AI0-001 Practice Test 10 — 10 Questions

Question 1

A data engineer is designing a pipeline to train a linear regression model on a dataset with 10 million rows and 50 features. The dataset fits in memory. Which approach should the engineer use to train the model efficiently?

Accepted Answer

Stochastic gradient descent. Stochastic gradient descent (SGD) is the most efficient approach for training a linear regression model on a dataset with 10 million rows and 50 features because it updates the model parameters using only one training example per iteration, leading to much faster convergence per epoch compared to batch methods. Since the dataset fits in memory, SGD can still be implemented efficiently without the overhead of loading data in batches from disk, and it scales well to large datasets where the normal equation or batch gradient descent would be computationally prohibitive.

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Normal equation

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Batch gradient descent

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Principal component analysis

Question 2

Which THREE are common activation functions used in neural networks? (Choose THREE.)

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a common activation function in neural networks because it introduces non-linearity while being computationally efficient. It outputs the input directly if positive, otherwise zero, which helps mitigate the vanishing gradient problem compared to sigmoid or tanh. This makes it a default choice for hidden layers in many deep learning architectures.

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Softmax

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Linear

Question 3

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

Accepted Answer

Overfitting. The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.

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Underfitting

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Data leakage

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Vanishing gradient

Question 4

A team is training a deep learning model for image classification. The training loss decreases rapidly but validation loss starts increasing after a few epochs. Which regularization technique should be applied to mitigate this issue?

Accepted Answer

Early stopping. Option C is correct because early stopping halts training when validation loss increases, preventing overfitting. Option A is wrong because L2 regularization penalizes large weights but doesn't stop training. Option B is wrong because dropout randomly drops neurons during training, but early stopping directly addresses the symptom. Option D is wrong because data augmentation increases data diversity, but the issue is overfitting due to training too long.

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Data augmentation

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L2 regularization

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Dropout

Question 5

An organization needs to store sensitive customer data for training a machine learning model. The data must be encrypted at rest and in transit, and access must be audited. Which combination of practices should be implemented?

Accepted Answer

Use TLS for transfer, AES-256 for storage, and AWS CloudTrail for auditing. Option A is correct because it combines TLS (Transport Layer Security) for encrypting data in transit, AES-256 for strong encryption at rest, and AWS CloudTrail for auditing API-level access. TLS ensures confidentiality and integrity during transmission, AES-256 provides robust symmetric encryption for stored data, and CloudTrail logs all AWS API calls for compliance and audit trails. This triad satisfies the requirements of encryption in transit, at rest, and audited access.

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Use FTP for transfer, AES-128 for storage, and manual log review

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Use SSH for transfer, store data in a database, and enable access logs

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Use MD5 for hashing, store data in plaintext, and enable server logs

Question 6

A data scientist is training a deep learning model for image classification. The training loss decreases steadily but the validation loss starts increasing after 10 epochs. Which technique should the scientist apply to address this issue?

Accepted Answer

Implement early stopping. The scenario describes overfitting: the model memorizes training data (loss decreases) but fails to generalize to unseen validation data (validation loss increases). Early stopping (Option C) halts training when validation performance degrades, preventing overfitting while preserving the best model weights. This is a standard regularization technique in deep learning frameworks like TensorFlow and PyTorch.

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Add more dropout layers

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Reduce the learning rate

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Increase the number of training epochs

Question 7

A data engineer is reviewing an S3 bucket policy for a machine learning project. The policy is intended to allow access to training data only from the corporate network (10.0.0.0/16). However, users in the corporate network report access denied. Which issue is most likely causing the problem?

Accepted Answer

The policy does not include s3:ListBucket action, which is needed to list objects.. Option C is correct. The condition key uses "aws:SourceIp" but the correct key for IP address condition is "aws:SourceIp". However, the exhibit shows "aws:SourceIp" which is correct? Wait, the exhibit shows "aws:SourceIp" — that is correct. But the problem might be that the policy uses "GetObject" but the users might be trying to list objects (s3:ListBucket). Option C says the action does not include s3:ListBucket, which is often needed first. Option A is wrong because the resource is correct. Option B is wrong because the policy allows access from 10.0.0.0/16. Option D is wrong because the effect is Allow.

Answer

The policy is missing a Deny statement.

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The resource ARN is incorrect.

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The IP address condition is not matching because the corporate network uses a different CIDR.

Question 8

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 streaming anomaly detection requires real-time feature computation over recent data, and a sliding window ensures that only the most relevant data points are used for model inference, maintaining low latency and adapting to concept drift. This approach avoids the staleness of batch processing and aligns with the continuous nature of streaming pipelines.

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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 9

A DevOps team is deploying a machine learning model using a CI/CD pipeline. They want to ensure the model is reproducible and traceable. Which TWO practices should they implement?

Accepted Answer

Version the training dataset and code using Git and DVC.. Option A and D are correct because versioning data and code, and using containers ensure reproducibility. Option B is wrong because manual deployment is error-prone and not traceable. Option C is wrong as it does not track the model. Option E is wrong because manual tagging is inconsistent.

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Manually deploy the model to production after approval.

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Store only the final model artifact in a shared drive.

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Use a spreadsheet to record model version numbers.

Question 10

An organization is implementing an AI governance framework. Which THREE components are essential for compliance with ethical AI standards?

Accepted Answer

Data privacy protection measures (e.g., differential privacy).. Options A, B, and D are correct because bias auditing, explainability, and privacy protection are core ethical requirements. Option C is wrong because maximizing revenue is a business goal, not an ethical requirement. Option E is wrong because open-source is not a mandatory component.

Answer

Open-source licensing of all models.

Answer

Maximizing model accuracy to increase revenue.