IBM C1000-156 Practice Test Questions, Exam Dumps

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C1000-156 IBM Practice Test Questions and Exam Dumps

Question 1

You want to use a quick filter search to look for certain elements:
10.100.100.*
BlueCoat -
TCP_REFRESH_MIS -

Which string provides the correct results?

A. (10.100.100. Bluecoat TCP_REFRESH_MIS)*
B. 10.100.100.*%Bluecoat%TCP_REFRESH_MIS
C. (10.100. 100. AND Bluecoat AND TCP_REFRESH_MIS)*
D. "10.100.100.*%AND%Bluecoat%AND%TCP_REFRESH_MIS"

Answer: C

Explanation:

To perform a quick filter search effectively, it's important to understand how the system handles search queries and the use of operators. Let's break down the options to identify the correct format for achieving the desired results:

*A. (10.100.100. Bluecoat TCP_REFRESH_MIS)**: This option is incorrect because it does not use the correct syntax for filtering or logical operators. It simply lists the elements without specifying how they should relate to one another.
B. 10.100.100.*%Bluecoat%TCP_REFRESH_MIS: This option uses % as a wildcard, which may be relevant in some search systems, but it's not a standard or effective way to combine these elements with logical operators in a structured query.
C. (10.100.100. AND Bluecoat AND TCP_REFRESH_MIS)**: This is the correct answer because it uses the AND operator to combine all three elements in the search. The AND operator ensures that the search results include all specified terms (i.e., the IP pattern 10.100.100., the word Bluecoat, and the term TCP_REFRESH_MIS). This query is structured to give precise results based on the presence of all three elements.
D. "10.100.100.*%AND%Bluecoat%AND%TCP_REFRESH_MIS": While this option uses the AND operator, it also incorrectly uses the % symbols around the operator, which is unnecessary and does not conform to the standard query syntax.

Therefore, the correct approach to filter for results that contain all three elements—10.100.100.*, Bluecoat, and TCP_REFRESH_MIS—is option C, which uses logical operators in the correct format for the search.

Question 2

A QRadar administrator is trying to tune a rule so that it cannot send an email more than 10 times in a 24-hour period.

Which method can be used to accomplish this goal?

A. Using the "response limiter"
B. Using a special rule test that limits the number of rule triggers
C. Tuning the rule conditions to make it trigger fewer times
D. Using the “execute custom action" rule response

Answer: A

Explanation:

In QRadar, administrators can configure rules to send notifications (like email alerts) when certain conditions are met. However, to prevent a rule from triggering too frequently (for example, sending more than 10 emails within 24 hours), a response limiter is used.

A. Using the "response limiter": The response limiter is a feature that is specifically designed to limit how often a particular rule's response (such as an email notification) can be triggered. By setting a response limiter, the administrator can specify that the rule will only send an email a maximum of 10 times in a 24-hour period. This is the most effective and straightforward method for limiting the number of times an action (like sending an email) occurs, without affecting the rule’s overall behavior.
B. Using a special rule test that limits the number of rule triggers: While QRadar rules can be tested for various conditions, there is no specific "special rule test" to limit the number of triggers. Instead, the response limiter should be used to manage how often a rule’s action occurs.
C. Tuning the rule conditions to make it trigger fewer times: Although this option could help reduce the frequency of the rule triggering (by adjusting conditions to be more restrictive), it does not specifically address limiting the number of actions (like emails) sent over time. It focuses on changing when the rule triggers, but does not restrict the number of triggers within a time frame.
D. Using the “execute custom action" rule response: This option is about executing custom actions, which can be a more advanced configuration, but it does not inherently address limiting the number of actions based on a time frame. The response limiter is the more suitable choice for this scenario.

In summary, the correct method to limit the number of email notifications sent by a QRadar rule to 10 in a 24-hour period is A, using the response limiter. This feature is designed to prevent excessive notifications from being triggered by the same rule within a specific time frame.

Question 3

Which command does an administrator run in QRadar to get a list of installed applications and their App-ID values output to the screen?

A. /opt/qradar/support/recon connect 1005
B. opt/qradar/support/deployment_info.sh
C. /opt/qradar/support/recon ps
D. /opt/qradar/support/threadTop.sh

Answer: C

Explanation:

In QRadar, to get a list of installed applications and their App-ID values, administrators typically use the /opt/qradar/support/recon ps command. This command will display a list of all the applications currently installed on the system along with their associated identifiers (App-IDs).

Let's go over the other options for clarity:

A. /opt/qradar/support/recon connect 1005: This command is used to establish a connection for support diagnostics, but it doesn't list the installed applications or App-IDs. The connect command has a different purpose related to connecting to specific support services.
B. opt/qradar/support/deployment_info.sh: This script provides deployment information about the QRadar system, such as network settings, deployment type, and hardware configurations. However, it does not list installed applications or their App-IDs.
D. /opt/qradar/support/threadTop.sh: This script is used to gather information about the performance of the QRadar system, particularly for troubleshooting thread-related performance issues. It does not show installed applications or their App-IDs.

Therefore, the correct command to list installed applications and their App-ID values in QRadar is C, /opt/qradar/support/recon ps. This command will display the relevant information directly on the screen.

Question 4

When will events or flows stop contributing to an offense?

A. When the offense becomes inactive
B. After the offense is assigned to an analyst
C. When the offense becomes dormant
D. When you protect the offense

Answer: A

Explanation:

In QRadar, events and flows contribute to the creation and behavior of an offense. The moment an offense becomes inactive, no more events or flows will be added to it, and its status will change accordingly. This is the key factor in determining when they stop contributing to an offense.

Let’s review the other options to understand why they are incorrect:

A. When the offense becomes inactive: This is the correct answer. When an offense becomes inactive, no new events or flows are added to it. Typically, an offense becomes inactive when it is resolved, or it no longer meets the conditions that triggered it. When inactive, the offense will no longer be updated with new data.
B. After the offense is assigned to an analyst: Assigning an offense to an analyst does not stop events or flows from contributing to that offense. The analyst assignment is a step in managing the offense but does not affect how new data is added to the offense.
C. When the offense becomes dormant: A dormant offense is one that is temporarily not active, but events and flows can still contribute to it until it is marked as inactive. A dormant offense means the system is still processing it, but it is not currently being actively monitored or investigated.
D. When you protect the offense: Protecting an offense simply prevents it from being accidentally deleted or marked as resolved. It does not stop the contribution of events or flows. Events and flows will continue to contribute to an offense unless it becomes inactive.

In conclusion, events or flows stop contributing to an offense when the offense becomes inactive, making A the correct answer.

Question 5

How many vulnerability processors can you have in your deployment?

A. 1
B. 10
C. 3
D. 5

Answer: D

Explanation:

In QRadar, vulnerability processors are components that help manage and process vulnerability data. The vulnerability processor in QRadar is responsible for handling the analysis of vulnerability scan results and integrating them into the QRadar environment for further analysis.

The maximum number of vulnerability processors that can be deployed in a QRadar system is typically 5. This allows the system to scale its vulnerability management capabilities based on the size and complexity of the deployment.

Let’s review why the other options are incorrect:

A. 1: While it's possible to have a single vulnerability processor in small deployments, the typical deployment allows for more than one processor to help scale the system and handle larger volumes of vulnerability data.
B. 10: QRadar does not support 10 vulnerability processors. The system's design limits it to 5 processors.
C. 3: While this number could theoretically work in some deployments, the maximum is 5, so this number is incorrect.
D. 5: This is the correct answer. QRadar allows up to 5 vulnerability processors in a deployment. This number is sufficient to handle most medium to large-scale environments.

Thus, the correct number of vulnerability processors allowed in a QRadar deployment is D, 5.

Question 6

An administrator receives a file with all the vital assets in the company and wants to import this file into QRadar.

How must this import file be formatted?

A. JSON file in the format: IP address, Name, Weight, Domain
B. XML file in the format: IP address, Name, Weight, Domain
C. CSV file in the format: IP address, Name, Weight, Description
D. XLS file in the format: IP address, Name, Weight, Description

Answer: C

Explanation:

In QRadar, the process of importing data, such as asset information, requires the file to be in the correct format. The system supports several formats, and understanding the correct format is essential for successful data integration.

For importing asset information into QRadar, the file should be formatted as a CSV (Comma Separated Values) file. The format should include key details for each asset, such as the IP address, Name, Weight, and Description. The CSV file is widely supported and can be easily manipulated in spreadsheet applications like Microsoft Excel, which makes it an efficient way to upload large sets of asset data into the QRadar system.

Here’s a breakdown of the components of the correct format:

IP address: The unique identifier for each asset on the network, which is critical for correlating events and flows in QRadar.
Name: The descriptive name of the asset, which helps identify it in reports or searches within QRadar.
Weight: A value used to indicate the importance or significance of the asset. This can be used for prioritizing events or analyzing the asset’s risk level.
Description: Additional details about the asset, such as its role in the organization, the owner, or its function, which can help provide context when investigating incidents.

Now, let’s review the incorrect options:

A. JSON file: While JSON files are often used for structured data exchange, QRadar typically supports CSV for asset imports, not JSON. JSON is not the expected format for asset import in this scenario.
B. XML file: Similar to JSON, XML is another structured data format, but QRadar typically uses CSV for importing assets. XML is more commonly used for configuration files or log parsing, but not for asset imports.
D. XLS file: While an XLS (Excel) file can contain structured data, QRadar does not directly support XLS files for asset imports. The preferred format is CSV, as it is simpler and more compatible for import purposes.

In conclusion, the correct file format for importing asset information into QRadar is C, a CSV file containing the IP address, Name, Weight, and Description of the assets. This format ensures that QRadar can efficiently ingest and process the asset data for use in security monitoring and analysis.

Question 7

An administrator wants to export a list of events to a CSV file.

Which items are in the default columns of the search result?

A. Protocol, Storage Time, Destination Port, Source Port
B. Log Source, Event Count, High Level Category, Related Offense
C. Event Name, Application, Username, Log Source
D. Username, Source Port, Event Count, Magnitude

Answer: C

Explanation:

When exporting a list of events to a CSV file in QRadar, the default columns typically contain the most relevant data fields related to security event monitoring and analysis. QRadar includes key fields that help administrators analyze event data in the most effective manner. These default columns in the search results typically include the following:

Event Name: The name or type of event, which is essential for understanding what triggered the event. It identifies the specific security-related activity that was logged.
Application: The application associated with the event. This provides context to the event, indicating which application or service was involved when the event occurred.
Username: The user associated with the event. This field identifies the user involved in the event, which is crucial for investigating user activity and potential security incidents.
Log Source: The source of the event, typically representing the device or system that generated the log. This can include information about the firewall, router, server, or other networked systems involved in generating the event.

These columns help administrators quickly understand the context of the events and provide critical information for further analysis and investigation.

Now, let’s examine the other options:

A. Protocol, Storage Time, Destination Port, Source Port: While these fields are relevant for network traffic analysis, they are not the default columns when exporting event data. These fields are more closely associated with network flow data or specific network security event types.
B. Log Source, Event Count, High Level Category, Related Offense: This option includes useful fields, such as Event Count and Related Offense, but these are not the default columns when exporting event data. Typically, the focus is on individual events and user-related data, rather than summary metrics like event count or offenses.
D. Username, Source Port, Event Count, Magnitude: While Username and Source Port are relevant, the Event Count and Magnitude fields are not the default columns. Event count and magnitude might be part of a more detailed or custom report, but not the default columns for exporting events.

In conclusion, the default columns for a CSV export of event data in QRadar typically include Event Name, Application, Username, and Log Source, making C the correct answer. These fields provide a clear overview of the event data, helping administrators with their analysis and incident response.

Question 8

An administrator would like to optimize event and flow payload searches for log data that is stored for up to a month.

What does an administrator need to do to achieve that requirement?

A. Configure the retention period for search indexes.
B. Configure the retention period for property indexes.
C. Perform a clean on the search model.
D. Configure the retention period for payload indexes.

Answer: D

Explanation:

To optimize event and flow payload searches for log data that is stored for a specific period, such as a month, the administrator needs to focus on configuring the retention period for payload indexes. Payload indexes are responsible for storing and indexing the contents of events and flows, which includes the raw log data or payload information.

When you configure the retention period for payload indexes, you're ensuring that QRadar retains the relevant log data for the specified time frame (e.g., 30 days). This ensures that searches performed on the log data remain efficient, as QRadar will keep the indexed data available within the retention period. Optimizing payload searches is crucial for maintaining performance and ensuring that event and flow data can be retrieved quickly when needed.

Let’s break down the other options to understand why they are incorrect:

A. Configure the retention period for search indexes: While the search indexes are important for improving the speed of searches in QRadar, they primarily index the metadata of events and flows (e.g., source IP, destination port) rather than the actual event content or payload. Configuring search indexes is important for optimizing metadata searches but does not directly optimize searches on payload data itself.
B. Configure the retention period for property indexes: Property indexes refer to the indexing of event properties, such as timestamps and log source information. Although these indexes are useful for optimizing searches on event properties, they do not directly address the content of the events (the payload). As a result, this option would not directly address optimizing searches for event and flow payloads.
C. Perform a clean on the search model: Running a clean operation on the search model may help in clearing old or unused data, but it does not optimize the retention of payload data. Performing a clean operation would not directly impact the ability to optimize searches on payloads, which is the primary goal in this scenario.

In summary, to optimize event and flow payload searches for log data that is stored for up to a month, the administrator should configure the retention period for payload indexes (Option D). This will ensure that the raw event and flow data are indexed for efficient searching within the desired retention period, improving search performance without sacrificing data retention.

Question 9

To detect outliers, which Anomaly Detection Engine rule tests events or flows for volume changes that occur in regular patterns?

A. Threshold rules
B. Anomaly rules
C. Building block rules
D. Behavioral rules

Answer: B

Explanation:

In QRadar, the Anomaly Detection Engine (ADE) is designed to help identify unusual patterns or outliers in event and flow data. Anomaly rules are specifically tailored to detect such outliers by analyzing events or flows for volume changes that occur in regular patterns, helping to highlight abnormal behavior. These rules are used to identify shifts in network activity or behavior that deviate from established baselines, which can be indicative of potential security threats.

Anomaly rules are built to track normal patterns of behavior over time and raise alerts when there is a significant deviation from these patterns. For instance, if a system regularly sends out a certain volume of data, and suddenly there is a substantial increase or decrease in that volume, the Anomaly rule will flag this change as an outlier. This helps administrators identify suspicious or unexpected behavior, which is a critical component of threat detection and response.

Let’s go over the other options to understand why they are incorrect:

A. Threshold rules: Threshold rules are used to trigger alerts when a specific condition or threshold is met, such as when a metric exceeds a certain limit. While they are effective for detecting specific conditions, they do not focus on volume changes that occur in regular patterns, which is the core focus of Anomaly rules. Threshold rules are more about triggering based on predefined conditions, not detecting outliers in regular volume patterns.
C. Building block rules: Building block rules are used in QRadar as reusable components in rule construction. They are essentially templates that can be combined with other rule conditions to create more complex detection logic. While they can contribute to creating rules for specific detection tasks, they are not specifically designed for detecting outliers or volume changes in regular patterns.
D. Behavioral rules: Behavioral rules focus on tracking specific behaviors or activities over time and may be used to detect anomalous activities based on the user's behavior or system patterns. However, they are not specifically geared toward detecting outliers related to volume changes in event or flow data. Behavioral rules are typically used to detect abnormal activity or changes in user behavior rather than shifts in data volume patterns.

In conclusion, Anomaly rules are the most effective for detecting outliers based on volume changes that follow regular patterns. This makes B the correct answer. These rules help identify significant deviations from normal activity, which is essential for detecting potential security incidents or unusual system behaviors.

Question 10

Which authentication type in QRadar encrypts the username and password and forwards the username and password to the external server for authentication?

A. RADIUS authentication
B. Two-factor authentication
C. TACACS authentication
D. System authentication

Answer: A

Explanation:

In QRadar, RADIUS authentication is a protocol that handles the transmission of authentication requests to external servers while ensuring the username and password are encrypted during the process. This is achieved through the use of RADIUS (Remote Authentication Dial-In User Service), which securely forwards credentials to an external server (like a RADIUS server) for validation.

The RADIUS protocol encrypts the password (but not the username) before forwarding it to the external server for authentication. The system uses UDP as its transport protocol and typically integrates with network devices (such as routers, switches, VPNs, and firewalls) for user authentication. In the context of QRadar, it ensures secure communication of the login credentials and helps centralize authentication by utilizing a central server for verification.

Let's break down why the other options are incorrect:

B. Two-factor authentication: Two-factor authentication (2FA) enhances security by requiring two forms of authentication (e.g., something you know, like a password, and something you have, like a phone or a token). Although it provides additional security, it does not specifically deal with forwarding encrypted credentials to an external server for authentication in the same way as RADIUS.
C. TACACS authentication: TACACS (Terminal Access Controller Access-Control System) is another authentication protocol similar to RADIUS, but it is generally more focused on device management and command authorization, often used for network devices. While TACACS does encrypt both the username and password, it typically handles more granular control for device access management and is less commonly used for general user authentication compared to RADIUS.
D. System authentication: System authentication refers to the default local authentication mechanism used by QRadar for its own user management. This does not involve forwarding credentials to external servers. It handles authentication based on locally stored credentials and does not provide the encryption of credentials when interacting with external servers.

Thus, the authentication type that encrypts the username and password and forwards these credentials to an external server for authentication is RADIUS authentication (A). This makes A the correct answer for securely forwarding credentials to an external server in QRadar.