A retail company is building a recommendation system. The data science team has access to transaction data, but they lack customer demographic data. The business requirement specifies that the model should personalize recommendations based on demographics. The team is evaluating their data readiness. What should they do first?

Proceed with transaction data only and rely on collaborative filtering techniques.

Identify the demographic data gap and develop a plan to acquire or substitute the missing data.

Use the transaction data to infer demographics through modeling.

Delay the project until demographic data is available.

What does representativeness validation ensure about a dataset?

The data reflects the diversity and characteristics of the target use cases

The data is collected from a single source for consistency

The data is free from any errors or outliers

The data is stored in a format that is easy to read

What is the purpose of a data readiness go/no-go decision?

To decide which cloud provider to use for data storage

To determine whether to proceed with model development based on data readiness

To assign team roles for the project

To select the best programming language for implementation

After identifying that a dataset lacks examples of a rare disease, what is an appropriate strategy to address this data gap?

Ignore the gap and proceed with model training

Delete the existing data and start over

Change the model to a simpler algorithm

Collect more data from specialized sources

PMICertified Professional in Managing AI (PMI-CPMAI)

Domain 3Objective 8

Determine If Data Meets Solution Needs PMI-CPMAI Practice Questions (Page 4)

Part of the Identify Data Needs domain, which accounts for 26% of the PMI-CPMAI exam. PMI does not publish an official question count, but from its 160-minute exam (~65–105 total, ~17–27 in this domain), expect 2–3 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

5concepts

26%of the exam

Questions 16–20

16foundation · easy
During a requirements comparison, a data analyst finds that the available dataset lacks a field for customer age, which is required by the model specification. What does this finding represent?
Select an answer first
17application · medium
A retail company is building a recommendation system. The data science team has access to transaction data, but they lack customer demographic data. The business requirement specifies that the model should personalize recommendations based on demographics. The team is evaluating their data readiness. What should they do first?
Select an answer first
18foundation · easy
What does representativeness validation ensure about a dataset?
Select an answer first
19foundation · easy
What is the purpose of a data readiness go/no-go decision?
Select an answer first
20foundation · easy
After identifying that a dataset lacks examples of a rare disease, what is an appropriate strategy to address this data gap?
Select an answer first

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Determine If Data Meets Solution Needs PMI-CPMAI Practice Questions (Page 4)

Questions 16–20

During a requirements comparison, a data analyst finds that the available dataset lacks a field for customer age, which is required by the model specification. What does this finding represent?

What does representativeness validation ensure about a dataset?

What is the purpose of a data readiness go/no-go decision?

After identifying that a dataset lacks examples of a rare disease, what is an appropriate strategy to address this data gap?

Keep studying — Identify Data Needs