AWS Certified Machine Learning – Specialty (MLS-C01) retired on 31 March 2026. These questions remain useful AWS machine-learning study material, but they no longer map to a bookable exam. Candidates certifying today should look at AWS Certified Machine Learning Engineer – Associate or AWS Certified Generative AI Developer – Professional.
8 of the 46 questions in this set, with the correct answer marked and explained.
1. Of the following, which is an example of machine learning? (Select TWO.)
✓Optimizing product pricing based on real-time sales data
Optimizing product pricing based on real-time sales data and Sentiment analysis of text on product reviews
✗Calculating the shortest route from current location to the destination
✓Sentiment analysis of text on product reviews
✗A loan approval system that classifies applicants entirely based on credit score
2. A Data Scientist at a Djamgatech retail company is using Amazon SageMaker to classify social media posts that mention the company into one of two categories: Posts that require a response from the company, and posts that do not. The Data Scientist is using a training dataset of 10,000 posts, each of which contain the timestamp, author, and full text of each post. However, the Data Scientist is missing the target labels that are required for training. Which approach can the Data Scientist take to create valid target label data? (Select TWO.)
✓Ask the social media handling team to review each post using Amazon SageMaker GroundTruth and provide the label
Use Amazon Mechanical Turk to publish Human Intelligence Tasks that ask Turk workers to label the posts. Review each post using Amazon SageMaker GroundTruth and provide the labe
✗Use the a priori probability distribution of the two classes. Then, use Monte-Carlo simulation to generate the labels
✗Use the sentiment analysis natural language processing library to determine whether a post requires a response
✓Use Amazon Mechanical Turk to publish Human Intelligence Tasks that ask Turk workers to label the posts
✗Use K-Means to cluster posts into various groups, and pick the most frequent word in each group as its label
3. An ML Engineer at a Djamgatech real estate startup wants to use a new quantitative feature for an existing ML model that predicts housing prices. Before adding the feature to the cleaned dataset, the Engineer wants to visualize the feature in order to check for outliers and overall distribution and skewness of the feature. What visualization technique should the ML Engineer use? (Select TWO.)
✗Scatterplot
✓Histogram
Box Plot and Histogram
✓Box Plot
✗T-SNE
✗Heatmap
4. A Djamgatech startup is building an image classification model. During the process, they copied over some of their validation data into their training examples, creating duplicate values in the training and validation subsets. Which is a possible result of taking this approach? (Select TWO.)
✓The model may perform worse with the test dataset than with the validation dataset
This could lead to overfitting the model
✗This is a good way to increase the training dataset size and therefore strengthen the model's ability to generalize
✓This could lead to overfitting the model
✗This is a common practice in machine learning and will improve the overall performance of the model
5. A data scientist wants to visualize the correlation between features in their dataset. What tool(s) can they use to visualize this in a correlation matrix? (Select TWO)
✗Scikitlearn
✓Matplotlib
Matplotlib and Seaborn. Matplotlib is a python library used to create 2D graphs and plots by using python scripts. It has a module named pyplot which makes things easy for plotting by providing feature to control line styles, font properties, formatting axes
✓Seaborn
✗Pandas
6. You are in charge of preprocessing the data your publishing company wants to use for a new ML model they're building, which aims to predict the influence an academic journal will have in its field. The preprocessing step is necessary to prepare the data for model training. What type of issue with the data might you encounter during this preprocessing phase. (Select TWO.)
✗Residuals
✗Insufficient batch size
✗Overfit data
✓Outliers
Outliers and Missing values
✓Missing values
7. A Machine Learning Engineer is creating and preparing data for a linear regression model. However, while preparing the data, the Engineer notices that about 20% of the numerical data contains missing values in the same two columns. The shape of the data is 500 rows by 4 columns, including the target column. How can the Engineer handle the missing values in the data?
✓Impute the missing values using regression
The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set.
✓Fill the missing values with mean of the column
✗Remove the columns containing the missing values
✗Add regularization to the model
✗Remove the rows containing the missing values
8. Gradient Descent is an important optimization method. Which one of the following is TRUE about the gradient descent method? (Select THREE)
✗It uses a batch of training data points to calculate its next step
✓It uses learning rate to multiply the effect of gradients
Let's first explain what a gradient is. A gradient is a mathematical function. When calculated on a point of a function, it gives the hyperplane (or slope) of the directions in which the function increases more. The gradient vector can be interpreted as the 'direction and rate of fastest increase'. If the gradient of a function is non-zero at a point p, the direction of the gradient is the direction in which the function increases most quickly from p, and the magnitude of the gradient is the rate of increase in that direction. Further, the gradient is the zero vector at a point if and only if it is a stationary point (where the derivative vanishes). In Data Science, it simply measures the change in all weights with regard to the change in error, as we are partially derivating by w the loss function.
✗It is more computationally efficient to use for large data sets
✓It tries to find the minimum of a loss function
✓It can involve multiple iterations
38 more questions in the app
Practise the full 46-question set with a timer, scoring and progress tracking.