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 200-101 Dumps with Practice Exam Questions Answers

Questions: 60 questions

Last Update: Dec 1, 2022

Facebook Certification Exam 200-101 has been designed to measure your skills in handling the technical tasks mentioned in the certification syllabus

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200-101 Questions and Answers

Question # 1

A marketing analyst wants to understand the relationship between campaign frequency and additional return on ad spend (ROAS) across 150 CPG Facebook campaigns. The analyst has the following information on these campaigns: reach, frequency, duration, budget, product category, buying strategies, and outcomes like additional sales and ROAS. The analyst suspects that campaign frequency is related to other campaign characteristics and is planning to run the following statistical model:

ROAS Lift = bO + b1.reach + b2.frequency + b3.duration + b4.budget + b5.product category + b6.buying strategy

What two additional statistical analysis are required to test the analyst's hypothesis? (Choose 2)


Correlation matrix of campaign frequency and other predictors of ROAS Lift


Logistic regression including all relevant campaign characteristics


Simple linear regression of frequency and ROAS Lift


Multiple linear regression including non-linear and interaction terms

Question # 2

An analyst analyzes 1,000 Facebook video campaigns and calculates there is a correlation of r = 0.6 between view duration of a campaign and additional ad recall lift. What should the analyst tell the company about the relationship between view duration and ad recall lift?


60% of campaigns with longer view durations had also higher ad recall lift


36% of the variation in ad recall can be explained by campaign's view duration


There is 60% probability that longer view durations in a campaign leads to higher ad recall


40% of the variation in ad recall can be explained by campaign's view duration

Question # 3

An analyst calculates data averages (norms) of more than 2,500 brand lift tests in the industry, then compares it to recent campaign results:

• Campaign delivered Ad recall lift +8.3pt (the norm Ad Recall is 9.Opt with a standard deviation of 0.6pt)

• Campaign delivered Message Association +7.1pt (the norm for Message Association is 7.8pt with a standard deviation of 0.8pt)

• Campaign delivered Favorability +6.3pt (the norm for Favorability is 4.Opt with a standard deviation of 1.1pt)

• Campaign delivered Purchase Intent +5.9pt (the norm for Purchase Intent is 5.3pt with a standard deviation of 0.5pt)

Which conclusion should the analyst make based on this data?


Campaign ad recall is significantly worse than the norm above 95% confidence


Campaign purchase intent is significantly better than the norm above 95% confidence


Campaign favorability is significantly better than the norm above 95% confidence


Campaign message association is significantly worse than the norm above 95% confidence

Question # 4

A brand plans to run a one-month multi-cell Conversion Lift test on Facebook. The cells are designed to help the brand understand experimentally whether its current creative strategy works for different business goals. The different targets are as follows:

• Prospective audience

• Retargeting audience

What should the analyst recommend pursuing after the test?


Continue with the creative strategy for all cells with positive statistically significant conversion lift


Continue with the creative strategy for the cell with the highest positive statistically significant conversion lift


Continue with the creative strategy for all cells with positive conversion lift


Continue with the creative strategy for the cell with the highest positive conversion lift

Question # 5

An advertiser is reviewing campaign results in Facebook Attribution and determines that optimizing its campaigns for website content views appears to be more successful than optimizing for clicks.

Details reviewed were as follows:

• A last touch attribution model was used

• Look back window was 90 days

• Campaigns optimized for clicks delivered a cost per view for SO.55

• Campaigns optimized for website content views yielded a cost per view of S0.49

The advertiser wants to assess whether or not this difference is meaningful or due to random chance before adjusting its strategy accordingly. What action could the analyst take to determine if the difference is meaningful?


Run a multi-cell Conversion Lift test


Run data-driven attribution


Run a single-cell Conversion Lift test


Run a single linear regression analysis