SPLK-1002 Data Models and Best Practices Practice Test 2 — 15 Questions

Question 1

A large e-commerce company uses Splunk to monitor its web application. They have a data model named 'Web_Transactions' that contains fields: status_code, response_time, uri, user_agent. The data model is accelerated with a 30-day time range. Recently, the operations team reported that the dashboard showing average response time by URI is loading slowly, taking over 30 seconds to display. Upon investigation, you find that the data model acceleration summary job is taking longer to complete and sometimes fails. The indexers have sufficient CPU and memory, but the disk I/O is high during the summary job. The volume of web logs is approximately 500 GB per day. Which action should the Splunk administrator take to improve dashboard performance?

Accepted Answer

Increase the maximum number of parallel searches for the data model acceleration job in the limits.conf.. Option B is correct because increasing the `max_concurrent_parallel_searches` for the data model acceleration job in `limits.conf` allows the summary process to use more parallel searches against the indexers, which can reduce the time it takes to build the acceleration summary. Since the indexers have sufficient CPU and memory but disk I/O is high, parallelizing the search workload can better utilize available resources and prevent the job from timing out, thereby improving dashboard performance.

Answer

Disable data model acceleration and create a report that runs a scheduled search every 30 minutes to pre-compute the averages.

Answer

Add more indexers to distribute the data and reduce the load per indexer.

Answer

Decrease the acceleration time range from 30 days to 7 days.

Question 2

Which TWO of the following are best practices when creating and using data models in Splunk?

Accepted Answer

Accelerate data models to improve search performance on large datasets.. Option A is correct because accelerating a data model pre-computes the data model's field values and stores them in a summary index, which significantly reduces search time when running reports or Pivot searches against large datasets. This is a core best practice for optimizing performance with data models in Splunk.

Answer

Always accelerate root events in a data model to ensure all data is pre-computed.

Answer

Define all possible fields in the data model to ensure maximum flexibility.

Answer

Use data model acceleration only when building Pivot reports.

Question 3

Refer to the exhibit. A user runs the search shown. The search returns results, but the user wants to use a data model to make future searches faster and more consistent. Which data model should the user select and what is the correct acceleration setting?

Accepted Answer

Use the CIM Web data model and accelerate the 'Web' dataset.. Option A is correct because the search shown uses fields like `status`, `action`, `uri`, and `method`, which are standard fields in the CIM Web data model. Accelerating the 'Web' dataset pre-computes the relevant field extractions and aggregations, making future searches faster and more consistent without requiring the user to manually define field extractions or transformations.

Answer

Use the CIM Web data model and select 'Accelerate All Datasets' from the settings.

Answer

Create a new data model called 'Access' and enable acceleration on root events.

Answer

Use the built-in 'Searches' data model to pre-compute the count and status fields.

Question 4

You are a Splunk administrator for a large e-commerce company. The security team frequently runs searches against the web access logs (sourcetype=access_combined) to investigate suspicious activity. These searches often take 5-10 minutes to complete, and the team is frustrated. You decide to implement a data model to accelerate these searches. After creating a data model based on the CIM Web model and enabling acceleration for the 'Web' dataset, you notice that the acceleration summary size grows to over 50 GB and the rebuild process takes more than an hour every night, causing some searches to time out during the rebuild window. What is the most effective way to address this issue?

Accepted Answer

Reduce the acceleration time range from 'All time' to 'Last 7 days' to limit the summary size and rebuild duration.. Option C is correct because reducing the acceleration time range from 'All time' to a shorter window like 'Last 7 days' directly limits the amount of data the acceleration summary must cover. This shrinks the summary size (under 50 GB) and shortens the nightly rebuild time, preventing search timeouts during the rebuild window while still accelerating the most relevant recent data for security investigations.

Answer

Increase the bucket size in the acceleration settings to reduce the number of buckets being rebuilt.

Answer

Create a custom data model that includes only the fields needed for security investigations and enable acceleration.

Answer

Disable acceleration and instead rely on the security team to use more focused time ranges.

Question 5

Which three of the following are best practices when working with Data Models in Splunk? (Choose three.)

Accepted Answer

Regularly rebuild data model acceleration summaries after making changes to the data model.. Constraints are essential for limiting the events in a data model to only those relevant to the dataset, which improves performance and accuracy. Rebuilding acceleration summaries after changes ensures that the pre-computed data reflects the updated model, preventing stale results. Defining fields using Eval expressions allows you to derive calculated values from raw data, enriching the dataset without modifying the underlying indexed data.

Answer

Always set acceleration to the maximum time range (e.g., All time) to ensure all data is pre-computed.

Answer

Design data models with a flat, non-hierarchical structure whenever possible to simplify acceleration.

Answer

Create root-level child fields that are independent of any specific dataset hierarchy to improve search efficiency.

Question 6

Which three options describe recommended practices for optimizing and maintaining data model acceleration? (Choose three.)

Accepted Answer

Set the acceleration time range to match the most common search timeframe for that data model.. Accelerating only frequently used data models conserves disk space and system resources by avoiding unnecessary summary builds. Setting the acceleration time range to match the most common search timeframe ensures that the accelerated data aligns with user query patterns, maximizing efficiency. Scheduling the acceleration summary rebuild during off-peak hours minimizes the performance impact on concurrent searches, as the rebuild process consumes significant CPU and I/O resources.

Answer

Use the `| datamodel` command to manually trigger acceleration rebuilds after major data additions.

Answer

Avoid using acceleration on data models that contain calculated fields, as they cannot be accelerated.

Answer

Increase the number of parallel search processes to automatically improve acceleration speed.

Question 7

Which four of the following are best practices for working with data models in Splunk? (Choose four.)

Accepted Answer

Use acceleration to improve search performance on large datasets.. Null is correct because data model acceleration pre-computes and stores summaries of the data, dramatically reducing search time on large datasets. This is a core best practice for optimizing performance when working with data models in Splunk.

Answer

Design data models to match the structure of your raw data as closely as possible.

Answer

Avoid using calculated fields within data models to reduce complexity.

Question 8

Drag and drop the steps to add a new data input using Splunk Web (e.g., monitor a log file) into the correct order.

Question 9

Drag and drop the steps to perform a Splunk software upgrade using the CLI into the correct order.

Question 10

Match each data input type to its description.

Question 11

Match each index type to its purpose.

Question 12

Which of the following is required to use data model acceleration for a Pivot report?

Accepted Answer

Check the 'Accelerate' box on the data model and set a time range. Option C is correct because data model acceleration is enabled by checking the 'Accelerate' box on the data model and setting a time range. Option A is wrong because summary indexing is not required. Option B is wrong because root objects are not the only requirement. Option D is wrong because acceleration is not configured via the datamodel command.

Answer

Create a data model with only root objects

Answer

Enable summary indexing

Answer

Use the `datamodel` command with `acceleration` parameter

Question 13

A user wants to use the Pivot interface to analyze web traffic data. Which data model should they select?

Accepted Answer

Web. The Pivot interface is designed to work with data models that have been properly defined and accelerated. The 'Web' data model is the correct choice because it is a standard, pre-built data model in Splunk that specifically models web traffic data, including fields like status, bytes, referrer, and user agent, which are essential for analyzing web traffic.

Answer

Network_Traffic

Answer

Authentication

Answer

Email

Question 14

When tagging events in Splunk to map them to a data model, which tag is used to associate events with a specific data model dataset?

Accepted Answer

tag::=. Option B is correct because in Splunk, the tag syntax `tag::=` is used to map events to a specific dataset within a data model. The tag key is the data model name, and the tag value is the dataset name, which allows Splunk's data model acceleration to correctly categorize events for reporting and pivot use.

Answer

tag::datamodel=<dataset>

Answer

tag::<datamodel>=<value>

Answer

tag::<dataset>=<datamodel>

Question 15

A data model has been accelerated but some Pivot reports are showing incomplete data. What is the most likely cause?

Accepted Answer

The acceleration summary range does not cover the full time range of the report.. Option B is correct because the acceleration summary range may not cover the full time range of the report, leading to incomplete data. Option A is wrong because normalization is not related. Option C is wrong because too many fields do not cause incomplete data. Option D is wrong because conflicting field extractions are not directly related.

Answer

The data model has too many fields.

Answer

The data model is not normalized.

Answer

The search time field extractions are conflicting.