event metadata, such as the metric name, source, and event type, but is ultimately a single attribute used by the platform’s correlation engine.
Option A, Metric Name, while important for identifying the kind of issue being monitored (like CPU_Usage or Disk_Space), is too broad on its own to determine if two events are duplicates. Multiple unrelated events could share the same metric name but originate from different systems or timeframes.
Option C, Type & Node, might be used as part of how the message key is constructed, but they are not individually used as the basis for deduplication. Two events from the same node of the same type could represent different problems and thus are not inherently duplicates.
Option D, Description, is not a reliable attribute for deduplication. Descriptions can be manually edited, localized, or configured differently across environments, making them inconsistent. Also, they are often meant for human readability, not systematic correlation.
Option E, Correlation ID, is usually used in tracing or linking events across systems in a broader process (like a request lifecycle in microservices), but it does not typically define duplicate event logic in most event monitoring platforms.
In summary, message key is the single attribute that determines event duplication by acting as a unique signature. Events with identical message keys are considered repeats of the same issue and are treated as duplicates. Thus, the correct answer is B.
Question 6
Under default settings, how frequently do baseline connectors retrieve event data from their sources?
A Once per minute
B Every 2 minutes
C Twice per minute
D Every 5 minutes
Correct Answer: D
Explanation:
Baseline connectors in event monitoring systems are configured to pull data at regular intervals from data sources such as logs, metrics, or external applications. These connectors are essential for ensuring that the monitoring platform stays synchronized with current system activity. The polling frequency determines how current the system's view of the environment is and can influence alert latency, system performance, and data accuracy.
Option D, every 5 minutes, is the correct and most commonly used default polling interval for baseline connectors. This interval reflects a carefully balanced configuration that aims to minimize system overhead while still maintaining an acceptable level of timeliness in event detection. Collecting data every 5 minutes is generally sufficient for environments where near-real-time data is not strictly necessary, and it helps prevent unnecessary load on both the data source and the monitoring platform.
Option A, once per minute, is more aggressive and can be used in environments where quicker detection of anomalies is required. However, it is not the standard default because it significantly increases the volume of data ingested and processed, which can impact performance and cost in large-scale deployments.
Option B, every 2 minutes, is somewhat of a middle ground and may be configured by administrators in custom setups. However, it is not the default interval provided out of the box with most monitoring tools.
Option C, twice per minute (i.e., every 30 seconds), is even more aggressive and would typically be used only in high-priority systems where immediate detection of changes is essential. This setting is rarely used as a default due to the overhead it introduces.
To summarize, baseline connectors are configured by default to pull data every 5 minutes, a value that balances monitoring freshness with system efficiency. Adjusting this interval is possible but depends on the specific needs of the system being monitored. Therefore, the correct answer is D.
Question 7
Which group of applications is part of the ITOM Health product suite?
A Event Management and Operational Intelligence
B ITOM Visibility
C Discovery and Service Mapping
D Cloud Management
Correct Answer: A
Explanation:
ITOM (IT Operations Management) in ServiceNow is a comprehensive suite designed to help organizations manage infrastructure and services more efficiently. Within this suite, the ITOM Health category is focused specifically on the monitoring, alerting, and predictive health of IT services. This set of applications plays a critical role in maintaining high availability, minimizing downtime, and responding proactively to incidents.
Option A, Event Management and Operational Intelligence, is the correct set of applications included in the ITOM Health product. These two applications work together to provide real-time visibility into the health of services:
Event Management ingests and correlates events from monitoring tools across the IT environment. It helps identify issues quickly by deduplicating and filtering events, converting them into actionable alerts. It reduces alert fatigue and speeds up incident response.
Operational Intelligence adds a layer of machine learning and analytics. It monitors trends and baseline behavior in performance metrics, providing anomaly detection and proactive warning signs before incidents occur. This allows IT teams to move from reactive to predictive operations.
Option B, ITOM Visibility, is incorrect because it refers to a separate ITOM product focused on discovery and service mapping, which provides visibility into the infrastructure and application dependencies but does not handle health monitoring or event correlation.
Option C, Discovery and Service Mapping, is part of ITOM Visibility, not ITOM Health. These applications help build a Configuration Management Database (CMDB) and visualize relationships between services and underlying infrastructure, but they do not deal directly with monitoring the health or events of those services.
Option D, Cloud Management, is part of ITOM Optimization, which is another sub-area of ITOM. This component deals with provisioning, governance, and cost optimization of cloud resources, not service health or event analytics.
In summary, ITOM Health is composed of Event Management and Operational Intelligence, tools that focus on identifying, correlating, and predicting issues impacting service health. These applications help IT teams stay ahead of incidents and maintain high service reliability, making A the correct answer.
Question 8
What is a key benefit of using Event Management and Operational Intelligence together?
A Enhancing service availability by helping IT teams trace service issues and assess the effects of planned changes
B Boosting service agility through automation of repetitive, manual tasks
C Enabling secure, agentless resource discovery and mapping
D Predicting potential outages using advanced machine learning
Correct Answer: D
Explanation:
Event Management and Operational Intelligence form a powerful combination in ServiceNow's ITOM Health solution, providing comprehensive monitoring and predictive capabilities. While both components have distinct functions, their integration offers a major advantage: early detection and prevention of service disruptions.
Option D is correct because one of the most valuable advantages of combining these applications is the use of advanced machine learning to predict potential service outages or performance degradation. Operational Intelligence continuously analyzes performance data (such as memory, CPU, disk usage, and application response times) and builds dynamic baselines for normal behavior. When metrics deviate significantly from the baseline, it identifies anomalies — early indicators that something is going wrong — even before a threshold is breached or an event is generated.
This predictive capability allows IT teams to respond proactively rather than reactively. Instead of waiting for an incident to occur, teams are alerted to abnormal trends that may lead to outages or major incidents, giving them time to investigate and resolve root causes early. It’s especially useful in large-scale environments where thousands of events occur, and hidden patterns could easily be missed without machine learning.
Option A is incorrect because while Event Management and Operational Intelligence do enhance service availability, the specific function of assessing the impact of planned changes is more aligned with Change Management and Change Success Score, not this product set.
Option B, which refers to automating manual tasks, aligns more closely with ITOM Optimization or IT Workflow Automation, rather than Event Management. These applications are not primarily focused on task automation but rather event analysis and anomaly detection.
Option C, which talks about agentless discovery and mapping, is a benefit of Discovery and Service Mapping, part of ITOM Visibility, not ITOM Health.
In conclusion, the integration of Event Management and Operational Intelligence allows organizations to leverage machine learning to anticipate issues before they impact users. This predictive insight enhances service reliability and operational efficiency, making D the correct choice.
Question 9
In the context of ITOM Health, what does the acronym MID represent in MID Server?
A Management, Instrumentation, and Discovery
B Messaging, Integration, and Data
C Monitoring, Insight, and Domain
D Maintenance, Information, and Distribution
Correct Answer: A
Explanation:
In the ServiceNow IT Operations Management (ITOM) suite — particularly in Discovery, Service Mapping, and Event Management — a MID Server is an essential component that facilitates communication between the ServiceNow platform and external environments like data centers, public clouds, and on-premises infrastructure. Understanding what MID stands for helps clarify its core functions and importance.
Option A, Management, Instrumentation, and Discovery, is the correct answer. The MID Server is a Java-based application installed on a server within the customer's network. It performs three key functions that align with its acronym:
Management: It manages communication securely between the ServiceNow instance (which is cloud-based) and on-premises systems. It serves as a trusted relay for tasks like running probes, sensors, or integrations.
Instrumentation: The MID Server can collect performance and availability data using monitoring tools or SNMP (Simple Network Management Protocol). This data is used for operational intelligence and event monitoring.
Discovery: It plays a critical role in ServiceNow Discovery by scanning the environment, identifying infrastructure and applications, and populating the Configuration Management Database (CMDB). Without a MID Server, ServiceNow cannot perform Discovery in secure or firewalled environments.
Option B, Messaging, Integration, and Data, sounds plausible but is not accurate or aligned with official ServiceNow documentation. Messaging and data transfer are certainly part of what a MID Server handles, but they are not the core pillars identified by the acronym.
Option C, Monitoring, Insight, and Domain, does not reflect the scope of the MID Server. While monitoring and insight are outcomes enabled by the MID Server, the term “domain” is not relevant, and this phrasing lacks technical accuracy.
Option D, Maintenance, Information, and Distribution, also misrepresents the MID Server's purpose. It is not used for software distribution or general maintenance but for targeted tasks like Discovery and integration execution.
In summary, a MID Server bridges the cloud-based ServiceNow platform and the customer’s network securely, providing management, instrumentation, and discovery functionality. This makes A the correct answer.
Question 10
When setting up Event Management to reduce alert noise, which two capabilities can automatically correlate multiple alerts into one incident? (Choose 2)
A Alert Aggregation Rules
B Alert Priority Override
C Event Rules with “De-duplication Key”
D Impact Normalization
E Alert Grouping (CMDB-based Topology Correlation)
Correct Answers: A and E
Explanation:
One of the biggest challenges in IT Operations is the overwhelming volume of alerts generated by infrastructure, applications, and monitoring tools. Without effective correlation, this noise can obscure real issues, delay responses, and increase mean time to resolution (MTTR). ServiceNow Event Management provides powerful features to automatically group or correlate related alerts into a single, actionable incident, which simplifies triage and incident handling.
Option A, Alert Aggregation Rules, is correct. These rules allow admins to specify conditions under which multiple alerts should be consolidated into a single alert group or incident. For example, alerts from the same CI (Configuration Item), with the same type, within a specific time window, can be grouped together. This significantly reduces the volume of incidents and helps ensure the right teams are notified about root issues rather than downstream symptoms.
Option E, Alert Grouping (CMDB-based Topology Correlation), is also correct. This feature leverages the ServiceNow CMDB topology to understand relationships between infrastructure and applications. By using CMDB-based relationships (such as "depends on," "hosted on," etc.), the system can group alerts that are related at a topological level, often indicating a shared root cause. This is particularly useful in complex, distributed environments where an issue in one component cascades into multiple alerts across systems.
Option B, Alert Priority Override, is incorrect because it is a feature used to adjust the severity or priority of an individual alert based on specific conditions — it does not correlate or group alerts.
Option C, Event Rules with De-duplication Key, is not directly used to group alerts into incidents, but rather to filter or deduplicate incoming events before they generate alerts. This reduces the number of alerts created, but it is not responsible for combining multiple alerts into a single incident.
Option D, Impact Normalization, is a feature used to align different impact values from third-party monitoring tools into a common framework. While it improves consistency in alert evaluation, it does not perform alert correlation or grouping.
To summarize, the two features that directly contribute to automatic correlation of alerts into one incident are Alert Aggregation Rules and CMDB-based Alert Grouping, making the correct answers A and E.
