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Amplitude Certification study materials
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Amplitude Certification practice questions

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Question 1Product Analytics

In Amplitude, what does a single event record represent when a user triggers a tracked action?

  1. One aggregated daily summary of that action
  2. One dashboard chart refreshed by that action
  3. One instance of that action with a timestamp and associated properties Correct answer.
  4. One user profile updated by that action

Explanation

An event record captures a single occurrence of a tracked action, including when it happened and any properties sent with it. Aggregation into counts and charts happens later during analysis.

Question 2Product Analytics

What is the primary purpose of an event property in Amplitude?

  1. To group users into cohorts automatically
  2. To define the retention window for a chart
  3. To set the project's data retention policy
  4. To describe attributes of a specific event occurrence Correct answer.

Explanation

Event properties add descriptive detail to each occurrence of an event, such as a product ID or price. They can then be used to filter, group, or break down analyses.

Question 3Product Analytics

A team wants to describe stable traits of a user such as their signup plan. Which Amplitude concept is designed for this?

  1. Event properties
  2. User properties Correct answer.
  3. Cohort syncs
  4. Session events

Explanation

User properties store attributes about the person rather than about a single action, so they persist across events. Plan type is a classic user property.

Question 4Product Analytics

Which Amplitude chart type is designed to show how many users return to perform an event after first doing a starting event?

  1. User Composition
  2. Funnel Analysis
  3. Retention Correct answer.
  4. Event Segmentation

Explanation

Retention charts measure the share of users who come back and repeat an action over subsequent time periods after an initial event.

Question 5Product Analytics

What does a Funnel Analysis chart primarily measure?

  1. The distribution of session lengths across users
  2. The revenue attributed to each acquisition channel
  3. The number of distinct events fired per user per day
  4. The proportion of users who complete an ordered series of steps Correct answer.

Explanation

Funnels track users through a defined sequence of steps and report conversion between them. The key output is how many users progress from the first step to later steps.

Question 6Product Analytics

In Event Segmentation, what is the difference between the 'event totals' and 'unique users' measures?

  1. Event totals counts distinct users, while unique users counts occurrences
  2. Both count occurrences but differ only in the date range used
  3. Both count distinct users but differ only in property filters applied
  4. Event totals counts occurrences, while unique users counts distinct users who triggered the event Correct answer.

Explanation

Event totals sums every occurrence of the event, so one user can contribute many. Unique users counts each user once regardless of how many times they fired the event.

Question 7Product Analytics

What does an Amplitude cohort represent?

  1. A saved set of events that share a naming convention
  2. A saved chart configuration with fixed date ranges
  3. A saved set of users who share a defined behavioral or property-based condition Correct answer.
  4. A saved list of dashboards visible to a team

Explanation

A cohort is a reusable group of users defined by criteria such as performing an event or having a property value. Cohorts can then be applied across multiple charts.

Question 8Product Analytics

Which statement best describes a session in Amplitude's default session tracking?

  1. A session ends only when the user logs out of the product
  2. A session is a period of activity bounded by a configurable inactivity timeout Correct answer.
  3. A session always lasts exactly 30 minutes regardless of activity
  4. A session is defined as one calendar day of user activity

Explanation

By default a session groups activity together and closes after a period of inactivity, which is configurable per project. Logout is not required to end a session.

Question 9Product Analytics

A product team wants to know which events most often immediately precede a purchase event. Which Amplitude capability is most directly suited to this?

  1. Revenue LTV
  2. Cohort Comparison
  3. Pathfinder Correct answer.
  4. Event Segmentation

Explanation

Pathfinder shows the sequences of events users perform, including what happens before or after a chosen event. That makes it suited to discovering preceding steps.

Question 10Product Analytics

A marketer wants to compare week-over-week retention of users acquired from two different campaigns. What is the most appropriate approach?

  1. Use a User Composition chart with no breakdown
  2. Build a funnel with the campaign event as step one
  3. Use Event Segmentation with event totals and no filters
  4. Create two cohorts, one per campaign, and compare them on a retention chart Correct answer.

Explanation

Cohorts let you isolate each campaign's users, and the retention chart's cohort comparison shows their return curves side by side. This directly answers the week-over-week question.

Question 11Product Analytics

In a funnel, what does the 'conversion over time' setting control?

  1. The refresh interval of the underlying dashboard
  2. The time window within which a user must complete all steps to count as converted Correct answer.
  3. The retention period of raw event data in the project
  4. The timezone used to display chart timestamps

Explanation

The conversion window defines how long users have to move through the funnel steps before they are no longer counted as converting. Widening it generally increases measured conversion.

Question 12Product Analytics

An analyst groups an Event Segmentation chart by a user property that has hundreds of distinct values. What is the most likely practical consequence?

  1. The chart will refuse to render until values are deleted
  2. The chart will produce too many series to read, reducing interpretability Correct answer.
  3. The chart will convert the property into an event property
  4. The chart will automatically merge all values into a single series

Explanation

Grouping by a high-cardinality property creates a large number of series, which makes the chart hard to interpret. Analysts typically limit to top values or use a lower-cardinality grouping.

Question 13Product Analytics

What does an Amplitude formula in a chart allow an analyst to do?

  1. Rename events across the whole project
  2. Schedule a chart to be emailed weekly
  3. Combine metrics mathematically, such as a ratio of two event counts Correct answer.
  4. Change the data type of an event property

Explanation

Formulas let you derive new metrics from existing ones, for example dividing one event's count by another to get a rate. They operate on chart metrics rather than on the underlying schema.

Question 14Product Analytics

A team notices their funnel shows a large drop between step two and step three. Which follow-up analysis best tests whether the drop is specific to one platform?

  1. Remove step one from the funnel
  2. Switch the funnel to event totals
  3. Add a breakdown by platform to the funnel Correct answer.
  4. Increase the funnel's conversion window to one year

Explanation

Breaking the funnel down by platform isolates conversion per platform so the drop can be compared across them. If one platform drives the drop, the breakdown will reveal it.

Question 15Product Analytics

What is the main difference between a retention chart and a funnel chart?

  1. Retention requires cohorts, while a funnel cannot use cohorts
  2. Retention measures revenue, while a funnel measures user counts
  3. Retention measures return over time after a start event, while a funnel measures ordered step completion Correct answer.
  4. Retention only works with user properties, while a funnel only works with event properties

Explanation

Retention focuses on whether users come back in later periods after an initial action. Funnels focus on whether users progress through a defined ordered sequence.

Question 16Product Analytics

An analyst wants to know the average number of distinct days per user on which a specific event occurred during a month. Which approach is most appropriate?

  1. Use a retention chart with the event as the start event
  2. Use a frequency or distribution analysis of active days for that event Correct answer.
  3. Use a funnel with the event as both the first and second step
  4. Use Event Segmentation with the 'unique users' measure and no grouping

Explanation

A distribution or frequency analysis summarizes how many active days or occurrences each user has, giving the average distinct days. This directly answers the per-user day-count question.

Question 17Product Analytics

Which scenario is best analyzed with a User Composition chart?

  1. Measuring week-four return rates for new users
  2. Tracking how conversion through three steps changed over time
  3. Understanding what share of the active user base belongs to each plan tier Correct answer.
  4. Identifying which event most often precedes checkout

Explanation

User Composition shows how a user base is distributed across a property such as plan tier at a point in time. It answers 'what share of users are in each group.'

Question 18Product Analytics

A product manager wants to see how many users first performed a key action each week and then returned to perform it again in later weeks. Which chart and setup fits best?

  1. Pathfinder with the key action as the ending point
  2. Retention with the key action as both the start and return event, weekly interval Correct answer.
  3. Funnel with the key action as the only step
  4. Event Segmentation with weekly interval and event totals

Explanation

A retention chart with the same event as start and return, using a weekly interval, shows new-user cohorts and their return in subsequent weeks. This matches the question precisely.

Question 19Product Analytics

What does the 'holding constant' option in an Amplitude chart accomplish?

  1. It restricts the comparison so that only users meeting a condition at the start are counted throughout Correct answer.
  2. It pins a chart to a dashboard for all viewers
  3. It freezes a chart's date range so it cannot be edited
  4. It locks the event definitions used by a chart

Explanation

Holding a property constant evaluates users against that property value at the start of the analysis and keeps them assigned to that value. Without it, users who change values could be counted inconsistently across periods.

Question 20Product Analytics

An analyst builds a funnel and sees step-two conversion above 100 percent relative to step one. Which explanation is most plausible?

  1. The data pipeline has necessarily lost all step-one events
  2. The funnel is using a retention measure by mistake
  3. The chart is counting events rather than users, allowing repeated step-two events to exceed the user count at step one Correct answer.
  4. The conversion window was set too short

Explanation

If the funnel measure counts event occurrences rather than unique users, users who repeat later steps many times can push later-step counts above earlier ones. Switching to unique users typically resolves the anomaly.

Question 21Product Analytics

A team wants to attribute revenue to the first marketing touch before signup. Which Amplitude concept most directly supports attributing revenue to events?

  1. User Composition by acquisition channel
  2. Pathfinder with revenue as an endpoint
  3. Event Segmentation with event totals
  4. Revenue tracking on events with revenue properties Correct answer.

Explanation

Amplitude supports revenue analysis by attaching revenue amounts to events, which can then be attributed and analyzed. This is the mechanism for tying revenue to user actions.

Question 22Product Analytics

An analyst wants to compare retention for users who did event A before event B versus users who did B before A. Which approach is most appropriate?

  1. A single funnel with A and B as steps
  2. A User Composition chart broken down by A and B
  3. Two cohorts defined by the order of A and B, compared on a retention chart Correct answer.
  4. Event Segmentation with A and B as separate series

Explanation

Order-sensitive comparison requires defining cohorts by sequence, then comparing their retention curves. This isolates the effect of doing A before B versus the reverse.

Question 23Product Analytics

A chart shows a sudden spike in a tracked event on a single day. Which diagnostic step best tests whether the spike is real user behavior rather than an instrumentation issue?

  1. Break the event down by platform, app version, and a user property to see whether the spike concentrates in one segment Correct answer.
  2. Add a second event to the chart as a comparison series
  3. Switch the chart measure from unique users to event totals
  4. Increase the chart's date range to a full year

Explanation

Concentration of a spike in a single platform, version, or property value often signals a release or instrumentation change rather than genuine behavior. Breaking down the event localizes the cause.

Question 24Product Analytics

Why might a retention chart show lower week-one retention when the start event uses a broad definition rather than a narrow one?

  1. Broader start events disable the conversion window
  2. Broader start events always exclude new users from the cohort
  3. Broader start events include more marginal users who are less likely to return, diluting the return rate Correct answer.
  4. Broader start events change the chart's timezone setting

Explanation

A broader start event captures a wider population, including users with weaker intent, so the share returning is typically lower. Narrower definitions concentrate more committed users and often show higher retention.

Question 25Product Analytics

What is the purpose of the 'any event' option in some Amplitude analyses?

  1. It treats any tracked event as satisfying a condition, such as activity in a period Correct answer.
  2. It excludes events from the analysis entirely
  3. It restricts results to events with no properties
  4. It merges all events into a single event definition

Explanation

'Any event' acts as a wildcard that any tracked event can satisfy, which is useful for measuring general activity. It does not alter event definitions permanently.

Question 26Product Analytics

In Amplitude, what is an event?

  1. A dashboard containing multiple charts
  2. A single tracked user action recorded with a timestamp and associated properties Correct answer.
  3. A saved segment of users who share a trait
  4. A scheduled email report delivered to stakeholders

Explanation

An event is the fundamental unit of data in Amplitude: one tracked action with a name, time, and optional properties. Cohorts, dashboards, and reports are built on top of events rather than being events themselves.

Question 27Product Analytics

Which type of property is attached to a user and persists across all of that user's events?

  1. Cohort property
  2. Chart property
  3. User property Correct answer.
  4. Event property

Explanation

User properties describe the person and are available across every event they send, which is why they are useful for grouping and filtering by traits such as plan type. Event properties describe only the specific action that carried them.

Question 28Product Analytics

What does the Amplitude SDK's identify call primarily do?

  1. Resets the device ID for the current user
  2. Updates user properties without logging an event Correct answer.
  3. Records a new event in the user's timeline
  4. Sends a batch of events to the server immediately

Explanation

The identify call sets or updates user properties on the user profile and does not itself create an event in the event stream. Events are logged with the track or logEvent call instead.

Question 29Product Analytics

Which chart type is designed to show how a group of users is distributed across the values of a single property at a point in time?

  1. Event Segmentation
  2. Funnel
  3. User Composition Correct answer.
  4. Retention

Explanation

User Composition breaks a user population into segments by one property and shows the share in each segment. The other chart types focus on event counts over time, repeat usage, or ordered step conversion.

Question 30Product Analytics

In Event Segmentation, what does selecting 'unique users' as the measure return?

  1. The total number of times the event fired
  2. The number of sessions containing the event
  3. The average events per user
  4. The number of distinct users who triggered the event at least once in the interval Correct answer.

Explanation

Unique users counts each user once per interval regardless of how many times they fired the event. Event totals instead sum every occurrence.

Question 31Product Analytics

What is the purpose of a dashboard in Amplitude?

  1. To arrange multiple saved charts into a shared view for monitoring Correct answer.
  2. To define which events the SDK is allowed to send
  3. To configure data retention rules for the organization
  4. To permanently delete users from the project

Explanation

Dashboards collect related charts so a team can monitor metrics together and share them. They do not govern ingestion, deletion, or retention policy.

Question 32Product Analytics

Which setting in a funnel chart determines the maximum time a user may take to complete all steps?

  1. Segment
  2. Breakdown
  3. Conversion window Correct answer.
  4. Holding constant

Explanation

The conversion window caps the elapsed time allowed between the first step and the final step for a conversion to count. Segments and breakdowns split the population, and holding constant restricts users to those who performed a specified event.

Question 33Product Analytics

What does the Amplitude 'active event' setting control in a chart?

  1. Which events count as evidence that a user was active in a period Correct answer.
  2. How many events are stored per user
  3. The time zone used to display the chart
  4. Which users are deleted for inactivity

Explanation

Active events define the set of events that mark a user as active, which affects counts such as active users and retention. Storage limits and time zone display are configured elsewhere.

Question 34Product Analytics

An analyst wants to see whether users who viewed a pricing page converted at a different rate than those who did not. Which configuration most directly answers this?

  1. Break down the funnel by the pricing page event using a 'did event' segment Correct answer.
  2. Add a fourth step to the funnel
  3. Switch the funnel measure from unique users to event totals
  4. Change the funnel conversion window to 30 days

Explanation

Comparing two populations defined by whether they performed an event requires splitting the users into segments, which is what a 'did event' segment accomplishes. Changing the window, measure, or step count alters the funnel itself rather than comparing groups.

Question 35Product Analytics

A team wants to know the median number of days between signup and first purchase across users. Which Amplitude approach fits best?

  1. A funnel with signup as step one and purchase as step two, reading the time-to-convert distribution Correct answer.
  2. An Event Segmentation chart of purchase event totals
  3. A retention chart with purchase as the return event
  4. A User Composition chart on the signup source property

Explanation

A two-step funnel measures how long users take to move from the first step to the second, and the time-to-convert distribution summarizes that duration across users. The other charts do not measure elapsed time between two ordered events.

Question 36Product Analytics

An analyst wants to compare how often users perform a core action per week, normalized by the number of active users that week. Which measure should they use?

  1. Unique users
  2. Cumulative event totals
  3. Event totals
  4. Events per active user Correct answer.

Explanation

Events per active user normalizes event volume by the active population, making week-over-week intensity comparable even when the user base changes size. Raw totals and unique users do not normalize in this way.

Question 37Product Analytics

A product manager wants to know what share of users who signed up in January were still performing a key action in week four. Which chart answers this most directly?

  1. An Event Segmentation chart of signup totals
  2. A funnel chart from signup to the key action
  3. A retention chart with signup as the start event and the key action as the return event Correct answer.
  4. A User Composition chart of acquisition channel

Explanation

Retention charts measure whether users who performed a start event return to perform a return event in later periods. A funnel would measure one-time conversion within a window, not persistence into week four.

Question 38Product Analytics

An analyst wants to see whether users who complete onboarding within one day retain better than those who take longer. Which configuration supports this comparison?

  1. Increase the retention chart's date range
  2. Switch the retention chart to event totals
  3. Add more return events to the same chart
  4. Segment the retention chart by a property or computed cohort that captures onboarding speed Correct answer.

Explanation

To compare two populations on retention, the chart must split users into groups, which is done with a segment or cohort. Extending dates, changing the measure, or adding return events does not create the comparison.

Question 39Product Analytics

What does a breakdown in an Amplitude chart do?

  1. Changes the date range shown on the x-axis
  2. Replaces the chart's measure with a formula
  3. Splits a single metric into separate series by the values of a chosen property Correct answer.
  4. Restricts the chart to users who performed a specific event

Explanation

A breakdown partitions the results by property values so each value appears as its own series or row. Restricting users by an event is a segment or holding-constant operation, not a breakdown.

Question 40Product Analytics

An analyst wants to compare the same funnel across iOS and Android without building two separate charts. What should they do?

  1. Add a step for the platform event
  2. Break down the funnel by the platform property Correct answer.
  3. Change the funnel's conversion window
  4. Switch the funnel to unique users

Explanation

Breaking down by the platform property produces one series per platform within a single chart. The other changes alter timing or step logic rather than splitting by platform.

Question 41Product Analytics

What does the 'group by' control in a User Composition chart determine?

  1. The statistical confidence level of the chart
  2. The date range of the composition snapshot
  3. The property whose values define the segments shown Correct answer.
  4. The event used as the start of the analysis

Explanation

Group by selects the property that partitions the user population into the displayed segments. Date range, start event, and confidence are separate controls.

Question 42Product Analytics

A team wants to count users who performed event A and then event B in that order within a single day, counting each user once. Which chart type is most appropriate?

  1. A funnel with A and B as ordered steps and a one-day conversion window Correct answer.
  2. An Event Segmentation chart of A and B totals
  3. A retention chart with A as start and B as return
  4. A User Composition chart grouped by day

Explanation

A funnel enforces order and a time limit between steps, which matches the requirement. Event Segmentation does not enforce order, and retention measures return in later periods rather than ordered completion.

Question 43Product Analytics

An analyst applies a formula that divides purchases by sessions on an Event Segmentation chart. What does the resulting series represent?

  1. Purchases per session for each interval Correct answer.
  2. Average session length for each interval
  3. Total sessions for each interval
  4. Unique purchasers for each interval

Explanation

A formula performs arithmetic on the chart's measures, so dividing purchases by sessions yields a per-session purchase rate for each time interval. It does not return either raw measure on its own.

Question 44Product Analytics

Why might an analyst apply a rolling average to a noisy daily Event Segmentation series?

  1. To increase the total event count shown
  2. To convert the chart into a funnel
  3. To remove users who churned
  4. To smooth short-term fluctuations so the underlying trend is easier to see Correct answer.

Explanation

A rolling average averages each point with neighboring points, which dampens day-to-day noise and reveals trend direction. It does not change the underlying totals or the user population.

Question 45Product Analytics

An analyst wants to understand which acquisition channel produces users with the highest long-term retention. Which combination is most appropriate?

  1. A retention chart segmented by the acquisition channel property Correct answer.
  2. A funnel chart of signups by channel
  3. A User Composition chart of channel without retention
  4. An Event Segmentation chart of signup totals by channel

Explanation

Retention is the outcome of interest, and segmenting by acquisition channel splits users into comparable groups. Funnels and event totals measure conversion or volume rather than long-term return.

Question 46Product Analytics

A funnel shows step-two conversion lower than expected, but event totals for step two are high. Which explanation best reconciles these observations?

  1. The chart is using unique users as the measure
  2. Many step-two events come from users who never completed step one, so they do not count as funnel conversions Correct answer.
  3. The conversion window is set too long
  4. Step two is not being tracked at all

Explanation

Funnel conversion counts only users who complete steps in order, while raw event totals count every occurrence regardless of prior steps. A large volume of out-of-sequence events can therefore coexist with low funnel conversion.

Question 47Product Analytics

An analyst suspects that a retention chart's week-one rate is inflated because the start event fires repeatedly for the same users. What is the most direct remedy?

  1. Switch the return event to 'any event'
  2. Use a start event that fires once per user lifecycle, or restrict to first occurrence Correct answer.
  3. Break the chart down by device type
  4. Shorten the retention window to one week

Explanation

Retention is anchored on when the start event occurs, so an event that fires repeatedly can make users appear to re-enter the cohort. Anchoring on a once-per-lifecycle event or first occurrence corrects the cohort definition.

Question 48Product Analytics

A team wants to compare conversion rates for two funnel variants but worries that a confound, such as traffic source, differs between them. Which approach best isolates the comparison?

  1. Switch both variants to event totals
  2. Extend the conversion window for both variants
  3. Increase the number of funnel steps
  4. Restrict both variants to the same traffic source segment before comparing Correct answer.

Explanation

Holding the confound constant by restricting to one segment removes its influence so the variant comparison reflects the change under study. Changing the window, step count, or measure alters the metric rather than controlling the confound.

Question 49Product Analytics

An analyst sees a metric drop sharply after a release and wants to determine whether the drop reflects a genuine behavior change or a data pipeline issue. Which step is most diagnostic?

  1. Rebuild the chart as a retention analysis
  2. Compare event volume by SDK version and ingestion date to check for gaps or version-specific loss Correct answer.
  3. Immediately change the chart's measure to unique users
  4. Delete the release date from the chart's annotations

Explanation

Checking volume by SDK version and ingestion date reveals whether data stopped arriving or changed shape around the release, which distinguishes pipeline problems from real behavior shifts. Changing measures or chart types obscures the diagnosis.

Question 50Product Analytics

An analyst wants to know whether users who perform event A before event B convert better than users who perform B before A. Which configuration most directly answers this?

  1. A User Composition chart grouped by A
  2. A retention chart with A as the start event
  3. A single Event Segmentation chart of A and B totals
  4. Two funnels with the step orders reversed, compared on the same conversion metric Correct answer.

Explanation

Order matters here, and a funnel enforces order, so reversing the step sequence in two funnels isolates the effect of sequence on conversion. Event Segmentation and retention do not enforce the ordering of A and B.