AI0-001 Practice Exam 57 — 20 Questions

Question 1

A retail company wants to forecast weekly demand for thousands of SKUs across stores. The data includes strong seasonal patterns, promotional calendars, and intermittent demand for slow-moving items. The team has limited time and wants a baseline before investing in custom deep learning. Which approach is BEST as the initial production model?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use a classical time-series method such as exponential smoothing or ARIMA applied per SKU-store series with seasonal and promotional regressors.. A fast, reliable baseline for seasonal and intermittent demand is a classical time-series method applied per SKU-store series with promotional regressors. These methods are well understood, quick to deploy, and produce explainable per-series forecasts that support monitoring. Deep learning, LLM prompting, and clustering either require disproportionate investment or do not yield actionable SKU-level forecasts.

Answer

Apply a clustering algorithm to group SKUs by sales similarity and forecast only the cluster centroids.

Answer

Train a single global deep neural network on all SKUs and stores with embeddings for product and location.

Answer

Deploy a large language model to generate demand forecasts by prompting it with recent sales figures and promotion descriptions.

Question 2

A healthcare organization uses a machine learning model to predict patient readmission risk. The model was trained on a dataset that includes sensitive patient information. During a security review, the team wants to verify that an attacker cannot determine whether a specific patient's record was part of the training set by querying the model. Which of the following should the team perform to directly assess this risk?

Accepted Answer

Conduct a membership inference attack simulation against the model.. Membership inference attacks specifically aim to determine if a data point was used during training. Simulating such an attack allows the team to empirically measure the model's susceptibility. Model inversion, k-anonymity, and SHAP values address different aspects of privacy or interpretability and do not directly evaluate the risk of membership inference.

Answer

Apply k-anonymity to the training dataset before retraining the model.

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Perform a model inversion attack to reconstruct training data samples.

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Use SHAP values to explain the model's predictions for individual patients.

Question 3

A data scientist is building a model to predict equipment failure using sensor data. The dataset contains time-series readings from multiple sensors, and the goal is to detect anomalies that precede failures. Which TWO feature engineering techniques are most appropriate for this time-series data? (Choose two.)

Accepted Answer

Compute rolling window statistics such as mean, standard deviation, and min/max over recent time intervals.. Rolling window statistics and lag features are essential for time-series data because they encode temporal dependencies and trends. Rolling statistics summarize recent behavior, while lag features provide historical context. Together, they enable the model to detect anomalies that precede equipment failure. The other options either ignore time order or are not suitable for temporal feature extraction.

Answer

Replace missing sensor values with the overall mean of the entire dataset.

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Apply one-hot encoding to the timestamp column to represent each time point as a binary vector.

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Perform principal component analysis (PCA) on the raw sensor readings to reduce dimensionality.

Question 4

A media company uses a generative AI assistant to draft customer responses. After an update to the underlying foundation model, agents report that responses sometimes include fabricated policy details. The operations team must detect this regression quickly and prevent fabricated content from reaching customers. Which combination of controls is most appropriate?

Accepted Answer

Add automated groundedness and hallucination checks against the approved policy knowledge base, and require agent approval before any response is sent.. Detecting fabricated policy details requires verifying generated text against the authoritative source, which groundedness or hallucination checks provide. Preventing delivery requires a gate such as agent approval before sending. Rollback, prompt tweaks, and retrospective sampling either do not detect unsupported claims or act too late to protect customers.

Answer

Roll back to the previous foundation model version and monitor customer satisfaction scores for improvement.

Answer

Enable request logging and review a sample of conversations weekly for quality issues.

Answer

Lower the model's maximum output token limit and add a system prompt instructing it to be accurate.

Question 5

A bank plans to deploy a credit-scoring model that will make automated decisions about loan applications. Compliance requires the bank to provide meaningful information about how the system reaches decisions and to give applicants a way to contest outcomes. Which TWO operational practices best support these obligations? (Choose two.)

Accepted Answer

Generate per-applicant reason codes that identify the principal factors driving each decision, using techniques such as SHAP or LIME.. Meaningful transparency and contestability for automated credit decisions rest on two capabilities: per-applicant reason codes from local attribution methods, and a documented human review path that can reconsider and override the model. Together they let an applicant understand the decision and challenge it. Publishing data and weights, collapsing to one tree, or deleting decision logs either breaches privacy or removes the evidence needed for review.

Answer

Publish the full training dataset and model weights so applicants can inspect them directly.

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Disable logging of decision inputs to minimize the personal data retained about applicants.

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Reduce the model to a single decision tree so every applicant can be shown the same global tree structure.

Question 6

A city agency deploys an AI system that scores permit applications. The vendor refuses to disclose model weights or feature importance, citing trade secrets. The agency's oversight board must still meet its obligation to explain adverse decisions to applicants. Which approach best satisfies that obligation?

Accepted Answer

Require the vendor to supply model-agnostic explanations, such as local surrogate or counterfactual reason codes, for each adverse decision.. The agency must explain adverse decisions without forcing the vendor to reveal proprietary internals. Model-agnostic explainability, including local surrogates and counterfactual reason codes, derives per-decision explanations from observable behavior, so applicants receive meaningful factors and the board meets its duty. Full disclosure, generic notices, and replacing the system each either breach the constraint or fail to provide decision-specific reasoning.

Answer

Inform applicants that the decision was made by an automated system and provide a generic appeal link.

Answer

Replace the vendor model with a transparent rule-based scoring system that the agency builds in-house.

Answer

Publish the vendor's source code and trained weights so independent researchers can audit the decisions.

Question 7

A data engineer is preparing a dataset for a machine learning model that predicts customer lifetime value. The dataset contains a 'monthly_spend' column with a highly right-skewed distribution. The team wants to apply a transformation to reduce skewness while preserving the relative order of values. Which transformation is most appropriate?

Accepted Answer

Apply a logarithmic transformation (log(1 + x)) to the 'monthly_spend' column.. A logarithmic transformation is the most appropriate because it compresses large values and reduces right skewness while preserving the relative order of observations. Unlike standardization, it changes the distribution shape; unlike binning or rank transformation, it retains the continuous nature and magnitude relationships, making it well-suited for monetary data that is positively skewed.

Answer

Standardize the 'monthly_spend' column by subtracting the mean and dividing by the standard deviation.

Answer

Replace each value with its rank among all values in the column (rank transformation).

Answer

Apply one-hot encoding to the 'monthly_spend' column by binning into deciles.

Question 8

A self-driving car company is testing a perception model that detects pedestrians. The model achieves 99% accuracy on the test set but fails to detect pedestrians wearing dark clothing at night. The company wants to improve the model's robustness. Which action should the team take to best address this specific weakness?

Accepted Answer

Collect and annotate more nighttime images of pedestrians wearing dark clothing and add them to the training set.. Collecting and annotating more nighttime images of pedestrians in dark clothing is the most direct way to address the specific weakness. The model's failure indicates a gap in the training distribution, so adding representative data will help it learn the necessary visual cues. Other options either do not target the root cause or introduce trade-offs without improving fundamental recognition.

Answer

Increase the model's depth by adding more layers to capture more complex patterns.

Answer

Apply data augmentation by randomly rotating and flipping existing daytime images.

Answer

Reduce the model's confidence threshold for pedestrian detection to increase sensitivity.

Question 9

A retail analytics team wants to group customers into segments based on purchase frequency, average order value, and recency, without having any predefined segment labels. They plan to use an algorithm that partitions customers into a fixed number of groups by minimizing within-cluster variance. Which technique should they use?

Accepted Answer

K-means clustering. K-means clustering is the correct technique because it is an unsupervised algorithm that partitions observations into a predefined number of clusters by minimizing the sum of squared distances to cluster centroids. The team's goal of grouping customers without labels aligns perfectly with K-means. The other options are either supervised prediction methods or dimensionality reduction techniques that do not produce discrete customer segments.

Answer

Linear regression

Answer

Logistic regression

Answer

Principal component analysis

Question 10

A hospital has deployed an AI triage assistant that summarizes patient intake notes and suggests an acuity level for the emergency department. Clinicians report that the assistant sometimes produces confident but unsupported acuity suggestions. The operations team must add safeguards appropriate for a high-stakes clinical deployment. (Choose two.)

Accepted Answer

Display the source note spans that the model used for each acuity suggestion so clinicians can verify the evidence before accepting it.. The reported failure is confident but unsupported suggestions, so the safeguards must both expose the evidence behind each suggestion and keep a clinician as the accountable decision-maker. Attribution to source note spans makes the claim checkable, and mandatory confirmation or override prevents an unverified acuity level from entering the patient record.

Answer

Retrain the assistant on the hospital's historical triage outcomes and redeploy it as the primary acuity assigner.

Answer

Lower the model's temperature and top-p sampling values so its generated summaries are more deterministic.

Answer

Increase the model's context window so it can ingest the patient's entire longitudinal chart in one request.

Question 11

An insurance company operates an AI claims-triage model that flags suspicious claims for human review. After six months in production, the operations team observes that the model's precision has fallen steadily while recall has stayed roughly constant, and the volume of false-positive flags has grown. The data science team suspects the input data pipeline is the cause rather than the model weights. Which TWO operational checks should the team perform first to diagnose the problem? (Choose two.)

Accepted Answer

Audit the feature engineering and ETL pipeline for silent failures such as nulls, default values, or unit changes that alter feature semantics.. Stable recall with falling precision points to inputs that no longer match training conditions rather than to the model weights. Comparing production feature distributions against the training baseline detects drift or schema changes, and auditing the ETL and feature engineering stages uncovers silent failures such as nulls, defaults, or unit changes. Retraining, threshold changes, or more reviewers treat symptoms without identifying the pipeline defect.

Answer

Increase the model's decision threshold so fewer claims are flagged as suspicious.

Answer

Retrain the model immediately on the last six months of production data to restore precision.

Answer

Expand the human review team so more flagged claims can be examined manually while the model remains unchanged.

Question 12

A startup is building a retrieval-augmented generation (RAG) application that must answer questions over a 500,000-document internal knowledge base with low query latency. They plan to use a vector database. Which TWO design choices best support fast, scalable similarity search? (Choose two.)

Accepted Answer

Choose embeddings with a smaller dimensionality that still preserve semantic quality for the domain.. ANN indexing with HNSW and lower-dimensional embeddings both reduce the computational cost of similarity search on a large corpus. HNSW avoids exhaustive comparison, while reduced dimensionality shrinks distance calculations and memory use. Together they enable low-latency retrieval at the 500,000-document scale without sacrificing meaningful semantic accuracy.

Answer

Store the full text of each document inside the vector database and run keyword search alongside vector search.

Answer

Recompute embeddings for all documents on every query to ensure freshness.

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Disable metadata filtering so the index can scan all vectors uniformly.

Question 13

A data science team is preparing a dataset of customer support tickets to train a supervised model that routes each ticket to the correct department. They have 40,000 tickets labeled with one of eight departments. Which TWO preprocessing steps are most appropriate before training? (Choose two.)

Accepted Answer

Split the data into training, validation, and test sets before fitting any preprocessing.. Text classification requires converting raw strings into numeric features and preserving an honest evaluation split. Tokenization with TF-IDF or embeddings supplies the numerical representation, while splitting before fitting preprocessing prevents leakage. Together these steps prepare the ticket data so the routing model can be trained and fairly assessed on unseen examples.

Answer

Delete all tickets shorter than 20 words to reduce noise.

Answer

Standardize the department labels to zero mean and unit variance.

Answer

Apply one-hot encoding to the raw ticket text strings.

Question 14

An AI governance committee is reviewing a resume-screening model. The model's accuracy is high overall, but its false negative rate is much higher for applicants from one demographic group than for others. The committee wants to address this disparity. Which action best targets the problem?

Accepted Answer

Measure and mitigate bias across demographic groups, including error rates and representation. The scenario describes unequal false negative rates, a fairness problem that requires measuring performance by demographic group and mitigating the disparity through data or threshold adjustments. Adding data, removing protected attributes, or switching to a simpler algorithm may change accuracy or transparency but does not directly close the group-level error gap.

Answer

Increase the model's overall accuracy by adding more training data

Answer

Remove all demographic attributes from the training data

Answer

Replace the model with a simpler algorithm that is inherently interpretable

Question 15

A healthcare AI team is training a model to predict patient readmission risk from electronic health records. The dataset contains sensitive patient data and must comply with HIPAA. They need to ensure that the model training process does not expose protected health information (PHI) and that the model does not memorize individual patient data. Which technique should they implement?

Accepted Answer

Differential privacy during training. Differential privacy is the only technique listed that provides a formal guarantee that the model does not memorize individual patient data. By injecting noise during training, it ensures that the model's predictions are statistically similar whether or not any single patient's data is included. This directly addresses the HIPAA compliance and privacy concerns in the scenario.

Answer

Data augmentation with synthetic records

Answer

Federated learning across hospitals

Answer

Homomorphic encryption of the model weights

Question 16

A retail company is deploying an AI system that generates personalized marketing copy and product recommendations. The legal team wants to align the deployment with the NIST AI Risk Management Framework's core functions. Which two activities are part of the MAP function? (Choose two.)

Accepted Answer

Categorizing the likelihood and potential impact of risks such as manipulated recommendations or exposure of inferred preferences.. The MAP function establishes context and characterizes risks. Documenting business context and stakeholders, and categorizing likelihood and impact of identified risks, are core mapping activities. Applying controls belongs to MANAGE, continuous post-deployment tracking belongs to MEASURE, and assigning accountability across the organization belongs to GOVERN, which underpins all other functions.

Answer

Establishing organizational policies that assign accountability for AI risk decisions across product, legal, and engineering teams.

Answer

Applying technical controls such as output filters and rate limits to reduce the chance of harmful generated copy reaching customers.

Answer

Continuously tracking key performance indicators and drift metrics after the recommendation engine enters production.

Question 17

A marketing team wants to use a third-party generative AI service to create ad copy. The service provider states that submitted prompts and outputs may be used to improve its models. The company's legal team is concerned about confidential product launch details being entered into the tool. Which of the following is the MOST appropriate first step?

Accepted Answer

Prohibit entering confidential information into the tool until a data processing agreement and enterprise configuration that excludes data from training are in place.. When a provider's terms permit using inputs for model improvement, confidential material should not be entered until contractual and technical safeguards exist. A data processing agreement plus an enterprise configuration that excludes submissions from training reduces the exposure directly. Privacy policies, paraphrasing, and chat deletion do not create enforceable confidentiality protections, so they fail to address the identified risk.

Answer

Allow the team to proceed because the provider's public privacy policy already covers all customer data handling.

Answer

Instruct the team to paraphrase confidential details before submitting prompts so the information is no longer recognizable.

Answer

Require the team to delete the chat history after each session to remove the provider's copy of the data.

Question 18

A machine learning engineer is building a model to predict whether a customer will make a purchase within the next week. The dataset contains 10,000 samples with 20 features, and the target variable is binary. The engineer wants to use a model that provides interpretable results to explain predictions to business stakeholders. Which model is most appropriate?

Accepted Answer

Logistic regression. Logistic regression is a linear model that provides coefficients for each feature, making it highly interpretable. Business stakeholders can understand how each feature influences the predicted probability of purchase. While ensemble methods like random forest and gradient boosting may offer higher accuracy, they are less transparent. Support vector machines with non-linear kernels are also difficult to interpret. Therefore, logistic regression is the most appropriate choice when interpretability is a primary requirement.

Answer

Gradient boosting machine

Answer

Random forest

Answer

Support vector machine with RBF kernel

Question 19

A team trains a recurrent neural network to translate sentences averaging 60 words. During evaluation they notice that translations of the final words in long sentences are frequently wrong, while the opening words are translated accurately. Which architectural change best addresses this behavior?

Accepted Answer

Replace the recurrent layers with an attention-based transformer encoder-decoder. Recurrent networks compress the entire source sentence into a fixed-size hidden state and suffer vanishing gradients across many timesteps, which disproportionately harms recall of distant tokens. An attention-based transformer removes the sequential bottleneck, allowing direct token-to-token connections and preserving context for the final words of long sentences.

Answer

Lower the learning rate and train for more epochs

Answer

Increase the batch size used during training

Answer

Increase the number of recurrent layers stacked in the encoder

Question 20

A retail company's ML platform team notices that one of their production models has begun returning predictions with a drastically different distribution than during training. The monitoring dashboard shows the input feature distributions have shifted but no code or model artifacts have changed. The team wants to automatically trigger a retraining pipeline when this condition is detected. Which approach should they implement?

Accepted Answer

Configure a data drift monitor that computes statistical distance between live inference inputs and the training baseline, and wire its alert to the retraining pipeline trigger.. The scenario describes data drift: input feature distributions have changed while the model and code remain the same. A data drift monitor that compares live inputs to the training baseline is the correct tool to detect this and can be integrated with the retraining pipeline. Other monitoring types either require labels, focus on label relationships, or are overly sensitive.

Answer

Enable concept drift detection by comparing the relationship between features and labels over time and trigger retraining when the mapping changes.

Answer

Implement a model versioning system that records the exact training data hash and triggers retraining whenever the hash of the incoming data differs from the stored hash.

Answer

Set up a model performance monitor that tracks prediction accuracy against ground truth labels and triggers retraining when accuracy drops below a threshold.