Splunk SPLK-4001 Practice Test Questions, Exam Dumps

Practice Exams:

View All

SPLK-4001 Splunk Practice Test Questions and Exam Dumps

Question No 1:

What are the best practices for creating detectors? (Choose all that apply.)

A. View data at highest resolution.
B. Have a consistent value.
C. View detector in a chart.
D. Have a consistent type of measurement.

Answer: A, B, D

Explanation:

When creating detectors, particularly for monitoring or analysis purposes (such as in systems for network security, environmental monitoring, or performance tracking), the goal is to ensure that the detectors provide reliable, meaningful, and consistent data. Let’s break down the best practices for creating effective detectors:

A. View data at highest resolution: This is a best practice because using the highest resolution available provides the most detailed and accurate representation of the data. Higher resolution helps in detecting subtle patterns, fluctuations, or anomalies that might be missed with lower-resolution data. By having the most detailed view, you can make more precise analyses and detect smaller, potentially important signals in the data.
B. Have a consistent value: It is important for detectors to maintain a consistent value or threshold to ensure that comparisons or analyses are meaningful. When a detector is used over time or in different scenarios, its value should remain consistent to avoid confusion or misinterpretation. Consistency helps ensure that the detector's readings are reliable and comparable.
C. View detector in a chart: While visualizing data in charts can certainly help in understanding trends or patterns, it is not a best practice specifically for the creation of detectors themselves. A chart may be useful for interpreting the results of detectors, but it’s not directly related to the design or implementation of the detectors. Detectors themselves should focus on accurately capturing and processing data rather than just presenting it visually.
D. Have a consistent type of measurement: This is crucial because detectors should consistently measure the same type of metric or signal in order to ensure reliable and comparable results. Mixing different types of measurements can lead to confusion and inaccurate analyses. For example, mixing temperature with humidity in a single detector without proper calibration or context could result in misleading readings.

In conclusion, the best practices for creating detectors include A, B, and D because they focus on ensuring accuracy, consistency, and reliability in the measurement process, which are key to effective detection and monitoring. C, while helpful for analysis, does not specifically relate to the best practices for the creation of the detectors themselves.

Question No 2:

An SRE came across an existing detector that is a good starting point for a detector they want to create. They clone the detector, update the metric, and add multiple new signals.

As a result of the cloned detector, which of the following is true?

A. The new signals will be reflected in the original detector.
B. The new signals will be reflected in the original chart.
C. You can only monitor one of the new signals.
D. The new signals will not be added to the original detector.

Correct Answer: D

Explanation:

When the SRE clones an existing detector to create a new one, the process involves duplicating the original configuration but creating a new independent instance of the detector. This means that any changes made to the cloned detector will not affect the original detector. Let’s break down each option to understand why D is correct:

A. The new signals will be reflected in the original detector.
This statement is incorrect. Since the detector was cloned, it creates a new independent instance. Changes made to the cloned detector (such as adding new signals) will not impact the original detector. The original detector remains unchanged, and its configuration is unaffected by the modifications in the clone.
B. The new signals will be reflected in the original chart.
Again, this is incorrect. The cloned detector will generate its own set of signals, and these changes will apply to the cloned instance. The original chart linked to the original detector will not reflect any of the changes made in the cloned detector.
C. You can only monitor one of the new signals.
This is not accurate. After cloning, the SRE can add multiple new signals to the cloned detector. There is no restriction to monitoring only one signal. The ability to monitor multiple signals is standard functionality, and adding more signals is common practice in detector configurations.
D. The new signals will not be added to the original detector.
This statement is true. When a detector is cloned, it creates a separate copy of the original detector. Changes such as adding new signals are made to the cloned version and will not be applied to the original detector. Therefore, the new signals will only exist in the cloned detector, not the original one.

In summary, since the cloned detector is a separate entity from the original, changes like adding new signals will not affect the original detector. Therefore, the correct answer is D. The new signals will not be added to the original detector.

Question No 3:

Which of the following are supported rollup functions in Splunk Observability Cloud?

A. average, latest, lag, min, max, sum, rate
B. std_dev, mean, median, mode, min, max
C. sigma, epsilon, pi, omega, beta, tau
D. 1min, 5min, 10min, 15min, 30min

Correct Answer: A

Explanation:

In Splunk Observability Cloud, rollup functions are used to aggregate or summarize time-series data across specified time intervals. These functions help in calculating key metrics and trends that are essential for monitoring and troubleshooting in observability platforms. Let's break down the options and explain why A is the correct choice:

Option A: average, latest, lag, min, max, sum, rate – This is the correct set of supported rollup functions in Splunk Observability Cloud. These functions are used to perform calculations across time series data, such as:

average: Computes the mean of a set of values.

latest: Retrieves the most recent value in a time window.

lag: Measures the difference between current and past values, often used to identify trends or anomalies.

min/max: Finds the minimum or maximum values in the dataset.

sum: Adds up the values in a specified time period.

rate: Computes the rate of change per unit time (useful for measuring things like request rates or error rates).

These functions are foundational for aggregating and analyzing data in observability and monitoring contexts, making this the correct option.
Option B: std_dev, mean, median, mode, min, max – While these functions (standard deviation, mean, median, mode, min, and max) are commonly used in statistical analysis and data science, they are not all specifically designed for rollups in the context of time-series data aggregation in Splunk Observability Cloud. In observability contexts, mean (average), min, and max may be used, but terms like std_dev, median, and mode are not typically rollup functions in this platform.
Option C: sigma, epsilon, pi, omega, beta, tau – These are not rollup functions at all and do not pertain to Splunk Observability Cloud. These terms are more commonly found in mathematics or physics, not in observability toolsets.
Option D: 1min, 5min, 10min, 15min, 30min – These values represent time intervals (often used for defining the granularity of the data collection or the window for aggregating data), but they are not rollup functions themselves. Rollups use functions like average or sum across these time intervals, but these intervals themselves do not perform calculations or transformations on the data.

In conclusion, Option A provides the set of supported rollup functions used in Splunk Observability Cloud for summarizing and analyzing time-series data, making it the correct choice.

Question No 4:

A Software Engineer is troubleshooting an issue with memory utilization in their application. They released a new canary version to production and now want to determine if the average memory usage is lower for requests with the 'canary' version dimension. They've already opened the graph of memory utilization for their service.

How does the engineer see if the new release lowered average memory utilization?

A. On the chart for plot A, select Add Analytics, then select Mean:Transformation. In the window that appears, select the Group By field.
B. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-1'.
C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' Group By field.
D. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version'.

Correct Answer: C

Explanation:

To determine if the average memory usage is lower for requests with the 'canary' version dimension, the engineer needs to group the data by the 'version' field to compare the memory utilization between the canary version and the other versions.

Why C is correct:

C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' Group By field.
This option allows the engineer to group the memory utilization data by the 'version' dimension, specifically to compare the canary version against other versions. By selecting the Mean:Aggregation function, the engineer can calculate the average memory usage for each version. This helps directly address whether the canary version leads to lower memory usage on average.

Why the other options are not correct:

A. On the chart for plot A, select Add Analytics, then select Mean:Transformation. In the window that appears, select the Group By field.

While this option involves adding analytics, Mean:Transformation is typically used for transforming existing data into a different form, not for directly comparing averages across different groups (in this case, versions). It doesn't directly help to show the difference in average memory usage for the canary version.
B. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-1'.

This option suggests creating a custom formula for comparing two metrics (A and B). However, this is not ideal for comparing averages across different versions. The Group By functionality, as in option C, is a better fit for this use case because it groups the data by version, which is what the engineer needs to assess the impact of the canary release.
D. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version'.

While this option may seem like a reasonable choice, the Compare Means button is more commonly used for directly comparing the means of two or more distinct groups rather than using a Group By function to aggregate data across multiple dimensions (like version). It may not give the engineer the flexibility to see how the 'canary' version performs specifically.

To compare the average memory utilization for requests with the 'canary' version, the engineer should use option C, which allows them to group by the 'version' field and calculate the mean aggregation. This method is the best way to determine if the canary version lowered memory usage.

Question No 5:

What type of dashboard would be most suitable for creating charts and detectors for a server that is regularly restarting due to power supply issues?

A. Single-instance dashboard
B. Machine dashboard
C. Multiple-service dashboard
D. Server dashboard

Correct Answer: D

Explanation:

A Server dashboard would be the most suitable for monitoring and diagnosing power supply issues on a single server, particularly in a situation where the server is restarting regularly. This type of dashboard is designed to track various metrics related to the health and performance of individual servers, such as uptime, system performance, and hardware status. It is highly effective for isolating and addressing issues like the one described (server restarts due to power supply problems), as it allows the IT team to visualize data specific to that server, set up alarms (detectors), and analyze potential root causes like hardware failures, power fluctuations, or overheating.

Here’s why D is the correct answer and why the other options are less appropriate:

D. Server dashboard: A server dashboard is tailored for tracking and displaying information about a single server’s performance and health. This includes metrics like CPU usage, memory consumption, disk activity, network performance, and system logs, which would be crucial for diagnosing why the server is restarting due to power issues. A server dashboard can also provide real-time alerts and logs, allowing system administrators to set detectors to notify them immediately when the server experiences issues such as unexpected reboots or power fluctuations. By focusing on the performance and health of a single server, it provides clear insights that are critical for troubleshooting.
A. Single-instance dashboard: While a single-instance dashboard could be used for monitoring one instance of an application or service, it is generally not as comprehensive as a server dashboard when it comes to monitoring hardware or infrastructure issues like power supply problems. A single-instance dashboard might track application-specific metrics, but for hardware-related problems such as power supply failures, a server dashboard is more appropriate because it covers a broader range of server health metrics, including power and hardware status.
B. Machine dashboard: A machine dashboard might seem like a good option, but it generally refers to monitoring physical machines and their associated systems, which could include both servers and other hardware. However, the term "machine" can be broader and less specific compared to the server dashboard. A machine dashboard typically integrates with various systems and may not provide the same depth of server-specific detail that a dedicated server dashboard offers. Therefore, it may not be as focused on the exact problem of server restarts due to power supply issues.
C. Multiple-service dashboard: A multiple-service dashboard is useful when monitoring multiple services or applications across different servers or systems. While it’s beneficial for overseeing a range of services running on various machines, it’s not ideal for pinpointing hardware issues related to a single server, such as power supply problems. Since the issue here is specific to one server, a server dashboard is a more focused and effective choice.

In conclusion, D. Server dashboard is the most appropriate solution, as it provides the necessary level of detail for tracking server health and performance metrics, including potential issues related to power supply, and allows for the creation of detectors that can trigger alerts when problems like restarts occur. This specialized dashboard is the best option for addressing server-specific issues in a data center environment.

Question No 6:

To refine a search for a metric, a customer types host:test-*. What does this filter return?

A. Only metrics with a dimension of host and a value beginning with test-.
B. Error
C. Every metric except those with a dimension of host and a value equal to test-.
D. Only metrics with a value of test- beginning with host.

Correct Answer: A

Explanation:

In this scenario, the customer is using a filter syntax to refine their search for a metric. The filter *host:test- **is designed to match specific metric values based on the host dimension.

A. Only metrics with a dimension of host and a value beginning with test-. This is the correct answer. The filter syntax host:test-* uses a wildcard (*) to match any value that begins with test- for the dimension host. The wildcard allows for flexibility, meaning the filter will return any metric where the host dimension starts with test-, followed by any characters.
B. Error: This is incorrect. The syntax *host:test- **is a valid way to search for metrics and would not return an error. The wildcard syntax is widely supported for filtering metrics in many monitoring systems.
C. Every metric except those with a dimension of host and a value equal to test-. This is incorrect. The filter does not exclude metrics where the value of the host dimension is exactly test-; rather, it matches metrics where the host value begins with test-, including test- itself. It doesn’t exclude this exact value.
D. Only metrics with a value of test- beginning with host. This is also incorrect. The search is specifically filtering based on the host dimension starting with test-, not the other way around. The filter is focused on the value of the host dimension, not the metric name.

In conclusion, the correct interpretation of host:test-* is that it returns only metrics where the host dimension’s value begins with "test-", making A the right answer.

Question No 7:

A customer operates a caching web proxy. They want to calculate the cache hit rate for their service. What is the best way to achieve this?

A. Percentages and ratios
B. Timeshift and Bottom N
C. Timeshift and Top N
D. Chart Options and metadata

Correct Answer: A

Explanation:

The cache hit rate is a key performance metric for a caching proxy, as it indicates how often the proxy serves content directly from its cache rather than fetching it from the origin server. To calculate the cache hit rate, the formula typically used is:

Cache Hit Rate (%) = (Cache Hits / (Cache Hits + Cache Misses)) * 100

Here, Cache Hits refer to the number of times the requested data was served directly from the cache, and Cache Misses refer to the number of times the data had to be fetched from the original server.

The best way to calculate and express this ratio is through percentages and ratios because this approach directly measures the proportion of cache hits versus the total number of requests (hits and misses combined). This method allows the customer to accurately assess the performance of the caching service in terms of how often data is retrieved from the cache versus how often it is retrieved from the origin server.

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

B. Timeshift and Bottom N: This approach generally refers to viewing data over time (timeshift) and focusing on the least frequent or least impactful items (Bottom N). While timeshift analysis can be useful in some cases (such as understanding trends over time), it is not directly relevant for calculating a simple metric like the cache hit rate. Bottom N would typically focus on the least frequently accessed data, not the calculation of cache hits or misses.
C. Timeshift and Top N: Similar to option B, timeshift analysis is focused on looking at changes over time, while Top N refers to the most frequent or most impactful items. While this could help identify the most accessed content, it doesn’t directly contribute to calculating the cache hit rate, which is a ratio of cache hits versus misses, not a ranking of the most popular data items.
D. Chart Options and metadata: This option involves customizing the way data is visualized or adding additional descriptive data. While chart options and metadata can be helpful for displaying the cache hit rate in a more readable way, they are not essential for calculating the actual metric. The calculation itself relies on raw data (hits and misses), not the way it is displayed.

Thus, the best method to calculate the cache hit rate is by using percentages and ratios because these directly give you the needed metric by comparing cache hits against the total number of requests, making it the most efficient and accurate way to measure cache performance.

Question No 8:

Which of the following are correct ports for the specified components in the OpenTelemetry Collector?

A. gRPC (4000), SignalFx (9943), Fluentd (6060)
B. gRPC (6831), SignalFx (4317), Fluentd (9080)
C. gRPC (4459), SignalFx (9166), Fluentd (8956)
D. gRPC (4317), SignalFx (9080), Fluentd (8006)

Answer: B

Explanation:

The OpenTelemetry Collector is a crucial component in the OpenTelemetry ecosystem, responsible for collecting, processing, and exporting telemetry data such as traces, metrics, and logs. The Collector interacts with several components, and each component may use specific ports for communication. Let's break down the options:

Correct Ports for OpenTelemetry Collector Components

gRPC (6831):

gRPC is commonly used for communication between components in the OpenTelemetry ecosystem. The port 6831 is typically used for gRPC communication in OpenTelemetry's default configuration. It is commonly used for receiving trace data in certain setups.

SignalFx (4317):

SignalFx is one of the supported receivers in the OpenTelemetry Collector. Port 4317 is the default port for the SignalFx receiver, which is used to receive data for processing.

Fluentd (9080):

Fluentd is an open-source data collector that is often used alongside OpenTelemetry to collect, process, and ship logs. Port 9080 is a common default for Fluentd's HTTP input or similar configurations.

Why Other Options Are Incorrect

A. gRPC (4000), SignalFx (9943), Fluentd (6060):

Port 4000 is not the typical default for gRPC communication in OpenTelemetry.
Port 9943 is not the standard default port for SignalFx integration.
Port 6060 is not the standard Fluentd input port.

C. gRPC (4459), SignalFx (9166), Fluentd (8956):

Port 4459 is not a standard gRPC port in the OpenTelemetry Collector.
Port 9166 is not used by SignalFx integration.
Port 8956 is not the typical Fluentd input port.

D. gRPC (4317), SignalFx (9080), Fluentd (8006):

Port 4317 is indeed correct for gRPC, but the other ports are not correct for SignalFx and Fluentd. SignalFx uses 4317, but 9080 is not the correct port.
Similarly, Fluentd uses 9080 (not 8006).

Thus, the correct combination of ports for the specified components in the OpenTelemetry Collector is B. gRPC (6831), SignalFx (4317), Fluentd (9080).

Question No 9:

When writing a detector with a large number of MTS, such as memory.free in a deployment with 30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot.

Which of the choices below would most likely reduce the number of MTS below the plot cap?

A. Select the Shared option when creating the plot.
B. Add a filter to narrow the scope of the measurement.
C. Add a restricted scope adjustment to the plot.
D. When creating the plot, add a discriminator.

Correct Answer: B

Explanation:

When working with large datasets, such as 30,000 hosts in the case of memory.free, it’s common to hit limits like the maximum number of MTS (metric time series) that can be displayed on a single plot. Exceeding the cap of MTS can make visualizations overly complex and difficult to analyze. To resolve this issue, you need to reduce the number of MTS that the plot attempts to show. Let’s examine each option:

A. Select the Shared option when creating the plot. The Shared option typically allows for grouping or sharing certain attributes across multiple measurements or series. This may help in some cases to reduce redundancy or allow multiple measurements to be displayed in a more consolidated way. However, it doesn't directly reduce the number of MTS on a plot—it focuses more on shared visual formatting and grouping rather than limiting the number of individual time series. Thus, A is less likely to solve the issue effectively.
B. Add a filter to narrow the scope of the measurement. Adding a filter is the most direct approach to narrowing down the number of MTS being plotted. By applying a filter, you can limit the data to a specific subset, such as focusing only on a particular region, host group, or specific conditions. This effectively reduces the total number of MTS being handled, thereby reducing the chance of exceeding the plot cap. Filters can be very effective for handling large datasets by focusing on the most relevant data. This makes B the most suitable choice.
C. Add a restricted scope adjustment to the plot. A restricted scope adjustment could potentially limit the data being visualized by applying more specific rules or constraints. While this might help in some cases, it is not a standard method for directly reducing the number of MTS in a plot. It may not always be available or as effective as simply applying a filter to narrow the scope of the data being visualized.
D. When creating the plot, add a discriminator. A discriminator is typically used to differentiate or segment data in a plot, such as by grouping different types of measurements or hosts. While adding a discriminator can help categorize and organize data, it doesn’t necessarily reduce the number of MTS on a plot. In fact, it could even increase the number of MTS by adding more granularity. Thus, D is unlikely to address the issue of exceeding the plot cap.

In conclusion, B (adding a filter to narrow the scope of the measurement) is the most effective approach to reducing the number of MTS below the plot cap, as it directly targets the data being plotted and limits it to a more manageable subset.

Question No 10:

How can the number of alerts be reduced for a custom metrics alert rule on server latency set at 260 ms?

A. Adjust the threshold.
B. Adjust the Trigger sensitivity. Duration set to 1 minute.
C. Adjust the notification sensitivity. Duration set to 1 minute.
D. Choose another signal.

Correct Answer: B

Explanation:

In the scenario described, the SRE is receiving alerts whenever server latency exceeds 260 milliseconds. However, this alert can become noisy or overly frequent, especially if latency fluctuates above the threshold for short periods. To reduce the number of alerts, it's important to adjust the sensitivity of the alert system, ensuring that only meaningful or persistent issues trigger notifications.

Key Concepts:

B. Adjust the Trigger sensitivity. Duration set to 1 minute:
This is the most appropriate option to reduce the number of alerts. Trigger sensitivity controls how the alert is triggered based on the metric. By setting the duration to 1 minute, the alert will only trigger if the latency exceeds 260 milliseconds for at least 1 minute, rather than triggering immediately when it briefly crosses the threshold. This helps to avoid false positives caused by short spikes in latency that are not significant. The 1-minute duration ensures that only sustained periods of high latency are flagged, reducing the frequency of alerts and making them more meaningful.

Breakdown of the Other Options:

A. Adjust the threshold:
Adjusting the threshold could change when the alert is triggered, but it doesn’t necessarily help to reduce the frequency of alerts if the latency is still regularly exceeding the set threshold. In fact, lowering the threshold could increase the number of alerts, making it counterproductive. So, A does not specifically help reduce the frequency of alerts.
C. Adjust the notification sensitivity. Duration set to 1 minute:
Notification sensitivity is related to how alerts are sent or how often they are sent once a condition is met. While adjusting the duration to 1 minute may help filter out brief fluctuations, adjusting notification sensitivity might not be as effective as adjusting trigger sensitivity in terms of reducing the number of alerts when a sustained problem is occurring. Notification sensitivity controls the alert delivery, not the actual detection of the problem itself.
D. Choose another signal:
Choosing another signal could be relevant if the current signal (latency) is not the best one for detecting the issue. However, this does not directly reduce the number of alerts; rather, it changes the metric being tracked. This is more of a broad change in strategy and not a specific solution to reducing the frequency of alerts based on the given metric.

In summary, B is the best option because adjusting the Trigger sensitivity and increasing the duration before an alert is triggered ensures that the system responds only to sustained issues, rather than transient spikes in latency, effectively reducing unnecessary alerts.