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Fundamentals of AI and ML practice questions

Practise AWS Certified AI Practitioner AIF-C01 Fundamentals of AI and ML practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

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Reviewed byJohnson Ajibi· MSc IT Security
20 questionsDomain: Fundamentals of AI and ML
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What the exam tests

What to know about Fundamentals of AI and ML

Fundamentals of AI and ML 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 Fundamentals of AI and ML 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

Fundamentals of AI and ML questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice
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A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?

Question 2mediummultiple choice
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An ML team is deploying a real-time inference endpoint for a computer vision model using Amazon SageMaker. The model requires GPU acceleration for low latency. Which instance type should the team choose to minimize cost while meeting the GPU requirement?

Question 3hardmultiple choice
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A company is training a deep learning model on Amazon SageMaker using a custom Docker container. The training job fails with the error 'CannotStartContainerError: API error (500): failed to create shim task'. The team verifies that the container image is compatible with the selected instance type. What is the most likely cause of this error?

Question 4mediummultiple choice
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A machine learning engineer is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for 10 consecutive epochs. Which SageMaker feature should be used?

Question 5easymultiple choice
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A company needs to store large amounts of unstructured training data (images, videos) in a cost-effective manner while ensuring low-latency retrieval for training jobs running on Amazon SageMaker. Which storage solution should be used?

Question 6mediummultiple choice
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An organization wants to detect anomalies in real-time streaming data from IoT devices. The data includes sensor readings, and the team plans to use a machine learning model. Which AWS service should be used to build and deploy the model with minimal operational overhead?

Question 7hardmultiple choice
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During a SageMaker training job, the data scientist observes that the loss is not decreasing after the initial few epochs. The model is a deep neural network with ReLU activations. Which hyperparameter adjustment is most likely to help?

Question 8easymulti select
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Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)

Question 9mediummulti select
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Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Question 10hardmulti select
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Which TWO factors should be considered when choosing between a CPU-based instance and a GPU-based instance for training a machine learning model on Amazon SageMaker? (Choose two.)

Question 11mediummultiple choice
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Refer to the exhibit. A data scientist attaches the above IAM policy to a SageMaker notebook instance role. The notebook is in the same AWS account as the S3 bucket. When trying to read a file from 's3://my-bucket/training/data.csv', the data scientist gets an Access Denied error. What is the most likely cause?

Exhibit

Refer to the exhibit.

```json
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/training/*"
    }
  ]
}
```
Question 12hardmultiple choice
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Refer to the exhibit. A data scientist is training a neural network model on SageMaker. The training log shows the loss values per epoch. Which issue is most likely occurring?

Exhibit

Refer to the exhibit.

```
2023-09-15 10:15:30,123 INFO     - Training job started
2023-09-15 10:15:35,456 INFO     - Epoch 1/10: loss=2.3456, accuracy=0.1234
2023-09-15 10:15:40,789 INFO     - Epoch 2/10: loss=2.3001, accuracy=0.1300
2023-09-15 10:15:46,012 INFO     - Epoch 3/10: loss=2.2800, accuracy=0.1350
2023-09-15 10:15:51,234 INFO     - Epoch 4/10: loss=2.3100, accuracy=0.1280
2023-09-15 10:15:56,456 WARNING - Loss increased from 2.2800 to 2.3100
2023-09-15 10:16:01,678 INFO     - Epoch 5/10: loss=2.3500, accuracy=0.1200
```
Question 13mediummultiple choice
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A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 records with 9,500 non-churners and 500 churners. After training a logistic regression model, the model achieves 95% accuracy on the test set. However, the business team reports that the model is not useful because it predicts almost all customers as non-churners. Which metric should the data scientist use to evaluate the model's performance in this scenario?

Question 14hardmultiple choice
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A company is deploying a machine learning model for real-time fraud detection. The model must make predictions with latency under 10 milliseconds. The data scientist trained a gradient boosting model that achieves high accuracy but has inference latency of 50 milliseconds. The team has access to a larger instance type with more CPU cores. Which approach should the data scientist take to reduce inference latency while maintaining accuracy?

Question 15easymultiple choice
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A company wants to build a system that automatically categorizes customer support tickets into predefined categories (e.g., billing, technical, account). The team has a large dataset of historical tickets with their category labels. Which type of machine learning problem is this?

Question 16mediummultiple choice
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A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long. The dataset consists of 100,000 images stored in Amazon S3. Which action can the data scientist take to reduce training time without modifying the model architecture?

Question 17hardmulti select
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Which TWO of the following are best practices for preparing training data for a machine learning model?

Question 18hardmultiple choice
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A financial services company uses a machine learning model to approve loan applications. The model is a gradient boosting classifier trained on historical loan data. Recently, the company noticed that the model's approval rate for applicants from a certain demographic group is significantly lower than for other groups, even though the model's overall accuracy remains high. The data science team has been asked to address this potential bias while minimizing the impact on overall model performance. The team has access to the training data and the trained model. They have limited time and budget. Which course of action should the team take first?

Question 19mediummultiple choice
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A retail company uses a machine learning model to forecast daily product demand. The model is a time series model that uses historical sales data. The model has been performing well, but recently the forecasts have been consistently too low, leading to stockouts. The data scientist notices that the model was trained on data up to last year, and the company has since launched a successful marketing campaign that increased sales by 20%. The data scientist needs to update the model to reflect the new sales patterns. Which approach should the data scientist take?

Question 20easymultiple choice
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A company wants to use AI to automatically transcribe customer service calls into text. Which AWS service is most suitable?

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Frequently asked questions

What does the AIF-C01 exam test about Fundamentals of AI and ML?
Fundamentals of AI and ML 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 Fundamentals of AI and ML questions in a focused session?
Yes — the session launcher on this page draws every question from the Fundamentals of AI and ML domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
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Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the AIF-C01 exam covers. They are not copied from any real exam or dump site.