AI Associate Practice Test 5 — 10 Questions

Question 1

A company wants to deploy an AI system that makes hiring decisions. To comply with ethical guidelines, what should they do before deployment?

Accepted Answer

Conduct an ethics review and perform bias testing on diverse datasets.. Conducting an ethics review and performing bias testing on diverse datasets are essential steps to identify and mitigate potential discriminatory outcomes in AI-driven hiring systems. This aligns with ethical AI frameworks that require fairness, accountability, and transparency before deployment, ensuring the model does not perpetuate historical biases or violate anti-discrimination laws.

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Ensure the system achieves high accuracy and ignore other metrics.

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Deploy immediately and monitor for issues.

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Test the system only on a small dataset to expedite launch.

Question 2

A company wants to build an autonomous AI agent in Salesforce that can handle customer returns, refunds, and exchanges without human intervention. Which THREE components are required to build this agent using Agentforce?

Accepted Answer

Topics and Actions. To build an autonomous AI agent in Salesforce that handles customer returns, refunds, and exchanges without human intervention, you need Topics and Actions to define the specific business processes (e.g., 'Process Return') and the corresponding API calls or flows, Agent Builder to configure the agent's behavior and link it to those topics, and Testing in Agent Builder to validate the agent's responses and ensure it operates correctly before deployment.

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Prompt Builder

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Einstein Copilot

Question 3

A team is labeling text data for a sentiment analysis model. To ensure consistency and quality, which practice should they prioritize?

Accepted Answer

Use majority voting among multiple labelers.. Majority voting among multiple labelers reduces individual bias and errors, improving label consistency and quality for training data. This approach is standard in supervised learning for sentiment analysis because it aggregates diverse judgments, leading to more reliable ground truth labels.

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Use a single expert labeler for all data.

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Label all data by a single expert labeler.

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Allow each labeler to interpret guidelines freely.

Question 4

According to Salesforce's AI ethics principles, which three pillars should guide the development of AI applications?

Accepted Answer

Accountability.. Accountability is one of Salesforce's core AI ethics principles, requiring that organizations take responsibility for the outcomes of their AI systems. This principle ensures that there is human oversight and that AI applications are designed with mechanisms for redress and governance, aligning with Salesforce's commitment to ethical AI development.

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Profitability.

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Scalability.

Question 5

A company is deploying an AI model that recommends next best actions for sales reps. They notice that the model's recommendations are biased towards high-revenue opportunities. Which data-related action can help reduce this bias?

Accepted Answer

Oversample the underrepresented segments in the training data. Oversampling underrepresented segments in the training data directly addresses the class imbalance that causes the model to favor high-revenue opportunities. By increasing the frequency of low-revenue examples, the model learns to treat all segments more equally, reducing bias in its recommendations. This is a standard data-level technique for mitigating bias in AI models.

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Use a larger neural network model

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Encrypt the data before training

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Remove all low-revenue opportunities from the training data

Question 6

A service organization wants to deploy an Einstein Bot to handle common support inquiries. They need to define the bot's conversational flow and train it to understand user requests. Which THREE components must be configured in the bot builder?

Accepted Answer

Intents. Intents (A) are correct because they define the purpose or goal of a user's input, such as 'Check Order Status' or 'Reset Password'. In the Einstein Bot Builder, intents map user utterances to specific bot actions, enabling the bot to understand and route requests appropriately. Without intents, the bot cannot classify what the user wants.

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Prediction scores

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Training phrases

Question 7

A sales manager wants to automatically surface the most important emails that require immediate attention from a high volume of daily customer messages. Which Einstein feature should they enable?

Accepted Answer

Einstein Email Insights. Einstein Email Insights (D) is the correct feature because it uses natural language processing (NLP) to analyze email content and metadata, automatically prioritizing messages that require immediate attention based on urgency, sender importance, and context. This directly addresses the sales manager's need to surface critical emails from a high volume of daily customer messages without manual sorting.

Answer

Einstein Conversation Insights

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Einstein Activity Capture

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Einstein Lead Scoring

Question 8

Which method is most suitable for ingesting streaming data from IoT sensors into a data lake?

Accepted Answer

Real-time streaming with Apache Kafka.. Apache Kafka is the most suitable option because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion. IoT sensors generate continuous, high-velocity data streams, and Kafka's publish-subscribe model allows data to be ingested into a data lake with low latency, ensuring near-real-time availability for analytics.

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Copying data via FTP.

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Batch ingestion every 24 hours.

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Manual upload via web interface.

Question 9

A company wants to use Einstein Reply Recommendations in Service Cloud. What data is required to train the model?

Accepted Answer

Case comments and chat transcripts.. Einstein Reply Recommendations in Service Cloud uses historical service interactions to suggest relevant replies to agents. The model is trained on case comments and chat transcripts because these contain the natural language patterns and resolutions that agents actually use in real-time service conversations, enabling the AI to learn effective response strategies.

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Knowledge articles only.

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Email templates.

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Historical email replies and customer satisfaction ratings.

Question 10

When using Einstein Lead Scoring, which data source is most critical for generating accurate lead scores?

Accepted Answer

Converted lead records with attached opportunities. Converted lead records with attached opportunities are the most critical data source because Einstein Lead Scoring uses supervised machine learning to analyze historical patterns in leads that successfully converted into opportunities. By training on these converted records, the model learns which lead attributes and behaviors are predictive of conversion, enabling it to assign accurate scores to new leads. Without this historical conversion data, the model lacks the ground truth needed to distinguish high-quality leads from low-quality ones.

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Lead source (e.g., Webinar, Trade Show)

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Lead field update timestamps

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Email open rates from marketing campaigns