AI Associate Data for AI Practice Question

Exhibit

{
  "dataflow": "EinsteinDataPrep_Sales",
  "nodes": [
    {
      "id": 1,
      "type": "source",
      "object": "Opportunity"
    },
    {
      "id": 2,
      "type": "transform",
      "operation": "filter",
      "condition": "StageName = 'Closed Won'"
    },
    {
      "id": 3,
      "type": "output",
      "target": "EinsteinDiscovery_Stage"
    }
  ]
}

Refer to the exhibit. A dataflow is set up to prepare data for a prediction model. The model is expected to predict close probability for all open opportunities. What is wrong with this dataflow?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The filter on StageName is too restrictive; it excludes non-won opportunities needed for training.

The filter excludes all opportunities that are not 'Closed Won'. The model should be trained on both won and lost opportunities to predict close probability. The filter should be removed or include all stages.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The output target should be a dataset, not a model.
Why it's wrong here
Output to model is valid for Einstein Discovery.
✓
The filter on StageName is too restrictive; it excludes non-won opportunities needed for training.
Why this is correct
To predict close probability, the model needs examples of both won and lost deals.
✗
The source should be Lead, not Opportunity.
Why it's wrong here
Close probability is for opportunities.
✗
The dataflow is missing a transform node to remove null values.
Why it's wrong here
Null handling is important but not the primary issue.

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Last reviewed: Jun 23, 2026

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