AI0-001 Practice Test 22 — 15 Questions

Question 1

A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 1% fraud cases (minority class) and 99% non-fraud cases. Which data preparation technique is MOST appropriate to address the class imbalance before training?

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

Accepted Answer

Apply SMOTE (Synthetic Minority Over-sampling Technique) to the minority class. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples of the minority class by interpolating between existing minority instances, which addresses class imbalance without simply duplicating data. This reduces overfitting compared to random oversampling and is the most appropriate technique for a 1% fraud detection dataset.

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Duplicate the minority class samples until the class ratio is 50:50

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Normalize all features to a range of 0 to 1

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Random undersample the majority class to match the minority class size

Question 2

An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?

Accepted Answer

The training data does not represent the new hospital's population or imaging equipment.. The model's high accuracy on the test set but poor performance in a different hospital indicates a distribution shift between the training data and the deployment environment. This is a classic case of dataset shift, where the training data does not represent the new hospital's patient population or imaging equipment, leading to degraded model generalization.

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The model is being attacked by adversarial examples.

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The model is overfitted to the training data.

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Data leakage occurred during preprocessing.

Question 3

A data engineer is designing a pipeline for a streaming data application that uses a machine learning model to detect anomalies in real time. Which TWO practices should the engineer implement to ensure data quality and model reliability?

Accepted Answer

Use a sliding window for feature computation. Option C is correct because a sliding window computes features over a continuously advancing time range, which is essential for real-time anomaly detection since it captures recent, temporally relevant data points while maintaining feature freshness as the stream evolves. Option D is correct because implementing data validation checks at the ingestion point catches malformed, missing, or out-of-range records before they reach the model, preventing garbage-in-garbage-out failures and preserving both data quality and model reliability in a streaming pipeline. Option A is not appropriate because batch processing in fixed intervals introduces latency and defeats the real-time requirement of the streaming application. Option B is not required for data quality or model reliability; storing all raw data indefinitely is a retention/archival decision and does not itself validate or improve streaming data. Option E is not ideal because a fixed 24-hour retraining schedule cannot adapt to concept drift or anomalies that emerge within the stream, and retraining cadence should be driven by drift detection rather than an arbitrary fixed interval.

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Use batch processing to transform data in fixed intervals

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Store all raw data indefinitely for future analysis

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Retrain the model on a fixed schedule every 24 hours

Question 4

A machine learning engineer notices that a linear regression model has high bias. Which action is most likely to reduce bias?

Accepted Answer

Use a more complex model, such as polynomial regression. High bias indicates that the model is too simple to capture the underlying patterns in the data, leading to underfitting. Using a more complex model, such as polynomial regression, increases the model's capacity to fit the training data better, directly addressing the underfitting issue. This is the standard approach to reduce bias in machine learning.

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Reduce the number of training samples

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Add L2 regularization

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Apply feature scaling

Question 5

A team is deploying an AI microservice for real-time object detection in streaming video. Which TWO integration patterns are most appropriate? (Choose two.)

Accepted Answer

Streaming responses for real-time inference. Option A (Streaming responses for real-time inference) is correct because object detection on live video requires continuous, low-latency output as frames arrive, and streaming responses (e.g., gRPC server-streaming or HTTP chunked/SSE) let the service emit detection results incrementally instead of waiting for a full batch to complete. Option E (AI microservice architecture) is correct because packaging the detection model as an independently deployable microservice allows separate scaling, GPU resource allocation, and model versioning without affecting the rest of the streaming pipeline. Option B (Batch processing with nightly jobs) is wrong because nightly jobs introduce hours of latency, which is incompatible with real-time video analytics. Option C (Synchronous request-response with long timeouts) is wrong because long timeouts block callers and cannot sustain the continuous frame-by-frame throughput that streaming video demands. Option D (Monolithic application deployment) is wrong because a monolith couples the detection workload to unrelated components, preventing independent scaling and rapid model updates needed for real-time inference.

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Batch processing with nightly jobs

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Synchronous request-response with long timeouts

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Monolithic application deployment

Question 6

A company needs to store large volumes of unstructured data (PDFs, images, logs) for future AI model training. The data must be easily accessible by data scientists using Spark and must support cost-effective storage. Which data infrastructure is MOST appropriate?

Accepted Answer

Amazon S3 data lake. Amazon S3 is the canonical data lake storage layer for large volumes of unstructured data such as PDFs, images, and logs, and it integrates natively with Spark via the S3A connector and with AWS Glue, EMR, and Athena. Its object storage model, tiered storage classes (Standard, IA, Glacier), and pay-for-what-you-use pricing make it cost-effective for petabyte-scale AI training corpora. This combination of scalability, accessibility, and cost is exactly what the scenario requires.

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Snowflake data warehouse

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Relational database like Amazon RDS

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Pinecone vector database

Question 7

During model deployment, a data engineer notices that the model's predictions are consistently lower than expected due to a shift in the distribution of one feature between training and production. Which technique should be used to detect and quantify this shift?

Accepted Answer

Calculate the population stability index (PSI). The Population Stability Index (PSI) is specifically designed to detect and quantify shifts in the distribution of a feature or score between two populations, such as training and production datasets. It measures the stability of the feature by comparing the proportion of observations in each bin across the two time periods, making it the correct choice for diagnosing distribution drift in model deployment.

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Compute the root mean square error (RMSE)

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Generate a confusion matrix

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Perform a t-test on the means

Question 8

An ML team uses Kubeflow to orchestrate a pipeline that includes data preprocessing, model training, and evaluation. The pipeline runs on a Kubernetes cluster. After a cluster upgrade, the pipeline fails at the training step with an 'OOMKilled' error. What is the MOST likely cause?

Accepted Answer

The Kubernetes node's memory resources were not correctly allocated to the pod's resource requests or limits. Kubeflow pipelines run each step as a Kubernetes pod, and the OOMKilled status is a Kubernetes-level signal that the container exceeded its memory limit (or the node ran out of allocatable memory). After a cluster upgrade, node instance types, kubelet reservations, or resource quotas often change, so the pod's original requests/limits no longer match available memory. The error appears specifically at the training step because that step has the largest memory footprint, making it the first to be evicted or killed.

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The training code has a memory leak

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The pipeline definition is missing a step dependency

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The training data is corrupted

Question 9

A large e-commerce company uses a recommendation engine trained on millions of user interactions. Recently, the marketing team noticed a sharp increase in click-through rates for a particular product category. Upon investigation, an engineer found that a competitor had injected fake user profiles that consistently clicked on their products, skewing the training data. The company needs to remediate the attack and prevent future occurrences. The team has limited time and budget. Which course of action should the company take first?

Accepted Answer

Identify and remove the fake user profiles from the training dataset, then retrain the model. The immediate priority is to remove the poisoned data from the training set and retrain the model, as the fake profiles are actively skewing predictions and causing incorrect click-through rate spikes. This direct remediation addresses the root cause with minimal time and budget, aligning with the team's constraints. Without cleaning the data, any further training or defensive measures would still operate on corrupted inputs.

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Implement adversarial training to make the model robust to future poisoning attempts

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Decrease the frequency of model retraining to limit exposure to new data

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Add differential privacy noise to the training data to mask the injected profiles

Question 10

A company is implementing a retrieval-augmented generation (RAG) pipeline using a vector database. They notice that the retrieved documents often lack relevance to the query. Which adjustment would MOST improve retrieval quality?

Accepted Answer

Use a better embedding model fine-tuned on domain-specific data. Retrieval quality in a RAG pipeline is fundamentally determined by the semantic alignment between query embeddings and document embeddings. A domain-specific fine-tuned embedding model captures the unique terminology, context, and relationships within the company's data, producing vector representations that are far more relevant than those from a generic model. This directly improves the similarity search results in the vector database, leading to higher-quality retrieved documents.

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Increase the chunk size of documents

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Switch from cosine similarity to Euclidean distance

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Reduce the number of retrieved documents from 5 to 3

Question 11

Refer to the exhibit. The training log shows loss and accuracy for a binary classification model. What is the most likely issue with this model?

Accepted Answer

Overfitting. The training log shows that training loss continues to decrease while validation loss increases after a certain point, and training accuracy approaches 100% while validation accuracy plateaus or drops. This divergence is the classic signature of overfitting, where the model memorizes noise in the training data rather than learning generalizable patterns.

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Insufficient epochs

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Underfitting

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Data leakage

Question 12

A data engineer is building a pipeline to process streaming clickstream data and feed it into a real-time ML feature store. Which tool is BEST suited for the streaming ingestion?

Accepted Answer

Apache Kafka. Apache Kafka is the best tool for streaming ingestion because it is a distributed event streaming platform designed for high-throughput, low-latency ingestion of real-time data streams. It can handle clickstream data and feed it into a feature store with minimal delay, supporting exactly-once semantics and scalability.

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Amazon S3

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Apache Airflow

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Apache Spark (batch mode)

Question 13

An organization deploys an AI system that processes personal data of EU citizens. Which regulatory framework imposes strict requirements on automated decision-making and profiling?

Accepted Answer

General Data Protection Regulation (GDPR). The General Data Protection Regulation (GDPR) is the correct regulatory framework because it specifically governs the processing of personal data of EU citizens and imposes strict requirements on automated decision-making and profiling under Article 22. This article grants individuals the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. The GDPR also mandates data protection impact assessments and transparency obligations for such AI-driven processing.

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Payment Card Industry Data Security Standard (PCI DSS)

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Health Insurance Portability and Accountability Act (HIPAA)

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Sarbanes-Oxley Act (SOX)

Question 14

A team is training a neural network for image classification. They observe that training loss decreases steadily but validation loss starts increasing after 20 epochs. What is the most likely issue?

Accepted Answer

Overfitting. The training loss decreasing while validation loss increases after 20 epochs is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.

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Underfitting

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Vanishing gradients

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Data leakage

Question 15

A team is implementing a machine learning pipeline to classify images for a defect detection system. They are considering using a pre-trained convolutional neural network (CNN) and fine-tuning it on their small dataset. What is the primary advantage of transfer learning in this scenario?

Accepted Answer

It allows the model to leverage learned features from a large dataset, reducing training time and required data. Transfer learning allows the team to start with a pre-trained CNN (e.g., trained on ImageNet) that has already learned general features like edges, textures, and shapes from a massive dataset. By fine-tuning only the later layers on their small defect dataset, they dramatically reduce training time and the amount of labeled data needed, while still achieving high accuracy.

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It ensures the model is not biased toward the original dataset

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It eliminates the need for data preprocessing

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It reduces the risk of overfitting by using a larger model