AI0-001 Practice Test 46 — 15 Questions

Question 1

Refer to the exhibit. A data engineer notices that the batch processing step is taking too long and causing delays. Which change would most likely reduce the latency?

Accepted Answer

Move feature engineering to the stream processing step in Flink. Moving feature engineering from the batch Spark job to the stream processing Flink job reduces the workload on the batch step, making it faster. Replacing Flink, increasing parallelism, or changing output format do not address the bottleneck as effectively.

Answer

Increase the parallelism of the Spark job

Answer

Replace Apache Flink with Apache Storm for stream processing

Answer

Change the output format from Parquet to CSV

Question 2

A company deploys an AI model via a REST API that handles sensitive customer data. To secure the endpoint, the security team requires that only authenticated and authorized applications can invoke the API. Which mechanism should be implemented?

Accepted Answer

API key or bearer token in the HTTP header. API keys or bearer tokens (e.g., OAuth 2.0 access tokens) are the standard mechanism for authenticating and authorizing client applications when invoking a REST API. These tokens are passed in the HTTP Authorization header, allowing the server to verify the client's identity and permissions before processing requests containing sensitive customer data.

Answer

TLS encryption for the connection

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Input sanitization to prevent injection

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IP whitelisting

Question 3

A data scientist is preparing a dataset for training a classification model. The dataset contains 10,000 records with a binary target variable where 9,500 belong to class A and 500 belong to class B. Which technique should the scientist use to address the class imbalance?

Accepted Answer

SMOTE (Synthetic Minority Oversampling Technique). SMOTE is the correct technique because it generates synthetic samples for the minority class (class B) by interpolating between existing minority instances, effectively balancing the dataset without losing information. This approach avoids the overfitting risk of simple oversampling and the information loss of undersampling, making it ideal for a 19:1 imbalance ratio.

Answer

Random undersampling of class A

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Adding Gaussian noise to class B

Answer

Principal Component Analysis (PCA)

Question 4

A security analyst at a fintech company is alerted to anomalous API requests to their deployed LLM chatbot. The requests contain carefully crafted inputs that cause the model to generate responses that include internal system prompts. Which type of attack is MOST likely occurring?

Accepted Answer

Prompt leaking via indirect prompt injection. The attack exploits the LLM's inability to distinguish between user-supplied instructions and system-level prompts. By crafting inputs that include hidden or indirect instructions, the attacker causes the model to output its internal system prompt, which is a classic prompt leaking scenario achieved via indirect prompt injection.

Answer

Data poisoning attack

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Membership inference attack

Answer

Model inversion attack

Question 5

An AI team notices that their model's performance degrades over time because the statistical relationship between input features and the target variable changes. This issue is called:

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target variable changes over time, causing model performance to degrade. This is distinct from data drift, which involves changes in the input data distribution alone. In the AI0-001 context, concept drift directly addresses the shift in the underlying mapping from features to labels.

Answer

Data drift

Answer

Overfitting

Answer

Model drift

Question 6

A healthcare company must deploy a diagnostic AI model that uses protected health information (PHI). To comply with HIPAA, the operations team needs to ensure data privacy during model inference. Which practice should be implemented?

Accepted Answer

Encrypt all PHI at rest and in transit within the inference pipeline. HIPAA mandates encryption of protected health information (PHI) both at rest and in transit to safeguard data confidentiality during model inference. Encrypting the entire inference pipeline ensures that even if data is intercepted or accessed without authorization, it remains unreadable. This practice directly addresses the compliance requirement for data privacy without relying on network location or partial obfuscation.

Answer

Run the model on-premises to avoid cloud data transmission

Answer

Mask sensitive fields in the input data before inference

Answer

Apply differential privacy during model training only

Question 7

A company is using Google Cloud Vertex AI for model training. They want to automate the retraining pipeline when new data arrives in BigQuery. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the correct choice because it enables you to define, automate, and orchestrate end-to-end ML workflows, including retraining models when new data arrives. By integrating with BigQuery triggers or Cloud Scheduler, you can set up a pipeline that automatically ingests new data, preprocesses it, retrains the model, and deploys the updated version—all without manual intervention.

Answer

Vertex AI Prediction

Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

Question 8

A company uses an AI system to screen job applicants. Under the GDPR, if the system makes automated decisions that have a legal or similarly significant effect on individuals, the data subject has the right to obtain an explanation of the decision. What is this right commonly called?

Accepted Answer

Right to explanation. The GDPR's right to explanation is not explicitly named in the regulation but is widely referred to as such in the context of automated decision-making under Article 22. The other options are other data subject rights under GDPR but do not specifically address explanation of decisions.

Answer

Right to erasure (right to be forgotten)

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Right to object

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Right to data portability

Question 9

Which TWO of the following are key stages in the AI lifecycle?

Accepted Answer

Model deployment. Model deployment (C) is a key stage in the AI lifecycle because it involves integrating the trained model into a production environment where it can make predictions on live data. This stage is critical for operationalizing AI and delivering business value, following the model training and validation phases.

Answer

Human annotation of all data

Answer

Model retraining

Answer

Manual feature extraction

Question 10

A company deployed an AI chatbot that started generating offensive responses after a data update. The security team needs to quickly mitigate the issue. What should they do first?

Accepted Answer

Disable the chatbot and investigate. The first priority when an AI chatbot generates offensive responses is to stop the harm immediately. Disabling the chatbot (Option B) halts all user interactions, preventing further offensive outputs while the security team investigates the root cause. This aligns with the principle of containment before remediation in incident response.

Answer

Delete the training data

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Roll back to previous model version

Answer

Add a content filter

Question 11

An AI system used for hiring is found to have a disparate impact on a protected group. What is the first step in addressing this under the NIST AI RMF?

Accepted Answer

Map - understand the use case and potential impacts. The NIST AI RMF follows a sequence of functions: Govern, Map, Measure, and Manage. When a disparate impact is discovered, the first step is to Map the context, which involves understanding the specific use case, the AI system's operational design, and the potential sources of bias. Without this foundational understanding, any subsequent governance, measurement, or mitigation efforts would be misdirected or ineffective.

Answer

Govern - establish governance policies

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Manage - implement mitigation strategies

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Measure - evaluate the fairness metrics

Question 12

A data engineer needs to process streaming clickstream data for real-time feature engineering in an ML pipeline. Which data pipeline technology is BEST suited for this task?

Accepted Answer

Apache Kafka. Apache Kafka is the best choice because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion and processing. It can capture clickstream events as they occur and make them immediately available for feature engineering in an ML pipeline, supporting exactly-once semantics and low-latency delivery.

Answer

Apache Spark in batch mode

Answer

Snowflake

Answer

Apache Airflow

Question 13

An AIOps platform monitors server metrics and triggers alerts. The team notices too many false positives. Which adjustment should be made to the anomaly detection model?

Accepted Answer

Raise the anomaly score threshold for triggering alerts.. Raising the anomaly score threshold (Option D) directly reduces false positives by requiring a higher deviation from normal behavior before an alert is triggered. In AIOps platforms, the anomaly score is a numeric value (e.g., 0–100) that quantifies how unusual a metric is; a higher threshold means only more extreme deviations generate alerts, filtering out minor fluctuations that were incorrectly flagged.

Answer

Use a more complex model to better fit the data.

Answer

Shorten the observation window to detect anomalies faster.

Answer

Increase the training data to include more normal patterns.

Question 14

A healthcare AI system used for diagnosis shows a significant accuracy difference between demographic groups. Which technique should be applied to directly reduce this bias during model training?

Accepted Answer

Apply adversarial debiasing during training. Adversarial debiasing directly reduces bias during model training by introducing an adversarial network that attempts to predict the protected attribute (e.g., demographic group) from the model's predictions. The primary model is trained to maximize accuracy while simultaneously minimizing the adversary's ability to infer the protected attribute, thereby forcing the model to learn representations that are invariant to that attribute. This technique directly addresses the accuracy disparity by encoding fairness as an optimization objective, unlike post-hoc or data-level approaches.

Answer

Ignore the disparity as long as overall accuracy is acceptable

Answer

Retrain the model with more data from the underperforming group

Answer

Remove demographic attributes from the training data

Question 15

Refer to the exhibit. A data scientist reviews the pipeline and notes that the model performance degraded. Which change to the pipeline would most likely improve model performance?

Accepted Answer

Change the impute strategy from mean to median for the 'income' column.. The 'income' column likely contains outliers that skew the mean, making mean imputation sensitive to extreme values. Using median imputation is more robust to outliers, preserving the central tendency of the data and reducing bias introduced during preprocessing. This directly addresses the model performance degradation caused by distorted feature distributions.

Answer

Remove the normalization step entirely.

Answer

Drop the 'product_category' column instead of one-hot encoding.

Answer

Change the encoding method from onehot to label encoding.