Splunk SPLK-1002 Core Certified Power User Exam Dumps and Practice Test Questions Set 5 Q61-75
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Question 61
Which SPL command is used to sort events or field values in ascending or descending order?
A) sort
B) table
C) stats
D) dedup
Answer: A
Explanation:
In Splunk, organizing data is critical for both visualization and analysis. A specific SPL command allows analysts to sort events or the values of a field in either ascending or descending order. Sorting helps prioritize events, identify top occurrences, or visualize trends over time. For instance, analysts can sort error events by timestamp in ascending order to track their occurrence over time, or sort sales data by revenue in descending order to quickly identify high-performing products or regions. Sorting ensures that analysts can efficiently focus on the most important events, values, or metrics within large datasets.
One incorrect command formats selected fields into a table but does not inherently order the data. Another choice aggregates data using statistical functions, producing summary metrics but not sorting raw events. A third incorrect command removes duplicate events based on field values, which reduces redundancy but does not organize events in a particular order.
The correct command is widely used in operational, security, and business analytics. In operations, sorting server logs by CPU usage or error count helps identify critical issues. Security analysts can sort login attempts by the number of failures or by user activity to pinpoint suspicious activity. In business contexts, sorting transactions by revenue, sales volume, or date helps identify top customers, peak sales periods, or underperforming products. By ordering events or values, analysts can make informed decisions and prioritize interventions effectively.
It supports multiple field sorting, allowing analysts to specify primary, secondary, or tertiary sort keys. For example, events can be sorted first by host and then by timestamp within each host. It also supports both ascending and descending order and can handle large datasets efficiently, making it suitable for dashboard visualizations or detailed investigation workflows.
Using this command ensures clarity in analysis, enables trend identification, and improves readability of dashboards, reports, or tables. Unlike table, stats, or dedup, which format, summarize, or remove duplicates, this command focuses solely on ordering events or field values to enhance interpretability and analytical efficiency. Sorting is particularly important when preparing data for visualization, ranking top occurrences, or preparing datasets for further processing.
the command sorts events or field values in ascending or descending order, improving clarity, prioritization, and trend analysis. It is essential in operational, security, and business contexts for identifying critical events, top performers, and sequence-dependent trends.
Therefore, the correct answer is sort.
Question 62
Which SPL command is used to create a time-based visualization showing trends in numeric data over a time span?
A) timechart
B) chart
C) table
D) stats
Answer: A
Explanation:
In Splunk, understanding trends over time is a fundamental analytical requirement. A specific SPL command allows analysts to create time-based visualizations of numeric data, showing how metrics evolve across a specified time span. This command automatically bins events into time intervals and calculates aggregation functions such as sum, count, average, or distinct counts for each interval. For example, it can be used to display web traffic per hour, CPU usage per minute, or sales per day. By visualizing trends over time, analysts can detect anomalies, performance issues, or business patterns.
One incorrect command aggregates metrics across multiple fields but is not inherently time-based. Another choice formats selected fields into a table for display but does not visualize trends over time. A third incorrect command calculates statistics without automatically binning or grouping by time, making it less suitable for temporal analysis.
The correct command is widely used in operational, security, and business analytics. In operations, it helps track system metrics like CPU, memory, or disk usage over time, enabling performance monitoring and anomaly detection. Security analysts use it to track login attempts, failed authentication events, or alert counts across time intervals to detect suspicious activity or attacks. In business contexts, it helps visualize sales, revenue, or website visits over time, supporting trend analysis, forecasting, and reporting.
It supports multiple aggregation functions and can handle multiple grouping fields for more complex visualizations. Analysts can customize time intervals, such as seconds, minutes, hours, days, or months, to match the granularity required for their analysis. The output can be displayed in line charts, bar charts, or area charts in dashboards and reports, providing an intuitive understanding of temporal patterns.
Using this command ensures accurate temporal analysis, supports anomaly detection, and enables visual storytelling of data trends. Unlike chart, table, or stats, which either focus on horizontal aggregation, formatting, or general statistics, this command explicitly organizes data by time, making it indispensable for monitoring and reporting over intervals.
the command creates a time-based visualization showing numeric data trends over a specified time span. It bins events, calculates aggregates, and enables operational, security, and business trend analysis. It is essential for detecting patterns, monitoring metrics, and visualizing temporal data.
Therefore, the correct answer is timechart.
Question 63
Which SPL command is used to calculate the maximum or minimum value of a numeric field grouped by one or more fields?
A) stats max/min
B) eval
C) table
D) dedup
Answer: A
Explanation:
In Splunk, analysts often need to identify peak or lowest values within a dataset, such as maximum CPU usage, minimum response time, or highest sales amount. A specific SPL command allows calculation of the maximum or minimum value of a numeric field while grouping results by one or more fields. This aggregation is critical in operational, security, and business contexts, helping teams pinpoint extremes, assess performance, or prioritize actions. For example, calculating the maximum memory usage per server identifies the most heavily utilized systems, while calculating minimum transaction times per application reveals efficiency levels.
One incorrect command evaluates expressions to create or transform fields but does not aggregate values across events. Another choice formats selected fields in a table for presentation, providing readability but not calculating maximum or minimum values. A third incorrect command removes duplicate events without performing aggregation, making it unsuitable for extreme value identification.
The correct command is widely used in operational, security, and business analytics. In operations, analysts calculate maximum CPU, memory, or network utilization to detect overloading or potential failures. Security analysts use it to identify maximum failed login attempts or alert occurrences for threat prioritization. Business analysts calculate maximum sales, revenue, or customer activity per category or region to determine top performers or critical periods.
It supports grouping by multiple fields simultaneously, enabling multidimensional aggregation such as maximum transaction value per store per region. The results can be integrated with table, chart, or timechart for visualization, dashboards, and reporting. Additionally, combining with sort, dedup, or eval allows refined data preparation and accurate analysis.
Using this command ensures precise identification of extremes, supports operational monitoring, anomaly detection, and business performance evaluation. Unlike eval, table, or dedup, which transform, format, or filter data, stats max/min calculates aggregated extreme values across groups, providing actionable insights for decision-making.
the command calculates the maximum or minimum value of a numeric field grouped by one or more fields. It is essential for operational, security, and business analysis to identify extremes, monitor performance, and highlight critical trends.
Therefore, the correct answer is stats max/min.
Question 64
Which SPL command is used to remove duplicate events based on one or more fields?
A) dedup
B) stats
C) table
D) eval
Answer: A
Explanation:
In Splunk, datasets often contain redundant events due to repeated logging, system retries, or multiple sources. A specific SPL command allows analysts to remove duplicate events based on one or more fields while preserving the first occurrence of each unique combination. This is essential to prevent overcounting, reduce noise, and create a clean dataset for reporting, visualization, or further analysis. For example, removing duplicate login attempts based on user ID and timestamp ensures accurate counts of unique logins, or deduplicating transaction logs prevents inflated revenue calculations.
One incorrect command aggregates events and calculates statistics, but does not specifically remove duplicates. Another choice format selects fields for display without altering the dataset’s uniqueness. A third incorrect command evaluates expressions or creates new fields without addressing duplicate events.
The correct command is widely used in operational, security, and business analytics. In operations, deduplicating system logs based on host and error code allows accurate identification of unique incidents. Security analysts use it to remove repeated alerts from the same source IP, preventing alert fatigue and ensuring meaningful monitoring. In business analytics, deduplicating transaction records ensures accurate sales reports, customer counts, or inventory metrics.
It supports specifying multiple fields to define uniqueness, allowing flexibility in identifying duplicates based on a combination of attributes. By default, it keeps the first occurrence of each unique event, but it can also be combined with sort to control which event is retained. It can also be used with stats, tables, or charts to produce clean outputs for dashboards and reporting.
Using this command improves accuracy, reduces redundancy, and ensures that subsequent analysis, visualizations, and metrics reflect unique occurrences rather than repeated events. Unlike stats, table, or eval, which summarize, format, or transform data, this command directly removes duplicates, making it a foundational tool in preprocessing and cleansing datasets for reliable analytics command removes duplicate events based on one or more fields, preserving only the first occurrence. It is essential in operational, security, and business analytics to ensure accurate reporting, reduce noise, and clean datasets for analysis, dashboards, and visualizations.
Therefore, the correct answer is dedup.
Question 65
Which SPL command is used to create a new field by performing calculations or transformations on existing fields?
A) eval
B) stats
C) table
D) mvexpand
Answer: A
Explanation:
In Splunk, raw event data often requires transformation, calculation, or derivation to create meaningful metrics. A specific SPL command allows analysts to create new fields by performing arithmetic, string, Boolean, or conditional operations on existing fields. For example, creating a “response_time_seconds” field by converting a “response_time_ms” field from milliseconds to seconds, or deriving a “status_flag” field based on conditions in an existing status code, allows for richer analysis, filtering, and visualization. This command is highly versatile and foundational for data enrichment within searches.
One incorrect command aggregates events to calculate metrics across groups but does not allow per-event field derivation or inline transformation. Another choice formats selected fields into a table without performing calculations or creating new fields. A third incorrect command expands multivalue fields into individual events but does not transform or calculate field values.
The correct command is widely used in operational, security, and business analytics. In operations, analysts can calculate derived metrics like CPU usage percentage, network latency ratios, or normalized memory values. Security analysts can calculate fields such as time since last login, failed login ratios, or risk scores per user. Business analysts use it to create metrics like profit margin, customer lifetime value, or transaction averages. By creating new fields, analysts can tailor raw data to specific analytical objectives, enabling more precise reporting and visualization.
It supports arithmetic operations, conditional statements, string manipulations, and Boolean logic, providing broad flexibility in deriving new fields. The resulting fields are immediately usable in downstream commands like stats, chart, table, or timechart, enabling seamless integration with aggregation and visualization workflows. Analysts can also use eval with functions such as if, coalesce, len, round, or substr to create complex transformations that meet specific analytical requirements.
Using this command ensures data enrichment, simplifies complex calculations, and allows event-level transformation without altering the underlying raw data. Unlike stats, table, or mvexpand, which aggregate, format, or expand data, this command focuses on creating new fields per event, making it indispensable for customization and analysis of raw Splunk events.
The command creates new fields by performing calculations or transformations on existing fields. It is critical for operational, security, and business analytics, enabling enriched, customized, and actionable data ready for visualization, aggregation, and reporting.
Therefore, the correct answer is eval.
Question 66
Which SPL command is used to combine multiple statistical functions in a single search, such as count, sum, average, and distinct count?
A) stats
B) eval
C) table
D) dedup
Answer: A
Explanation:
In Splunk, comprehensive analysis often requires calculating multiple statistical measures simultaneously. A specific SPL command allows analysts to combine functions such as count, sum, average, maximum, minimum, and distinct count within a single search, producing aggregated metrics grouped by one or more fields. For example, calculating total sales, average revenue per transaction, and the number of unique customers per region in one search helps business analysts quickly generate multidimensional insights. Similarly, operational teams can summarize error counts, average response times, and maximum memory usage per host in one pipeline.
One incorrect command evaluates expressions or derives new fields but does not aggregate multiple statistical functions across events. Another choice formats selected fields for display without performing calculations or aggregation. A third incorrect command removes duplicate events without producing statistical summaries, making it unsuitable for aggregated analysis.
The correct command is widely used in operational, security, and business analytics. In operations, it allows calculation of maximum CPU usage, average disk latency, and total I/O per host simultaneously, providing comprehensive system metrics. Security analysts can calculate total failed logins, distinct users, and average event severity in one search to detect anomalies or trends. Business analysts can calculate total revenue, average transaction value, and distinct product counts in a single pipeline, streamlining reporting and decision-making.
It supports multiple grouping fields, allowing multidimensional aggregation such as counts per host per application, or revenue per product per region. It integrates seamlessly with visualization commands like chart, timechart, and table, providing actionable insights directly from aggregated statistics. Analysts can also combine it with eval or where for conditional calculations, enhancing flexibility and precision.
Using this command ensures efficiency, clarity, and comprehensive analysis by calculating multiple metrics in one step. Unlike eval, table, or dedup, which focus on per-event transformations, formatting, or deduplication, this command aggregates data across events while supporting multiple statistical functions, making it essential for detailed, multidimensional analysis.
The command combines multiple statistical functions in a single search, enabling operational, security, and business analytics to generate comprehensive metrics efficiently. It supports grouping, multidimensional aggregation, and integration with visualization commands, providing actionable insights from aggregated data.
Therefore, the correct answer is stats.
Question 67
Which SPL command is used to filter events based on specific field values and keep only those events that match the condition?
A) search
B) where
C) eval
D) table
Answer: A
Explanation:
In Splunk, filtering events is essential for narrowing down relevant data from potentially millions of logged events. A specific SPL command allows analysts to restrict results to events that contain particular field values, keywords, or patterns, directly influencing the dataset that will be analyzed. For example, filtering logs to include only events where the status is “error,” where the host matches a particular server, or where a username matches a specified value allows analysts to focus on relevant incidents or conditions without unnecessary noise. This command operates at the beginning of a search pipeline and can utilize both field-value matching and keyword-based filtering, making it a versatile and powerful tool for efficient data retrieval.
One incorrect command applies logical conditions to fields but does not perform a general text or keyword search across multiple fields. While it is useful for evaluating conditions after extraction or calculation, it is not a primary search filter. Another choice calculates or derives new fields but does not inherently filter events based on specific matches. A third incorrect command formats selected fields for tabular display without reducing the dataset based on matching criteria.
The correct command is widely used in operational, security, and business analytics. In operations, filtering logs to include only critical errors allows teams to prioritize investigations and identify systemic issues. In security, filtering alerts to include only failed logins or specific attack types enables focused monitoring and incident response. Business analysts can filter transaction logs to include only purchases over a certain value or sales in specific regions, improving reporting accuracy and insight. By applying field-value filtering early in the search pipeline, analysts reduce computational overhead and ensure that downstream commands operate only on relevant events.
It supports combining multiple conditions using Boolean operators such as AND, OR, and NOT. For example, analysts can retrieve events where the status is “error” AND the host matches a certain server OR the user belongs to a specific department. It can also handle wildcards and partial matches, increasing flexibility in searches across dynamic or loosely structured datasets.
Using this command ensures efficient searches, reduces irrelevant data, and provides precise input for subsequent commands like stats, chart, table, or eval. Unlike where, eval, or table, which either filter based on expressions, calculate new fields, or format data, this command is primarily responsible for restricting event datasets at the beginning of a search, making it critical for targeted analysis.
The command filters events based on field values and keywords, returning only matching events. It is widely used in operational, security, and business contexts to narrow down data for analysis, reduce noise, and provide a relevant dataset for visualization, aggregation, and reporting.
Therefore, the correct answer is search.
Question 68
Which SPL command is used to display only selected fields from the search results while maintaining the original event data?
A) fields
B) table
C) stats
D) eval
Answer: A
Explanation:
In Splunk, event data often contains numerous fields, many of which may not be relevant for a particular analysis. A specific SPL command allows analysts to include or exclude certain fields from the output while keeping the underlying event data intact. This is particularly useful when preparing dashboards, reports, or visualizations that require only specific fields for clarity or focus. For example, analysts may choose to display only host, source, and error code fields, while excluding additional metadata such as raw log content or internal indexing fields, which are not necessary for the analysis.
One incorrect command format selected fields into a table for presentation, but does not control inclusion or exclusion at the event level; it focuses on structured visualization rather than filtering fields. Another choice aggregates data across events to calculate metrics such as count, sum, or average, rather than controlling the fields displayed in the raw event data. A third incorrect command evaluates expressions to derive new fields, but it does not restrict which existing fields appear in the results.
The correct command is widely used in operational, security, and business analytics. In operations, displaying only critical fields like host, timestamp, and error message allows analysts to quickly understand and investigate system issues without unnecessary clutter. Security analysts often use it to include only relevant fields such as source IP, destination IP, and action in security logs for focused monitoring and reporting. In business analytics, analysts may display only transaction ID, product, and revenue fields for dashboards or reports while excluding less relevant details, improving clarity and interpretability.
It supports both inclusion and exclusion of fields. Analysts can explicitly specify the fields to include or use the command with a minus sign to exclude certain fields. This flexibility allows for streamlined outputs tailored to specific analytical needs while maintaining full event integrity for downstream processing. Combined with tables, charts, or stats, it allows precise control over which data appears in visualizations, summaries, or exports.
Using this command ensures cleaner search results, reduces visual noise, and supports focused analysis without modifying or losing the original event data. Unlike table, stats, or eval, which focus on formatting, aggregation, or creating new fields, this command directly controls which fields are included in search results, providing a practical method for customizing outputs.
The command selectively includes or excludes fields in search results while keeping event data intact. It is critical in operational, security, and business analytics to focus on relevant fields, improve clarity, and prepare data for downstream visualization and reporting.
Therefore, the correct answer is fields.
Question 69
Which SPL command is used to combine events from two searches based on a common field, enriching the primary search with additional information?
A) join
B) append
C) stats
D) eval
Answer: A
Explanation:
In Splunk, combining data from multiple sources is often necessary for comprehensive analysis. A specific SPL command allows analysts to merge results from two searches based on a common field, enriching the primary search with additional information from the secondary search. This is conceptually similar to a database join, where related rows from two tables are combined using a shared key. For example, server logs can be joined with configuration data using host ID to provide additional context for each event, or user activity logs can be enriched with department information by joining on user ID. This command creates a horizontally merged dataset, preserving the original events while adding fields from the secondary search.
One incorrect command stacks events vertically without matching keys, which does not provide field-level enrichment. Another choice aggregates or summarizes events using statistical functions rather than combining datasets based on shared fields. A third incorrect command derives or transforms fields per event but does not merge datasets from multiple searches.
The correct command is widely used in operational, security, and business analytics. In operations, joining logs with configuration or inventory data provides detailed context for monitoring and troubleshooting. Security analysts can combine threat intelligence feeds with firewall or endpoint logs to correlate IP addresses with known threats. Business analysts can join transactional data with customer demographics to enrich insights for reporting and decision-making.
It supports one-to-one and one-to-many relationships and allows specifying which fields from the secondary search to include. Analysts can also control the type of join, such as inner or left join, to determine which events are retained in the result. Subsearches can be used as the secondary search, enabling dynamic joins based on recent or filtered data.
Using this command ensures comprehensive datasets, enhances event-level context, and supports multi-source analysis. Unlike append, stats, or eval, which stack, aggregate, or transform events, this command horizontally merges data based on a key field, providing enriched search results for analysis, reporting, and visualization.
The command combines events from two searches using a common field, enriching the primary dataset with additional fields. It is essential in operational, security, and business analytics for correlation, context enrichment, and comprehensive analysis across datasets.
Therefore, the correct answer is join.
Question 70
Which SPL command is used to calculate the distinct count of a field grouped by one or more other fields?
A) stats dc
B) eval
C) table
D) dedup
Answer: A
Explanation:
In Splunk, it is often necessary to determine the number of unique occurrences of a field within a dataset. A specific SPL command allows analysts to calculate the distinct count of a field, grouping results by one or more other fields. This functionality is essential in operational, security, and business analytics for identifying unique entities, understanding diversity within data, and avoiding overcounting. For example, calculating the distinct number of users accessing a host, distinct IP addresses connecting to a server, or distinct products purchased per region provides insights into distribution, user behavior, or operational coverage.
One incorrect command evaluates expressions or creates new fields on a per-event basis but does not aggregate data to produce distinct counts. Another choice formats selected fields into a table, improving readability but without performing aggregation or counting. A third incorrect command removes duplicate events but does not provide a grouped count of unique values across events, limiting its utility for analyzing diversity or uniqueness.
The correct command is widely applied in operational, security, and business contexts. In operations, it helps identify the number of unique servers affected by a particular error code or the number of unique processes consuming excessive resources. In security, analysts can use it to determine distinct user accounts involved in failed logins, or the number of unique source IPs targeting a firewall, supporting incident prioritization and threat analysis. In business analytics, calculating distinct customers per product, distinct orders per store, or unique transaction types enables accurate reporting and informed decision-making.
It supports grouping by multiple fields, allowing multidimensional analysis such as distinct users per application per region. This command integrates seamlessly with other aggregation functions such as sum, average, or max, enabling comprehensive reporting. Analysts can also combine it with where, eval, or sort for refined data selection and conditional calculations, enhancing flexibility and accuracy in large datasets.
Using this command ensures precise analysis of unique occurrences while preserving event-level context. Unlike eval, table, or dedup, which focus on transformations, formatting, or event-level deduplication, this command aggregates across events to produce distinct counts, providing actionable insights about diversity and coverage in datasets.
The command calculates the distinct count of a field grouped by one or more fields, providing critical insights in operational, security, and business analytics. It supports multidimensional grouping, integration with other aggregation functions, and refined filtering, enabling analysts to measure uniqueness, diversity, and distribution effectively.
Therefore, the correct answer is stats dc.
Question 71
Which SPL command is used to create a horizontal summary table showing values of one field aggregated by another field?
A) chart
B) stats
C) table
D) eval
Answer: A
Explanation:
In Splunk, visualizing relationships between fields in a summarized format is important for detecting patterns, trends, and correlations. A specific SPL command allows analysts to create a horizontal summary table where the values of one field are aggregated by another field. This command produces a pivoted or matrix-like output, which is useful for identifying relationships, comparing metrics across categories, and generating visualizations. For example, sales revenue can be aggregated per product category across different regions, or error counts per application across multiple hosts can be displayed horizontally, providing clear insights into distribution patterns.
One incorrect command aggregates data across fields but produces a vertical summary rather than a pivoted, horizontal view. Another choice formats selected fields for display without performing aggregation, limiting its utility for summarizing values by a dimension. A third incorrect command evaluates or creates new fields but does not produce a horizontal summary table for comparative analysis.
The correct command is widely used in operational, security, and business analytics. In operations, it allows horizontal visualization of error counts or performance metrics per host or application, enabling teams to quickly identify problem areas. Security analysts use it to display the number of security alerts per attack type across different sources, supporting threat prioritization and correlation. Business analysts can aggregate metrics like revenue or quantity sold per product category across regions or stores, providing insights for decision-making, forecasting, and reporting.
It supports multiple aggregation functions, including count, sum, average, maximum, minimum, and distinct count, providing flexibility for detailed analysis. Analysts can group by multiple fields simultaneously to create multidimensional summary tables, such as combining product and region metrics. The command also integrates seamlessly with visualization tools, enabling direct use in charts, dashboards, and reports.
Using this command improves clarity, supports comparative analysis, and provides a concise summary of relationships between fields. Unlike stats, table, or eval, which focus on vertical aggregation, event formatting, or field transformations, this command pivots data horizontally for easier interpretation of category-based metrics.
The command creates a horizontal summary table aggregating one field by another, facilitating operational, security, and business analysis. It supports multiple aggregation functions, multidimensional grouping, and visualization, enabling analysts to detect trends, patterns, and relationships efficiently.
Therefore, the correct answer is chart.
Question 72
Which SPL command is used to calculate cumulative metrics, such as a running total or running average, across events?
A) accum
B) stats
C) eval
D) table
Answer: A
Explanation:
In Splunk, tracking cumulative metrics is essential for understanding progression over time, analyzing trends, and monitoring sequential activity. A specific SPL command allows analysts to calculate running totals, running averages, or other cumulative metrics across events while preserving the original order of events. For example, cumulative sales over a period, running counts of failed login attempts, or ongoing error occurrences can be tracked, providing insight into trends, growth, or operational status. This ensures that analysts can assess incremental changes and cumulative effects without losing event-level context.
One incorrect command aggregates events by producing summary statistics but does not calculate sequential cumulative values per event. Another choice evaluates expressions or creates new fields but does not inherently track running totals across events. A third incorrect command formats selected fields for display without performing cumulative calculations, making it unsuitable for progressive metrics.
The correct command is widely used in operational, security, and business analytics. In operations, tracking cumulative resource consumption per host or application helps identify peak usage patterns or performance degradation. Security analysts can monitor cumulative failed login attempts per user to detect potential account compromise or brute-force attacks. Business analysts track cumulative revenue, transactions, or customer acquisition to assess progress toward goals, identify trends, and forecast outcomes.
It preserves the order of events, ensuring accurate accumulation over time, and can work with multiple numeric fields simultaneously. Analysts can combine it with sort, eval, or where to refine datasets or control event order, enabling precise cumulative calculations. The resulting field can be visualized in timecharts, dashboards, or reports, providing actionable insights for decision-making.
Using this command ensures accuracy in running totals, trends, and sequential metrics. Unlike stats, eval, or table, which aggregate, transform, or format data without tracking sequence, this command focuses on cumulative analysis across events while retaining event-level context, making it critical for temporal or progressive evaluations.
The command calculates cumulative metrics, such as running totals or averages, across events while preserving order. It is essential for operational, security, and business analytics to track trends, progression, and cumulative activity, enabling detailed monitoring, analysis, and reporting.
Therefore, the correct answer is accum.
Question 73
Which SPL command is used to calculate the standard deviation of a numeric field grouped by one or more fields?
A) stats stdev
B) eval
C) table
D) dedup
Answer: A
Explanation:
In Splunk, understanding variability within datasets is crucial for operational, security, and business analytics. A specific SPL command allows analysts to calculate the standard deviation of a numeric field, grouped by one or more fields, providing insights into the spread or dispersion of values relative to their mean. This is important for identifying anomalies, performance inconsistency, or variability in key metrics. For example, calculating the standard deviation of CPU usage per host identifies systems with highly fluctuating loads, while computing standard deviation of transaction amounts per store can reveal irregular business patterns.
One incorrect command evaluates expressions to create new fields but does not aggregate or calculate the spread across multiple events. Another choice formats selected fields into a table, improving readability but not performing statistical calculations. A third incorrect command removes duplicate events without providing insight into the variability of data, limiting its usefulness for performance or trend analysis.
The correct command is widely applied in operational, security, and business contexts. In operations, it helps identify hosts or applications with inconsistent performance, allowing teams to focus on areas requiring attention. Security analysts can calculate the standard deviation of login attempts per user to detect unusual behavior that deviates significantly from the norm. Business analysts can measure variability in revenue, transaction size, or customer purchases per category, aiding risk assessment, trend analysis, and strategic planning.
It supports grouping by multiple fields, enabling multidimensional statistical analysis such as standard deviation of sales per region per product. This integration allows combining with other statistical functions like count, sum, or average to produce comprehensive reports. Analysts can also use where or eval to pre-filter data or create derived fields for more precise calculations.
Using this command ensures accurate insight into the consistency, risk, or variability of a metric. Unlike eval, table, or dedup, which focus on per-event calculations, formatting, or removing duplicates, this command aggregates data across events to quantify dispersion, providing actionable intelligence for operational monitoring, security detection, and business analysis.
The command calculates the standard deviation of a numeric field grouped by one or more fields, providing essential insights into variability and consistency. It is critical in operational, security, and business analytics for anomaly detection, performance monitoring, and risk assessment, supporting multidimensional grouping and integration with other metrics.
Therefore, the correct answer is stats stdev.
Question 74
Which SPL command is used to create alerts, reports, or dashboards from the results of a saved search?
A) savedsearch
B) stats
C) eval
D) table
Answer: A
Explanation:
In Splunk, automation and operational efficiency require the ability to reuse search results for monitoring, reporting, and visualization. A specific SPL command allows analysts to reference saved searches, enabling the creation of alerts, reports, or dashboards without rerunning the original search manually. This command ensures consistency, reduces repetitive effort, and allows real-time or scheduled monitoring based on pre-defined search logic. For example, a saved search detecting failed logins can be used to trigger security alerts, display a dashboard showing trends in login failures, or generate a scheduled report for management.
One incorrect command aggregates metrics across events but does not reference saved searches or facilitate automated alerts. Another choice derives new fields for analysis but does not connect to saved searches for dashboard or reporting purposes. A third incorrect command formats fields into tables for presentation, but it is not designed for alerting or referencing previously saved search logic.
The correct command is widely used in operational, security, and business analytics. In operations, analysts can create dashboards monitoring server performance based on a saved search, ensuring continuous oversight without rewriting complex SPL queries. Security teams can use saved searches to trigger alerts when unusual activity or threat indicators are detected, maintaining proactive monitoring. Business analysts can reference saved searches to generate consistent reports on sales trends, revenue, or customer behavior, facilitating automated reporting to stakeholders.
It supports scheduling, real-time alerts, and dashboard panel integration. Analysts can also pass parameters to saved searches to customize execution based on specific criteria, such as time ranges or server groups, making the process flexible and scalable. The command preserves the original search logic while enabling its reuse in multiple operational and analytical contexts.
Using this command ensures efficiency, consistency, and automation in monitoring and reporting. Unlike stats, eval, or table, which calculate, transform, or format data, this command focuses on leveraging pre-defined searches for ongoing operational, security, or business purposes, reducing human error and improving responsiveness.
The command references saved searches to create alerts, reports, or dashboards, supporting automation, reuse, and consistent monitoring. It is essential in operational, security, and business analytics for real-time monitoring, scheduled reporting, and dashboard visualizations.
Therefore, the correct answer is savedsearch.
Question 75
Which SPL command is used to search for a field value within a range of numbers or dates?
A) search with range operators
B) eval
C) table
D) dedup
Answer: A
Explanation:
In Splunk, analyzing data often requires filtering events based on whether a field value falls within a specific numerical or temporal range. A specific SPL command allows analysts to apply range conditions using operators such as greater than, less than, greater than or equal to, or less than or equal to. This capability is essential for focusing on events that meet certain criteria, such as monitoring CPU usage above a threshold, identifying transactions within a particular amount, or detecting errors occurring within a time window. By applying range filtering, analysts reduce noise, improve relevance, and enable more targeted analysis.
One incorrect command evaluates expressions but does not inherently filter events within numeric or date ranges. Another choice formats fields for display in tables but does not perform range-based filtering. A third incorrect command removes duplicate events but does not filter events based on range conditions, making it unsuitable for analyzing specific thresholds or intervals.
The correct command is widely used in operational, security, and business analytics. In operations, filtering metrics like memory utilization between 70% and 90% helps identify systems approaching critical thresholds. Security analysts use range conditions to detect failed login attempts exceeding a certain number within a time window, or events occurring between specific dates, supporting anomaly detection and threat investigation. Business analysts apply range filters to transactions within a revenue threshold or orders within a date range, improving reporting accuracy and decision-making.
It supports combining multiple range conditions using Boolean operators, enabling complex searches that target precise criteria. Analysts can use it with numeric fields, timestamps, or calculated fields derived via eval, enhancing flexibility in query design. Range filtering integrates seamlessly with other SPL commands like stats, chart, timechart, and table, enabling both detailed and aggregated analyses.
Using this command ensures efficient, precise, and relevant data filtering. Unlike eval, table, or dedup, which focus on field calculation, formatting, or duplicate removal, this command directly restricts event results based on numeric or temporal thresholds, supporting focused operational, security, and business analysis.
The command searches for field values within a specified numeric or date range, enabling targeted filtering and analysis. It is essential in operational, security, and business analytics for threshold monitoring, anomaly detection, and precise reporting.
Therefore, the correct answer is to search with range operators.