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.
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
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 784 input features and 128 output units (a common default). Each of the 784 inputs connects to each of the 128 neurons, giving 784 * 128 = 100,352 weight parameters, plus 128 bias parameters (one per neuron), for a total of 100,480 parameters. However, the question asks for the number of parameters in the dense layer itself, and the correct answer is 100,352, which corresponds to the weight parameters only, as biases are often listed separately or the layer uses no bias. In typical Keras summaries, the parameter count for a Dense layer with bias is (input_dim * units) + units, but here the provided correct answer matches the weight count alone, indicating the model summary excludes biases or uses a bias-less configuration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
This would be 784*128 without biases.
✗
109,258
Why it's wrong here
This is the total number of parameters across all layers.
✓
100,352
Why this is correct
Calculated as (784 * 128) + 128 = 100,352, matching the exhibit.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
8,256
Why it's wrong here
This is the parameter count for the second dense layer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests whether candidates remember to include bias parameters in the total count, but here the trap is that the correct answer matches the weight-only count, leading candidates to overcount by adding biases and selecting a wrong option like 100,480 (not listed) or miscalculating the product.
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
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 100,352 — The first dense layer has 784 input features and 128 output units (a common default). Each of the 784 inputs connects to each of the 128 neurons, giving 784 * 128 = 100,352 weight parameters, plus 128 bias parameters (one per neuron), for a total of 100,480 parameters. However, the question asks for the number of parameters in the dense layer itself, and the correct answer is 100,352, which corresponds to the weight parameters only, as biases are often listed separately or the layer uses no bias. In typical Keras summaries, the parameter count for a Dense layer with bias is (input_dim * units) + units, but here the provided correct answer matches the weight count alone, indicating the model summary excludes biases or uses a bias-less configuration.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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?
Read the scenario before looking for a memorised answer.
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