SPLK-1002 Exam Dumps

Explanation:
Let's break down the command:

| datamodel Application_State All_Application_State search

This is a valid use of the datamodel command in Splunk:

datamodel is a generating command that retrieves data from a data model.
Application_State is the name of the data model.
All_Application_State is the specific dataset within the Application_State data model.
The word search tells Splunk to return raw events from that dataset.

✅ What this command does:
It retrieves events (not just summaries or pivots) from the All_Application_State dataset inside the Application_State data model.
This is commonly used in CIM-compliant data and accelerated data models.

❌ Why the other options are incorrect:

A. Events would return a report of sales by state
⛔ Irrelevant — nothing in the command specifies sales or any report logic.

C. Events will be returned from the data model named All_Application_State
⛔ Incorrect — All_Application_State is a dataset, not the data model. The data model is Application_State.

D. No events will be returned because the pipe should occur after the datamodel command
⛔ False — this is the correct syntax. | datamodel search is valid.

Explanation:

The timechart command in Splunk is used to create time-series visualizations. Its primary axis (x-axis) is always based on the _time field, which is implicitly included — you don’t need to specify it. Syntax example:

... | timechart count by status
This will produce a timechart showing the count of events over time, broken down by the status field.
The field after by becomes a split-by field, dividing the y-axis values by different categories.

Important Limitation:
You can specify only one field after the by clause in a timechart command.
This is because _time is fixed as the x-axis, and Splunk doesn't support multi-dimensional splits in timechart beyond one "by" field.

❌ Why the other options are incorrect:
A. because timechart doesn't support using a by clause — ❌ Incorrect. It does support one by field.
C. because one field would represent the x-axis and the other would represent the y-axis — ❌ Misleading. _time is always the x-axis; fields after by split the y-axis.
D. There is no limit specific to timechart — ❌ Incorrect. timechart does limit to only one by field.

Explanation:

The command:
sourcetype=access_combined | transaction JSESSIONID
uses the transaction command to group related events that share the same JSESSIONID.

✅ What this command does:
It groups events that share the same JSESSIONID value.
Creates a single "transaction event" that represents the entire session.
When you use | transaction , the following fields are automatically created for each transaction:

1. duration ✅
Represents the time difference between the first and last event in the transaction.

2. eventcount ✅
Indicates how many events were grouped into the transaction.

3. Grouping by JSESSIONID ✅
All events with the same JSESSIONID are merged into one transaction.

❌ What about maxspan?
A. An additional field named maxspan is created
❌ Incorrect — maxspan is a parameter, not a field.
You can use maxspan to limit the maximum duration of a transaction (e.g., | transaction JSESSIONID maxspan=30m), but it’s not a field that is automatically created.

Explanation

This search uses the transaction command to group events that share a common value for JSESSIONID into transactions1.

The transaction command assigns a duration field to each transaction, which is the difference between the latest and earliest timestamps of the events in the transaction1. The search then uses the timechart command to create a time-series chart of the average duration of each transaction1.

Therefore, option A is correct because it describes the search accurately.

Option B is incorrect because the search does not use the stats command or the pause field.

Option C is incorrect because the transaction command does not require the startswith and endswith options, although they can be used to specify how to identify the beginning and end of a transaction1.

Option D is incorrect because the transaction command does not have to be the last command in the search pipeline, although it is often used near the end of a search1.

Explanation:

Delimiter-based field extraction in Splunk is designed to parse structured text files where fields are separated by a specific character (e.g., commas, tabs, pipes).

Why CSV?
CSV (Comma-Separated Values) files use a delimiter (typically ,) to separate fields, making them ideal for this extraction method.

Example:
csv
timestamp,user,action
2023-10-01,admin,login
Splunk can split this into timestamp, user, and action fields using comma-delimited extraction.

Why Not the Others?
B) PDF → Unstructured/binary data; requires OCR or specialized extraction.
C) XML → Hierarchical format; use spath or XML-specific extractions.
D) JSON → Nested structure; use spath or automatic JSON extraction.

Key Takeaway:
✔ Use delimiter extraction for:
CSV, TSV, pipe-delimited (|), or other uniformly separated data.
✔ For JSON/XML, use:
Automatic parsing (JSON) or spath (XML/JSON).

Explanation:

In Splunk, when multiple event types are matched to the same event, each event type may have its own color and priority value. To determine which color is displayed, Splunk uses the priority assigned to the event type.

✅ Priority in Event Types:
Priority is an integer value from 1 (highest) to 10 (lowest).
The event type with the highest priority (lowest number) determines the color shown for the event in the search results.

Example:
If an event matches:
Event type A (priority 5, color red)
Event type B (priority 2, color blue)
➡️ The event will be displayed with the color blue (because event type B has a higher priority).

❌ Why the other options are incorrect:
A. Rank – Not a Splunk concept in this context.
B. Weight – Not applicable for event types.
D. Precedence – Not used for determining color; Splunk uses the term "priority."

Explanation:

The search:
spl
index=corndogs type=mysterymeat action=eaten | stats count by user

Does the following:
Filters events where:
index=corndogs
type=mysterymeat (only "mysterymeat" corndogs)
action=eaten (only eaten actions)
Aggregates results with stats count by user:
Counts how many times each user ate "mysterymeat" corndogs.

Why Not the Other Options?
A) Incorrect—counts are split by user, not corndog type.
C) Incorrect—only counts "mysterymeat" type (not all types).
D) Incorrect—no filter for "vegetarian" exists in the search.

Key Takeaway:
✔ stats count by field creates a table of counts grouped by the specified field.
✔ Filtering (type=mysterymeat) restricts the analysis to a subset of data.