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AI0-001 · topic practice

Machine Learning and Deep Learning practice questions

Practise CompTIA AI+ AI0-001 Machine Learning and Deep Learning practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security
20 questionsDomain: Machine Learning and Deep Learning
Practice 10 questionsBrowse domain →

What the exam tests

What to know about Machine Learning and Deep Learning

Machine Learning and Deep Learning questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Machine Learning and Deep Learning exam traps

  • ▸Answering from memory before reading the full scenario.
  • ▸Missing a constraint such as cost, availability, security, scope or command context.
  • ▸Choosing a broad answer when the question asks for the most specific fix.
  • ▸Ignoring why the wrong options are tempting.

Practice set

Machine Learning and Deep Learning questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice
Read the full Machine Learning and Deep Learning explanation →

A team is reviewing a neural network model summary. The input layer expects 784 features (e.g., 28x28 images). How many parameters does the first dense layer have?

Exhibit

Refer to the exhibit.

```
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 128)               100352
_________________________________________________________________
dense_1 (Dense)              (None, 64)                8256
_________________________________________________________________
dense_2 (Dense)              (None, 10)                650
=================================================================
Total params: 109,258
Trainable params: 109,258
Non-trainable params: 0
_________________________________________________________________
```
Question 2easymultiple choice
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A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraud cases. Which technique is most appropriate to address the class imbalance?

Question 3hardmultiple choice
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A company deploys a deep learning model for real-time object detection in autonomous vehicles. The model was trained on high-end GPUs but needs to run on edge devices with limited computational resources. Which technique is most effective for reducing model size and inference latency while maintaining acceptable accuracy?

Question 4hardmultiple choice
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A data scientist is training a random forest model on a large dataset and notices that the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

Question 5mediummulti select
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A deep learning engineer is training a convolutional neural network for image classification. The model is overfitting the training data. Which three techniques can help reduce overfitting? (Choose three.)

Question 6hardmulti select
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A data scientist is evaluating a trained binary classification model. The model has high accuracy but the precision is low and recall is high. Which three actions are most appropriate to improve precision? (Choose three.)

Question 7mediummultiple choice
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Refer to the exhibit. A data scientist is training a neural network and observes the training log above. What is the most likely cause?

Exhibit

Training Log:
Epoch 1/50 - loss: 5.234 - acc: 0.120
Epoch 2/50 - loss: 8.910 - acc: 0.110
Epoch 3/50 - loss: 15.678 - acc: 0.095
Epoch 4/50 - loss: 25.432 - acc: 0.080
Question 8easymultiple choice
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Refer to the exhibit. An AI developer implements the above neural network architecture for handwritten digit recognition. The model achieves 85% training accuracy and 83% test accuracy. Which modification is most likely to improve training accuracy?

Exhibit

Architecture Diagram:
Input (28x28 grayscale image) -> Conv2D(32 filters, 3x3, ReLU) -> MaxPooling2D(2x2) -> Conv2D(64 filters, 3x3, ReLU) -> MaxPooling2D(2x2) -> Flatten -> Dense(128, ReLU) -> Dropout(0.5) -> Dense(10, Softmax)
Question 9easymultiple choice
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A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 80% non-churn and 20% churn. The model achieves 95% accuracy but fails to identify churners correctly. Which metric should the scientist focus on to evaluate model performance properly?

Question 10easymultiple choice
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A company uses linear regression to predict sales based on advertising spend. The model's residuals show a pattern of increasing variance as spend increases. Which assumption of linear regression is violated?

Question 11mediummultiple choice
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A healthcare organization wants to use patient data to predict disease risk. They are concerned about bias in the model. Which step is most critical during the data preparation phase to mitigate bias?

Question 12mediummultiple choice
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A team trains a random forest model on a dataset with 50 features. The model's performance on the test set is significantly worse than on the training set. Which technique is most appropriate to address this issue?

Question 13hardmultiple choice
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A deep learning model for natural language processing uses a recurrent neural network (RNN) to process long sequences. The gradients vanish after many time steps. Which architectural change is most effective to mitigate this problem?

Question 14hardmultiple choice
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An organization has a dataset with categorical features having high cardinality (e.g., ZIP codes). They plan to use a tree-based model. Which encoding method is most appropriate?

Question 15hardmultiple choice
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A company deploys a machine learning model that makes predictions on streaming data. Over time, the data distribution shifts, causing model performance to degrade. Which monitoring strategy is most appropriate to detect this drift?

Question 16easymultiple choice
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Refer to the exhibit. The training log shows losses and accuracies over 5 epochs. What is the most likely problem?

Exhibit

{
  "train_loss": [0.8, 0.6, 0.5, 0.45, 0.42],
  "val_loss": [0.9, 0.85, 0.88, 0.92, 0.95],
  "train_acc": [0.7, 0.75, 0.8, 0.82, 0.83],
  "val_acc": [0.65, 0.68, 0.67, 0.66, 0.65]
}
Question 17mediummultiple choice
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Refer to the exhibit. A developer is using the above configuration for a multi-class classification task. The model performs well on training data but poorly on validation data. Which modification could help?

Exhibit

model:
  type: sequential
  layers:
    - type: dense
      units: 128
      activation: relu
      input_shape: [784]
    - type: dropout
      rate: 0.5
    - type: dense
      units: 10
      activation: softmax
optimizer:
  type: adam
  learning_rate: 0.001
Question 18hardmultiple choice
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Refer to the exhibit. The training pod is using 2 GPUs. During training, the GPU utilization is only 30% each. What is the most likely cause?

Network Topology
command: ["python"epochs=50"batch-size=32"]apiVersion: v1kind: Podmetadata:name: ml-training-jobspec:containers:- name: trainerimage: ml/training:latestenv:- name: LEARNING_RATEvalue: "0.01"resources:limits:nvidia.com/gpu: 2priority: high
Question 19easymultiple choice
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A data scientist needs to predict whether a customer will churn based on historical data containing features like account age, monthly charges, and support tickets. The target variable is binary (churn or not). Which type of machine learning algorithm should be used?

Question 20mediummultiple choice
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A team trained a deep neural network on a limited dataset. The training loss decreases consistently, but the validation loss starts increasing after 20 epochs. What is the most likely issue and the best corrective action?

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Frequently asked questions

What does the AI0-001 exam test about Machine Learning and Deep Learning?
Machine Learning and Deep Learning questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?
Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Machine Learning and Deep Learning questions in a focused session?
Yes — the session launcher on this page draws every question from the Machine Learning and Deep Learning domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other AI0-001 topics?
Use the topic links above to move to related areas, or go back to the AI0-001 question bank to see all topics.
Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the AI0-001 exam covers. They are not copied from any real exam or dump site.