AI0-001 Practice Test 10 — 10 Questions

Question 1

A real-time recommendation system requires low latency. Which data storage strategy is best for serving user profiles and item embeddings?

Accepted Answer

In-memory key-value store (e.g., Redis). An in-memory key-value store like Redis is ideal for serving user profiles and item embeddings in a real-time recommendation system because it provides sub-millisecond read/write latency by keeping data in RAM. This directly meets the low-latency requirement for fetching embeddings and profiles on each request, without the overhead of disk I/O or complex query processing.

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Time-series database (e.g., InfluxDB)

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Relational database with joins

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Data lake in object storage (e.g., Amazon S3)

Question 2

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. OOMKilled indicates the container exceeded its memory limit. The resource requests/limits likely were not adjusted for the new cluster configuration, or the node's allocatable memory decreased after upgrade.

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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 3

Refer to the exhibit. An AI auditor reviews the fairness configuration. What is the purpose of this policy?

Accepted Answer

Ensure equal positive prediction rates across groups. The policy sets a fairness constraint that requires the model's positive prediction rate (the fraction of instances predicted as the positive class) to be equal across all defined groups. This is a standard demographic parity requirement, which is implemented by adjusting the decision threshold or reweighting training data to ensure that each group receives the same proportion of positive predictions, regardless of the actual outcome distribution.

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Ensure equal error rates across groups

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Ensure equal accuracy across groups

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Ensure model interpretability

Question 4

A company operating in the EU must comply with GDPR. An AI model processes personal data for customer segmentation. Which of the following ensures compliance?

Accepted Answer

Implement data anonymization and allow users to request deletion.. GDPR mandates that personal data must be processed lawfully, with data minimization and the right to erasure. Implementing data anonymization removes personally identifiable information (PII) so the data is no longer considered personal data under GDPR, and allowing users to request deletion directly satisfies the 'right to be forgotten' (Article 17). This approach ensures compliance by both protecting individual privacy and providing a mechanism for data subjects to exercise their legal rights.

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Obtain explicit consent once and use data indefinitely.

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Store personal data permanently for model improvement.

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Use only aggregated data without any individual records.

Question 5

An AI system that can perform any intellectual task that a human being can is referred to as:

Accepted Answer

Artificial General Intelligence (AGI). Artificial General Intelligence (AGI) is the concept of a machine that understands or learns any intellectual task that a human being can.

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Machine learning

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Narrow AI

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Deep learning

Question 6

Based on the exhibit, what issue should the team address?

Accepted Answer

Potential fairness bias across groups. The exhibit likely shows a confusion matrix or performance metrics broken down by demographic groups (e.g., race, gender), revealing that the model's false positive or false negative rates differ significantly across groups. This disparity indicates a potential fairness bias, which must be addressed to ensure equitable outcomes, especially in high-stakes AI applications like hiring or lending.

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Model accuracy below threshold

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High latency

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Low throughput

Question 7

An organization wants to implement an AI system to automatically categorize support tickets into predefined categories. They have a labeled dataset of 10,000 tickets. Which approach is MOST appropriate?

Accepted Answer

Fine-tune a pre-trained text classification model. Fine-tuning a pre-trained text classification model is a standard and effective approach for supervised classification when labeled data is available.

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Use a rule-based system with keyword matching

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Use a prompt-based LLM with few-shot examples

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Train a custom neural network from scratch

Question 8

An AI developer is selecting a model architecture for a real-time video surveillance system that must detect objects in each frame and also track movement patterns across frames. Which TWO architectures should the developer combine? (Choose 2)

Accepted Answer

Recurrent neural network (RNN) or LSTM. CNNs are ideal for image feature extraction; RNNs/LSTMs are designed for sequence modelling to track temporal patterns.

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Transformer encoder only

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Generative adversarial network (GAN)

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Variational autoencoder (VAE)

Question 9

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

Question 10

A company trains a large language model on a dataset that includes copyrighted books. Under current legal interpretations, which statement about copyright infringement is MOST accurate?

Accepted Answer

Training on copyrighted data without permission likely infringes copyright, though fair use may be a defense.. Training on copyrighted works without permission is generally considered copyright infringement, unless a specific exception applies (e.g., fair use in the US). Fair use is determined on a case-by-case basis and is not automatically granted. Using only public domain works avoids infringement. The EU AI Act does not provide blanket permission.

Answer

Training on copyrighted data is generally permissible under the EU AI Act.

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Training on copyrighted data is always covered by fair use in the US.

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Training on copyrighted data is allowed as long as the model is not used commercially.