Free AD0-E406 sample questions

Correct answer: Time on Site

Time on Site is a direct engagement metric available in Adobe Target that accurately reflects the business KPI of 'User Engagement'. While RPV is a valuable metric, it measures financial performance rather than active engagement. Conversion Rate measures specific actions rather than continuous interaction. Pageviews measure volume but not necessarily the quality or duration of engagement.

Correct answer: Apply the Bonferroni correction by checking the 'Multiple Offers' box or manually dividing the significance level by the number of comparisons.

When testing multiple variations against a control, the probability of encountering a false positive (Type I error) increases. The Bonferroni correction accounts for this by adjusting the required confidence level (and consequently increasing the required sample size). Failing to apply this correction when running multi-offer tests leads to unreliable statistical significance. flowchart LR A[Multiple Offers] --> B{Increased False Positive Risk} B --> C[Apply Bonferroni Correction] C --> D[Higher Confidence Threshold] D --> E[Larger Sample Size Required]

Correct answer: Idea 3 (Adding testimonial logos) using a standard A/B Test activity.

Using the PIE framework, Idea 3 offers the best balance. It has moderate-to-high Potential and Importance, and high Ease (only 2 hours of dev time). This allows for a quick win within the 14-day constraint. Idea 1 has low Ease (3 weeks dev time) and violates the time constraint. Idea 2 has high Ease but low Potential, making it less likely to secure the budget. An A/B test is the optimal activity type for simply adding a new element to validate its impact against a control.

Correct answers: A clear definition of the specific change being introduced (the independent variable).; A measurable expected outcome or metric that will indicate success (the dependent variable).

A valid hypothesis must clearly identify the independent variable (the specific change or treatment being introduced) and the dependent variable (the measurable outcome or metric). Defining the independent variable ensures the test is actionable, and defining the dependent variable ensures it is measurable. Guarantees of statistical significance or reliance on vanity metrics weaken a hypothesis.

A valid hypothesis must clearly identify the independent variable (the specific change or treatment being introduced) and the dependent variable (the measurable outcome or metric). Defining the independent variable ensures the test is actionable, and defining the dependent variable ensures it is measurable. Guarantees of statistical significance or reliance on vanity metrics weaken a hypothesis.