AI0-001 Practice Test 9 — 10 Questions

Question 1

A data engineer needs to combine two datasets, each with unique customer_id, to include all records from both datasets. Which join type should be used?

Accepted Answer

FULL OUTER JOIN. A FULL OUTER JOIN returns all records from both datasets, matching rows where the customer_id is present in both and filling in NULLs for missing matches. This is the only join type that guarantees every unique customer_id from either dataset appears in the result, which is exactly what the requirement specifies.

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RIGHT JOIN

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LEFT JOIN

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INNER JOIN

Question 2

A research lab is training a large language model and wants to minimize its environmental impact. Which THREE practices are most effective for reducing the carbon footprint of model training?

Accepted Answer

Apply model compression techniques like pruning and quantization. Model compression techniques like pruning and quantization directly reduce the computational requirements of training and inference. Pruning removes redundant weights, and quantization reduces the precision of weights (e.g., from 32-bit to 8-bit), which lowers the number of operations and memory bandwidth needed, thereby decreasing energy consumption and carbon emissions.

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Extend the number of training epochs to ensure convergence

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Increase the model size to achieve better accuracy faster

Question 3

An organization wants to use a pre-trained language model from a third-party vendor. What is the most important security step before deployment?

Accepted Answer

Vet the model for backdoors and malicious behavior. Vetting a third-party pre-trained model for backdoors and malicious behavior is the most important security step before deployment because the model could contain hidden triggers or biases intentionally inserted by the vendor or a compromised supply chain. Without this vetting, the organization risks deploying a model that behaves maliciously under specific conditions.

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Host the model on a public cloud

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Apply differential privacy to the model

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Fine-tune the model on internal data

Question 4

An AI team is developing a model that will make hiring recommendations. Which ethical principle requires that candidates be informed about how their data is used and have a way to challenge decisions?

Accepted Answer

Transparency and contestability.. Transparency and contestability directly require that candidates be informed about data use and have a way to challenge decisions. Beneficence, non-maleficence, and fidelity are ethical principles but do not specifically mandate disclosure and challenge mechanisms in automated hiring.

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Fidelity.

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Non-maleficence.

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Beneficence.

Question 5

A media company is deploying an AI service that transcribes customer support calls and then summarizes them for agents. The transcription model runs on-premises and produces text, but the summarization LLM is hosted in a public cloud. Compliance requires that no raw call audio or verbatim transcript ever leaves the company network. Which deployment pattern best satisfies this requirement while still using the cloud LLM?

Accepted Answer

Run the transcription on-premises, redact or abstract the transcript locally, and send only the redacted, non-verbatim text to the cloud LLM for summarization.. The requirement is a data-egress boundary: raw audio and verbatim transcripts must stay on-premises. The only pattern that respects that boundary while still using a cloud LLM is to perform transcription locally, then redact or abstract the text before sending only sanitized content to the hosted model. Encryption, private endpoints, and prompt instructions do not change where the sensitive data is processed, so they cannot satisfy the constraint.

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Transcribe in the cloud and store the raw audio in a cloud object store, but encrypt the bucket with a customer-managed key.

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Use a cloud-hosted transcription model and a cloud-hosted summarization model, and configure a private VPC endpoint between them.

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Send the raw audio to the cloud LLM and instruct the model via the system prompt to ignore the audio and only produce a summary.

Question 6

A company is deploying a fraud detection model that must return predictions within 100ms to avoid transaction delays. The team is deciding between batch and real-time inference. Which factor most strongly supports a real-time inference architecture?

Accepted Answer

The application requires immediate feedback for each transaction. Real-time inference is required when the application must return predictions within strict latency bounds (e.g., 100ms) to avoid transaction delays. The need for immediate feedback per transaction directly aligns with a real-time architecture, where each request is processed individually as it arrives, rather than waiting for a batch window. Batch inference would introduce unacceptable latency because it processes groups of records on a schedule, not on-demand.

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The model requires large amounts of historical data for each prediction

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The infrastructure budget is limited and must be optimized

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The model can be retrained weekly using gathered data

Question 7

A financial institution is deploying an AI system to approve personal loans. To comply with the EU AI Act's high-risk AI requirements, the bank must ensure meaningful human oversight. Which implementation BEST satisfies this requirement?

Accepted Answer

Require a human to review and approve every loan decision before it becomes final. The EU AI Act's high-risk requirements (Article 14) mandate that humans can effectively oversee AI systems, including the ability to intervene, override, or halt decisions. Requiring a human to review and approve every loan decision before it becomes final embeds a genuine human-in-the-loop control at the decision point, which is the strongest form of meaningful oversight for a high-risk credit-scoring use case. This ensures no automated output becomes binding without human judgment, directly satisfying the oversight obligation.

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Use a separate AI model to audit the primary AI's decisions weekly

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Allow applicants to appeal AI decisions through a customer service process

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Provide a dashboard showing the AI's confidence score for each application

Question 8

An organization wants to detect if someone is trying to steal their proprietary machine learning model by querying its API. Which monitoring technique is MOST effective?

Accepted Answer

Monitoring for anomalous query patterns, such as high volume or systematic variations. Model extraction attacks rely on systematically querying the API to reconstruct the model's decision boundary. Monitoring for anomalous query patterns—such as high request volume, uniform input distributions, or systematic variations (e.g., grid-like sampling of feature space)—directly detects the behavioral signature of extraction attempts, unlike passive controls that do not address the attack vector.

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Output filtering to remove sensitive information from responses

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Rate limiting on the number of API requests per user

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Input validation to reject malformed requests

Question 9

A team is building a recommendation system using collaborative filtering. They have a sparse user-item matrix. Which technique should they use to handle the sparsity and improve recommendations?

Accepted Answer

Matrix factorization. Matrix factorization (B) is the correct technique because it decomposes the sparse user-item matrix into lower-dimensional latent factor matrices, effectively capturing underlying patterns and filling in missing entries. This directly addresses sparsity by learning dense representations that generalize beyond observed interactions, which is a core strength in collaborative filtering for recommendation systems.

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Association rule mining

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k-nearest neighbors

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Content-based filtering

Question 10

A data scientist is building a binary classification model to predict customer churn. The dataset has 90% non-churn and 10% churn. After training, the model achieves 90% accuracy, but the recall for the churn class is only 20%. Which metric should the team primarily focus on to evaluate the model's effectiveness?

Accepted Answer

Recall for the churn class. With 90% non-churn and 10% churn, a model that predicts 'no churn' for everyone achieves 90% accuracy but 0% churn recall. The current model's 20% churn recall means it misses 80% of actual churners, which is the business-critical failure. Recall for the churn class directly measures how many true churners are caught, making it the primary metric.

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Accuracy

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Area Under the ROC Curve (AUC-ROC)

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Precision for the non-churn class