Question 1

Why should the Response Time Composition Chart in AppResponse NOT be used on All Traffic or other large traffic Groups? (Choose all that apply.)

Accepted Answer

A, B, C

Explanation: The Response Time Composition Chart in AppResponse displays average performance metrics for the selected traffic group. When applied to a broad, heterogeneous group like "All Traffic," performance data from numerous different applications, clients, and servers is aggregated and averaged. This statistical averaging masks the true performance of individual components. Consequently, high-latency events or poorly performing applications (performance anomalies) are obscured or "smoothed out" by the large volume of normal, low-latency traffic. This makes the chart misleading for troubleshooting, as the root cause of a specific issue cannot be isolated. Effective analysis requires using the chart on narrowly defined, specific application groups.

Question 2

NetProfiler's Service Monitoring definitions allow users to:

Accepted Answer

A

Explanation: NetProfiler's Service Monitoring feature is designed to provide a business-centric view of IT performance. It allows administrators to define a "business service" by grouping together multiple related applications and network components. This aggregates their individual performance metrics into a single, consolidated dashboard for the service. This contextualizes raw network and application data, making it easier to understand the performance and availability of critical business functions, rather than just monitoring isolated IT elements.

Question 3

Which of the following statements are true regarding the grouping of elements in SteelCentral Portal?

Accepted Answer

D

Explanation: Grouping in SteelCentral Portal is a foundational feature for organizing and analyzing performance data. It allows administrators to combine multiple, related configuration items (such as servers, applications, or network devices) into a single logical entity. This grouping facilitates the aggregation of performance metrics (A), enables the display of this combined data in a single dashboard widget or report visualization (B), and supports the creation of nested, parent-child hierarchies for data roll-ups and drill-downs (C). Because all listed statements accurately describe core functionalities of element grouping within the platform, "All of the above" is the most comprehensive and correct answer.

Question 4

Which data source will have the highest priority for populating the End-User Experience Dashboard based on Application Models in Portal?

Accepted Answer

C

Explanation: The Riverbed Portal prioritizes data sources to provide the most accurate representation of the end-user's experience. AppInternals JavaScript provides Real User Monitoring (RUM) data by capturing metrics directly from the end-user's browser. This is the most direct and highest-fidelity source for metrics like page load times and browser rendering performance. Therefore, when data from the AppInternals JavaScript agent is available for an application model, Portal gives it the highest priority for populating the End-User Experience Dashboard over network-inferred sources.

Question 5

What is the minimum statistic reporting interval for AppResponse Standard Groups (Pre-computed Aggregates)?

Accepted Answer

A

Explanation: Riverbed AppResponse is designed to provide high-fidelity performance data. For its Standard Groups, which rely on pre-computed aggregates, the system collects and summarizes performance statistics at a base interval of one minute. This is the highest level of granularity available for historical reporting and analysis. All longer-term reporting intervals (e.g., 5-minute, 1-hour) are created by "rolling up" or further aggregating these foundational one-minute data points. Therefore, the minimum possible reporting interval is one minute.

Question 6

Which of the following best describes Advanced Group data in AppResponse?

Accepted Answer

B

Explanation: In Riverbed AppResponse, an "Advanced Group" is a user-defined logical entity used to classify and monitor traffic for a specific application, site, or set of servers. AppResponse processes the raw packets matching the group's criteria to extract detailed, structured performance metrics for individual application interactions. This processed information is known as transactional data, which includes metrics like response times for web page views, database queries, or file transfers. This granular, transaction-level data is the foundation for deep-dive performance analysis and troubleshooting within AppResponse.

Question 7

What is the best starting point for downstream discovery of relationships and hosts when creating Portal Application Models?

Accepted Answer

B

Explanation: The most effective starting point for downstream discovery in Riverbed Portal is the front-end hosts or their IP addresses. Application performance monitoring and modeling aim to trace the path of a user transaction from its origin. This journey begins when a user request hits a front-end component, such as a web server or a load balancer's virtual IP (VIP). By defining these front-end entities first, the discovery process can naturally follow the traffic flow "downstream" through the application servers, middleware, and ultimately to the back-end databases, creating a complete and accurate model of the entire service delivery chain.

Question 8

Which Portal UI feature allows you to show other metrics correlated to the element selected?

Accepted Answer

D

Explanation: The "Analytics" feature, typically accessed via a right-click context menu on an element within the Riverbed Portal UI, is specifically designed for this purpose. When an element such as a host, application, or conversation is selected, the Analytics menu presents a list of available reports and views. Selecting an option from this menu allows the user to pivot their analysis and view different, correlated metrics for that specific element, such as seeing top talkers for an application or performance metrics for a specific host. This provides a powerful, context-sensitive way to investigate issues by exploring related data sets.

Question 9

Which types of data are carried forward when you generate a report using 'Traffic in Context' in NetProfiler?

Accepted Answer

A

Explanation: The 'Traffic in Context' feature in NetProfiler is a powerful drill-down tool designed to maintain the analytical context when pivoting from one report to another. When a user generates a new report using this feature, NetProfiler preserves the complete context of the original report. This includes both the specific time frame being analyzed and all previously applied traffic criteria or filters (e.g., hosts, applications, conversations). This functionality enables a seamless and focused investigation of a specific traffic pattern or performance issue without requiring the user to manually re-apply the same parameters for each new analytical view.

Question 10

Why are point tools ineffective in managing digital experience?

Accepted Answer

A

Explanation: Point tools are specialized for monitoring individual IT domains like networks, applications, or infrastructure. Digital Experience Management (DEM) requires a holistic, end-to-end view of the entire service delivery chain as perceived by the end-user. The primary ineffectiveness of point tools is that they operate in silos. When troubleshooting a user experience issue, this siloed data creates "tunnel vision," where each IT team can only see their specific component. This prevents effective correlation of data across domains, leading to finger-pointing and significantly longer resolution times, as the true root cause often lies in the interactions between components.

Question 11

Which parameter(s) need to be specified in order for AppResponse WTA to perform user tracking?

Accepted Answer

B

Explanation: To perform user tracking, AppResponse Web Transaction Analysis (WTA) requires a complete definition of both the application and the tracking method. First, the web application must be defined by its Server IPs and Server Ports so AppResponse knows which traffic to analyze. Second, a user identification rule must be configured for that application. This rule specifies the User Key or Session Key (e.g., a cookie name like JSESSIONID or a URL parameter) used to uniquely identify a user's session, and a URL Pattern to define the specific pages where this key should be extracted. All these parameters are essential for the feature to function correctly.

Question 12

Which host group type is used by default for the End User component in NetProfiler Service definitions?

Accepted Answer

B

Explanation: In the Service Definition wizard of SteelCentral NetProfiler (and NetExpress), each tier is pre-assigned a default host-group type. The End User tier is automatically populated with host groups of type “ByLocation,” because client hosts are normally grouped by site or geography. The Application and Infrastructure tiers use other defaults (ByInternalHosts and ByDevice, respectively). Therefore, the only host-group type chosen by default for the End User component is ByLocation.

Question 13

Which factors contribute to the effect of Congestion in application response time?

Accepted Answer

C

Explanation: Congestion occurs when the demand for a network resource, typically bandwidth, exceeds its available capacity. This leads to the formation of queues in network devices (like routers and switches), increased delay, and potential packet loss. The primary factors causing this state are the presence of competing traffic flows from other applications or users ("Other traffic on the network") and network designs where the aggregate potential traffic from endpoints is greater than the capacity of a shared link ("link oversubscription"). Both factors directly contribute to the network overload that defines congestion, thereby degrading application response time.

Question 14

Which of the following are prerequisites for discovering relationships between hosts in Portal Application Models? (Choose all that apply.)

Accepted Answer

C, E

Explanation: Riverbed Portal's Application Models feature discovers and maps the communication relationships between hosts by analyzing network traffic data. The primary data sources for this discovery process are flow data and packet data. Flow-based data from NetProfiler provides information on conversations between IP addresses (hosts), while packet-based data from AppResponse offers deep packet inspection for high-fidelity application identification and communication mapping. Official Riverbed documentation explicitly states that Portal uses data from both NetProfiler and AppResponse for application discovery and dependency mapping, making them direct prerequisites.

Question 15

Which of the following workflows is supported by Packet Capture and Inspection?

Accepted Answer

B

Explanation: Packet Capture and Inspection is a passive monitoring technique that involves capturing network traffic and performing deep packet inspection (DPI). This process decodes the captured data at various layers of the network stack, including the TCP/IP and application layers. By analyzing these decodes, network and application performance metrics such as network latency, server response time, retransmissions, and application-specific transaction details can be derived. This granular visibility is fundamental to the workflow of performance monitoring and troubleshooting complex application issues.

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