Parameters in a Dense Layer — Weight Count Calculation
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: dense layer parameters. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
The answer is 100,352. This number represents the weight parameters alone in a dense layer with 784 input features and 128 output units, calculated as 784 multiplied by 128. In a standard dense layer, each input neuron connects to every output neuron, so the total weight count equals the product of the input dimension and the number of units; biases, if included, would add 128 more parameters for a total of 100,480, but the exam’s correct answer isolates the weight count. On the CompTIA AI+ AI0-001 exam, this tests your ability to read a model summary and distinguish between weights and biases, a common trap where test-takers add biases unnecessarily. A quick memory tip: for weight count alone, think “inputs times outputs”—no plus.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
100,352
The first dense layer has 128 neurons. The parameter count is computed as input_dim * units = 784 * 128 = 100,352. This model summary excludes bias parameters, so the weight count alone is 100,352.
Key principle: Dense layer parameters
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
100,224
Why it's wrong here
100,224 is incorrect. This value corresponds to (784*128) - 128, which does not represent a standard parameter count.
✗
109,258
Why it's wrong here
109,258 is incorrect. It does not match any realistic calculation for this architecture.
✓
100,352
Why this is correct
100,352 is correct. It equals the weight parameters (784 * 128) with no biases.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Dense layer parameters
✗
8,256
Why it's wrong here
8,256 is incorrect. This value is far too small; it would require only about 10 neurons, not 128.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may add bias parameters (128) to get 100,480, but that option is not listed. The correct answer is the weight-only count (100,352) because the summary excludes biases.
Detailed technical explanation
How to think about this question
In a fully connected dense layer, each neuron computes a weighted sum of all inputs plus a bias, so the number of parameters is (input_dim * units) + units. For 784 inputs and 128 units, this equals 100,352 + 128 = 100,480. The question's correct answer of 100,352 suggests the model summary reports only the weight parameters, which can happen if the bias is disabled (use_bias=False) or if the summary separates biases into a different category. In real-world scenarios, such as deploying a model on edge devices, disabling biases can reduce parameter count and memory footprint without significant accuracy loss.
KKey Concepts to Remember
Dense layer parameters
Model summary
Input features
Bias parameters
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Dense layer parameters
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Dense layer parameters Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Review dense layer parameters, then practise related AI0-001 questions on the same topic to reinforce the concept.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Dense layer parameters.
What is the correct answer to this question?
The correct answer is: 100,352 — The first dense layer has 128 neurons. The parameter count is computed as input_dim * units = 784 * 128 = 100,352. This model summary excludes bias parameters, so the weight count alone is 100,352.
What should I do if I get this AI0-001 question wrong?
Review dense layer parameters, then practise related AI0-001 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Dense layer parameters
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