AI0-001 Practice Exam 60 — 20 Questions

Question 1

A hospital wants an AI system to review chest X-ray images and flag those that may show pneumonia, but a radiologist will make the final diagnosis. The IT team must classify this system for documentation. Which category of AI best describes this deployment?

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

Accepted Answer

Narrow AI. The system performs one bounded task—identifying possible pneumonia in chest X-rays—so it is narrow AI. General AI implies human-level breadth across many domains, supervised learning describes how a model is trained rather than what the system is, and reactive machine is an outdated capability category that does not fit a trained deep learning model.

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Supervised learning

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General AI

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Reactive machine

Question 2

A machine learning engineer is evaluating a classifier on a dataset with 1,000 examples where only 30 are positive. The model predicts the negative class for almost every example. The team reports 97% accuracy and claims success. Which metric should the engineer introduce to reveal the model's poor performance on the positive class?

Accepted Answer

Recall for the positive class. Accuracy is misleading under severe class imbalance because a trivial majority-class predictor scores high. Recall on the positive class directly measures how many of the 30 true positives were captured, exposing the near-zero detection rate. Recall is the appropriate complement to accuracy when the cost of false negatives is high, such as fraud, medical screening, or safety defects, and it guides the team toward resampling, class weighting, or threshold adjustment.

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Silhouette score

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Mean squared error

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R-squared

Question 3

A data science team is preparing a dataset of loan applications. Each row contains income, credit score, employment length, and a loan amount. Before training a model, the team wants to reduce the influence of income, which is measured in dollars and ranges into the hundreds of thousands, compared with credit score, which ranges from 300 to 850. Which technique should the team apply?

Accepted Answer

Feature scaling. Income and credit score differ by orders of magnitude, so algorithms that rely on distances or gradients will let income dominate. Feature scaling, whether min-max normalization or standardization, brings numeric features onto comparable ranges and is the standard preprocessing remedy. Encoding, dimensionality reduction, and augmentation change the data in ways that do not resolve the scale disparity.

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One-hot encoding

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Data augmentation

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Principal component analysis

Question 4

A data engineer is building a real-time feature store for an AI recommendation engine. The system must ingest millions of clickstream events per second, retain each event for 7 days, and allow the ML model to read the most recent user activity with sub-10ms latency. Which storage technology should the engineer select for the online feature serving layer?

Accepted Answer

A Redis in-memory key-value store with TTL-based expiration. Online feature serving requires a low-latency, key-value access pattern keyed by entity ID, with automatic expiry of stale features. An in-memory key-value store provides O(1) lookups in sub-millisecond time and supports TTL-based eviction, satisfying both the 7-day retention and sub-10ms latency requirements. Streaming platforms and analytical stores serve ingestion and batch computation roles instead.

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Amazon S3 with Parquet files and AWS Glue catalog

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Apache Kafka with a 7-day retention policy

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A Snowflake data warehouse with a materialized view

Question 5

A financial services firm uses an AI model to detect fraudulent transactions. The model's decisions must be explainable to regulators. The data science team proposes using a complex deep neural network with high accuracy. Which of the following approaches best balances accuracy and explainability?

Accepted Answer

Use the deep neural network and apply post-hoc explanation techniques such as LIME or SHAP.. Post-hoc explanation techniques such as LIME and SHAP offer a practical compromise: they explain individual predictions of complex models without requiring the model to be inherently interpretable. This allows the firm to maintain high fraud detection accuracy while providing the transparency regulators demand. Replacing the model with a simpler one may reduce accuracy, and providing only overall metrics is insufficient.

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Use the deep neural network without explanations but provide regulators with the model's overall accuracy metrics.

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Train a surrogate decision tree to mimic the neural network's predictions and use that for explanations.

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Replace the deep neural network with a simple logistic regression model to ensure full interpretability.

Question 6

A retail bank's fraud model was trained on customer transaction data that included account holders in the EU. An internal audit finds the training pipeline copied raw transaction records, including names and card numbers, into an unencrypted research bucket for model retraining. Which action best aligns the remediation with data-protection obligations for that pipeline?

Accepted Answer

Delete the research bucket and retrain using pseudonymized or tokenized transaction records with documented retention limits.. The finding combines excessive identifiability with a purpose and retention failure. Removing the exposed copy stops ongoing risk, while pseudonymization or tokenization plus documented retention limits lets the fraud model keep learning from transaction behavior without carrying direct identifiers into a research environment. Encryption, monitoring, and new consent each address part of the problem but leave the core data-minimization defect unresolved.

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Add the research bucket to the data-loss-prevention watchlist and require monthly access reviews.

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Apply column-level encryption to the research bucket and continue retraining on the same raw records.

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Obtain a new consent notice from all account holders before the next retraining cycle.

Question 7

A financial services firm is deploying a credit-scoring model that uses alternative data such as utility payments and rental history. The compliance team is concerned about fairness and transparency. Which TWO practices best support responsible AI deployment in this scenario? (Choose two.)

Accepted Answer

Provide adverse-action explanations for declined applicants that identify the principal factors influencing the decision.. Disparate-impact testing with documentation and adverse-action explanations directly address fairness and transparency in credit scoring. Testing detects discriminatory outcomes across protected groups, while explanations make individual decisions understandable and contestable. Together they satisfy core responsible-AI obligations when alternative data is used.

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Use only the most predictive features and remove any feature that reduces overall accuracy.

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Retrain the model monthly on all new applications without human review to keep pace with changing data.

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Encrypt the training data at rest and restrict access to the data science team.

Question 8

A financial services firm is deploying a credit-scoring model built with Amazon SageMaker. Compliance requires that every prediction be explainable to a loan officer and to regulators. The model uses gradient boosting on 120 features. Which approach BEST satisfies the explainability requirement while keeping the production model unchanged?

Accepted Answer

Use SageMaker Clarify to generate SHAP-based feature attributions for each inference request through the Clarify explainer endpoint.. Explainability for regulated decisions requires per-prediction attributions tied to the actual model. SageMaker Clarify's SHAP explainer endpoint returns feature-level contributions for each request while the gradient boosting model remains in production, satisfying both accuracy and compliance. Monitoring tools track aggregate drift, simpler models change behavior, and manual reconstruction is not auditable, so only the Clarify-based approach meets the constraint of leaving the production model unchanged.

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Log the raw feature vector for every request and have analysts manually reconstruct the decision using spreadsheet formulas.

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Enable SageMaker Model Monitor with a data quality baseline and publish the monitoring reports to the loan officers.

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Replace the gradient boosting model with a single decision tree so that every prediction follows a human-readable path.

Question 9

A startup trains a proprietary recommendation model that predicts which products users will buy. The model is served through a public API that returns only the top five product identifiers for each request. The founders are worried that a competitor could clone the model by querying the API extensively. Which control most directly limits this model extraction risk?

Accepted Answer

Enforce per-account query quotas and monitor for high-volume, low-diversity query patterns.. Model extraction depends on the attacker's ability to submit many varied queries and observe outputs, so the most direct countermeasure is to constrain and monitor that query stream. Quotas limit total data collection, and behavioral monitoring detects the low-diversity, high-volume signature typical of cloning attempts. Legal agreements, endpoint rotation, and response trimming either do not operate at the API layer or can be circumvented by simply querying more.

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Require API consumers to sign a license agreement prohibiting reverse engineering of the model.

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Rotate the model's prediction endpoint URL every 24 hours and distribute it through a private channel.

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Return only the single top product instead of the top five to reduce the information per response.

Question 10

A retail bank deploys a machine learning model that scores loan applications. Compliance requires that the bank be able to explain to regulators why any individual applicant was denied, in terms of the applicant's own feature values. The model is a gradient-boosted tree ensemble trained on 200 features. Which approach BEST satisfies this requirement?

Accepted Answer

Apply SHAP (SHapley Additive exPlanations) values to produce per-applicant feature attributions for each decision.. The requirement is individualized, feature-value-based justification for each denial on a complex ensemble. SHAP attributions decompose a single prediction into additive feature contributions, which is exactly what an adverse-action explanation needs. Global importance, coefficient tables, and raw input logging all describe population behavior or stored data rather than the reason for one applicant's specific outcome, so they cannot satisfy the regulator's question.

Answer

Log the model's predicted probability alongside the applicant's raw input record for each decision.

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Report the global feature importance ranking produced by the tree ensemble's gain-based split scores.

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Retrain the model as a logistic regression and present the learned coefficients as the explanation.

Question 11

A media company wants to generate short video summaries from long recordings using a generative AI model. The model is hosted in the cloud, and the company needs to minimize cost while handling unpredictable traffic spikes. Which cloud service model is most appropriate?

Accepted Answer

Serverless inference endpoint with automatic scaling. A serverless inference endpoint with automatic scaling aligns cost with actual usage and handles unpredictable traffic without manual intervention. Dedicated VMs, on-premises clusters, and reserved instances all involve either idle costs or commitment risks that are suboptimal for variable demand.

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Dedicated GPU virtual machine with a fixed hourly rate

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Reserved instances with a one-year commitment

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On-premises GPU cluster with a load balancer

Question 12

An ML platform team is running a recommendation model on a Kubernetes cluster with GPU nodes. During peak traffic, inference pods are frequently evicted and restarted, causing latency spikes. The team wants to reduce restart frequency and keep GPU utilization high without changing the model. Which combination of Kubernetes configuration changes should they apply?

Accepted Answer

Set resource requests equal to limits for GPU and memory, and assign a high-priority PriorityClass to the inference pods. Evictions under node pressure are mitigated by raising pod QoS and priority. Setting requests equal to limits yields Guaranteed QoS, which protects the pods' resource allocation, and a high PriorityClass makes the kubelet evict other workloads first. Autoscalers and disruption budgets influence capacity and voluntary disruptions but do not shield the pods from pressure-driven eviction on a contended GPU node.

Answer

Configure a PodDisruptionBudget with minAvailable set to zero and enable cluster autoscaler

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Use a Vertical Pod Autoscaler in recommendation mode and set the pod restart policy to Always

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Add a Horizontal Pod Autoscaler targeting CPU utilization and increase the pod replica count

Question 13

An AI operations team is designing a rollback strategy for a fraud-detection model served behind a feature flag. A new model version shows degraded precision after release. The team wants to restore the previous behavior within minutes without redeploying code or losing the ability to collect data on the new version. Which approach best meets these requirements?

Accepted Answer

Route traffic back to the prior model version by toggling the feature flag, while continuing shadow-mode evaluation of the new version. A feature flag allows instant switching between model versions without touching application code, and shadow mode lets the suspect version keep receiving copied traffic for evaluation while customers are served by the proven model. Redeployment is too slow, threshold tuning does not restore validated behavior, and retraining cannot act within minutes while labels accumulate.

Answer

Lower the model's decision threshold so fewer transactions are flagged as fraudulent

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Keep the new model live and immediately retrain it on the most recent labeled fraud cases

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Rebuild the container image with the prior model artifact and redeploy through the CI/CD pipeline

Question 14

A hospital wants to run a diagnostic image classifier entirely inside its own data center because patient images cannot leave the premises. The IT team needs a deployment model that keeps all data and inference local while still allowing the AI team to push updated model versions. Which deployment approach fits these requirements?

Accepted Answer

Deploy the model on on-premises GPU servers and manage updates with a self-hosted model registry and serving stack.. Data residency demands that inference run where the images already reside, so on-premises GPU servers hosting the model are required. Pairing them with a self-hosted registry and serving stack preserves the ability to version and update models without sending patient data to any external environment.

Answer

Use a public cloud inference endpoint with a private VPC connection from the hospital.

Answer

Run the classifier on each radiologist's workstation using a CPU-only runtime.

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Use a serverless function in the cloud that processes images uploaded through a signed URL.

Question 15

A media company uses a large language model (LLM) to generate article summaries. They want to reduce inference costs and latency without significantly degrading summary quality. The LLM is currently served at full precision. Which optimization technique is most appropriate?

Accepted Answer

Apply quantization to convert the model weights to lower precision (e.g., INT8).. Quantization converts model weights to lower precision, reducing memory bandwidth and compute requirements, which lowers latency and cost. For LLMs, INT8 quantization often preserves summary quality well. Increasing batch size helps throughput but not per-request latency, a larger model worsens cost, and caching is ineffective for unique articles. Thus, quantization is the most appropriate optimization.

Answer

Cache all generated summaries to avoid repeated inference.

Answer

Use a larger model with more parameters to improve summary quality.

Answer

Increase the batch size for inference requests.

Question 16

An operations team is preparing to deploy a new AI inference service. Security leadership requires that all data in transit between the application and the model endpoint be encrypted and that clients be authenticated before they can submit inference requests. Which combination of controls should the team implement?

Accepted Answer

TLS for transport encryption and API keys or OAuth tokens for client authentication. The requirement has two distinct parts: confidentiality of data in transit and verification of client identity. TLS provides the former by encrypting the connection, while API keys or OAuth tokens provide the latter by proving the caller is authorized. Controls that protect stored weights, filter traffic, or restrict network ranges address other risks and do not deliver both required properties.

Answer

Network segmentation of the inference subnet and IP allowlisting of known clients

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A web application firewall and rate limiting on the inference endpoint

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AES-256 encryption of the model weights at rest and role-based access control on the model registry

Question 17

A hospital's AI team is deploying a real-time patient deterioration prediction model on bedside monitoring devices. The devices have limited RAM (512 MB) and no GPU, and the model must perform inference within 50 ms. The team has a trained TensorFlow model saved as a SavedModel. Which deployment approach best meets these constraints?

Accepted Answer

Convert the SavedModel to TensorFlow Lite and apply post-training quantization to INT8.. TensorFlow Lite is optimized for edge devices with limited compute and memory. Post-training INT8 quantization reduces the model size by up to 4x and speeds up CPU inference, directly addressing the 512 MB RAM and 50 ms latency constraints. Other options either rely on external servers or fail to optimize the model for the device's limitations.

Answer

Deploy the SavedModel using TensorFlow Serving on a central GPU server and stream patient data to it.

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Use the SavedModel directly in a Python script with TensorFlow's default runtime on the bedside device.

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Convert the model to ONNX and run it with ONNX Runtime on the bedside device without quantization.

Question 18

A junior data scientist is training a supervised classification model to predict whether a loan applicant will default. The dataset has 40,000 labeled historical records with a clear binary outcome column. The team needs a model that outputs a probability between 0 and 1 for the default class. Which algorithm is the most appropriate choice for this task?

Accepted Answer

Logistic regression. Logistic regression is designed for binary classification and directly estimates the probability that an observation belongs to the positive class through the sigmoid link function. With abundant labeled data and a clear binary target, it satisfies the supervised learning requirement and delivers interpretable, well-calibrated outputs suitable for credit risk decisions.

Answer

Principal component analysis

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Apriori association rule mining

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K-means clustering

Question 19

An AI team is preparing a fraud-detection model for production deployment. The model performs well offline, but the team must ensure the deployment is operationally safe and that problems are detected quickly after release. Which TWO practices should be implemented as part of the pre-deployment and post-deployment plan? (Choose two.)

Accepted Answer

Establish a shadow deployment that mirrors live traffic to the new model and compares its decisions with the incumbent without affecting customers.. Operational safety before release is best established by shadow deployment, which tests the model on real traffic without customer impact, and rapid detection after release comes from monitored alert thresholds tied to automated rollback. Together they cover both the pre-deployment validation gap and the post-deployment recovery path. Fixed retraining cadence, complexity increases, and documentation acknowledgments do not provide either capability.

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Increase the model's complexity by adding more layers until offline AUC reaches 0.99.

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Publish a model card and require the fraud operations team to acknowledge it before go-live.

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Retrain the model weekly on the most recent fraud data regardless of any observed performance change.

Question 20

A financial services firm is deploying a credit-scoring model that uses alternative data such as utility payments and rental history. The model shows high accuracy but the firm is concerned about regulatory compliance and explainability. The firm must provide adverse action notices to applicants who are denied credit. Which approach best satisfies the need for explainability while maintaining model performance?

Accepted Answer

Use a gradient boosting model with SHAP values to generate per-applicant reason codes for denials.. Gradient boosting with SHAP values is the best choice because SHAP provides per-applicant, locally faithful explanations that can be directly used in adverse action notices. It preserves the model's ability to leverage alternative data for accuracy while delivering the transparency regulators demand. Other options either sacrifice performance, fail to provide specific reasons, or rely on approximations that may not be reliable.

Answer

Use a black-box deep learning model and provide only the top-level feature importances from a surrogate model.

Answer

Replace the model with a simple logistic regression using only traditional credit bureau data.

Answer

Deploy the model as a black box but disclose the general factors that influence credit decisions in the application terms.