The correct answer is to select relevant features that are likely to influence the prediction, alongside ensuring data quality by cleaning missing values and outliers. These two actions are best practices because feature selection directly impacts model accuracy by removing noise and irrelevant variables, while data cleaning prevents skewed predictions caused by incomplete or anomalous training data. On the Salesforce AI Associate exam, this concept tests your understanding of the foundational prerequisites for any machine learning model within the Einstein Prediction Service framework. A common trap is focusing solely on model complexity or algorithm choice, when the exam emphasizes that garbage in equals garbage out. Remember the mnemonic “Clean and Curate” to recall that cleaning data and curating relevant features are the twin pillars of successful implementation.
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO actions are best practices when implementing Einstein Prediction Service?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Clean the data to handle missing values and outliers.
Option B is correct because data quality directly impacts the accuracy and reliability of Einstein Prediction Service models. Cleaning data to handle missing values and outliers ensures that the training data is representative and reduces the risk of skewed predictions, which is a fundamental prerequisite for any machine learning model within Salesforce's AI framework.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Ignore correlated features to simplify the model.
Why it's wrong here
Correlated features can be handled but not ignored.
✓
Clean the data to handle missing values and outliers.
Why this is correct
Data cleaning improves model accuracy.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Select relevant features that are likely to influence the prediction.
Why this is correct
Feature selection reduces overfitting and improves performance.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Include all available fields in the dataset for maximum information.
Why it's wrong here
Including all fields can introduce noise and overfitting.
✗
Use the default field mapping without review.
Why it's wrong here
Reviewing mapping ensures correct data usage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that more data always leads to better predictions, but in practice, irrelevant or noisy features degrade model performance, making feature selection and data cleaning critical.
Detailed technical explanation
How to think about this question
Einstein Prediction Service uses automated machine learning (AutoML) to train models, but it relies on the quality and relevance of input data. Under the hood, the service applies techniques like feature engineering and model tuning, but it cannot compensate for systematic data issues such as missing values or irrelevant fields. In a real-world scenario, a sales prediction model trained on uncleaned data with outliers (e.g., extreme deal amounts) might overemphasize those anomalies, leading to poor generalization on typical sales data.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Clean the data to handle missing values and outliers. — Option B is correct because data quality directly impacts the accuracy and reliability of Einstein Prediction Service models. Cleaning data to handle missing values and outliers ensures that the training data is representative and reduces the risk of skewed predictions, which is a fundamental prerequisite for any machine learning model within Salesforce's AI framework.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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