Download Certified Machine Learning Associate.CERTIFIED-MACHINE-LEARNING-ASSOCIATE.ExamTopics.2026-01-23.61q.tqb

Vendor: Databricks
Exam Code: CERTIFIED-MACHINE-LEARNING-ASSOCIATE
Exam Name: Certified Machine Learning Associate
Date: Jan 23, 2026
File Size: 2 MB
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Demo Questions

Question 1
A data scientist is developing a machine learning model to predict house prices in a competitive real estate market. They initially select a loss function that heavily penalizes large errors, hoping it will improve the model’s performance. However, after training, they observe that the model struggles to converge and produces unstable predictions, with large variations in price predictions for similar houses. The data scientist suspects that the chosen loss function is causing these issues.
Why is it crucial to select the right loss function in this situation?
  1. The loss faction ensures that the data is balanced.
  2. The loss function directly influences how the model's parameters are updated during training.
  3. The loss function controls the size of the training set.
  4. The loss function determines the computational efficiency of the model.
Correct answer: B
Question 2
A machine learning engineer working for an Energy Distributor wants to perform a time-series forecast of critical turbines sensor measures.
Here is the feature set under consideration:
The machine learning engineer creates a first model and gets a higher than expected RMSE. They believe the model can be improved using data imputation. They will use data science best practices for imputation.
Which data imputation strategy is most likely to enhance the dataset's quality and predictive performance?
  1. For sensor type use mode, for temperature and vibration use mean (per day).
  2. They can just remove all out of range values and missing values from the training set.
  3. For sensor type use mode, for temperature use median (per day) and for vibration use mean (per day).
  4. For sensor type use mode, for temperature use mean (per day) and for vibration use median (per day).
Correct answer: D
Question 3
A data scientist wants to train a model using an existing Delta table as a feature table, in a workspace with Unity Catalog enabled. As this Delta table already contains data that is constantly being updated, they want to avoid duplicating data or having to keep another table synchronized. This table was created with the following code, with the customer_id column as the unique identifier:
Which SQL code statement will enable them to use this Delta table as a feature table?
  1. CREATE FEATURE TABLE store.rfv_analysis.customer_features USING PRIMARY KEY (customer_id);
  3. ALTER TABLE store.rfv_analysis.customer_features ADD CONSTRAINT customer_features_pk PRIMARY KEY(customer id);
  4. ALTER TABLE store.rfv_analysis.customer_features ALTER COLUMN customer_id SET NOT NULL;
    ALTER TABLE store.rfv_analysis.customer_features ADD CONSTRAINT customer_features_pk PRIMARY KEY (customer_id);
Correct answer: A
Question 4
A data scientist in an investment banking company has been asked to accelerate their model development process on certain tasks of their everyday job so they can free up time for other ones.
Which task is typically automated by AutoML in Databricks for that purpose?
  1. Monitoring model performance for inference.
  2. Training and optimizing different models.
  3. Deploying the model on a serving endpoint.
  4. Creating a feature engineering set for model development.
Correct answer: B
Question 5
A machine learning engineer is converting a decision tree from sklearn to Spark ML.
During the training process following this conversion, they receive the following error describing that need to set the maxBins parameter to at least 515 (or remove the feature):
Which of the following results in Spark ML needing the maxBins parameter to be at least as large as the number values in each categorical feature?
  1. Spark ML tests a limit of 32 split candidates for categorical features in the splitting algorithm
  2. Spark ML needs more split candidates in the splitting algorithm than single-node implementations
  3. Spark ML needs at least one bin for each category in each categorical feature
  4. Spark ML tests only categorical features in the splitting algorithm
Correct answer: C
Question 6
A machine learning engineer is updating a machine learning project to automatically refresh its model each time it runs. The project is attached to the existing model model_name in the MLflow Model Registry.
They are using the following code block as part of their solution:
Which of the following describes the impact of the registered_model_name=model_name parameter and argument given that model_name already exists in the MLflow Model Registry?
  1. It identifies the name of the logged model in the MLflow Experiment.
  2. It registers a new model called model_name in the MLflow Model Registry.
  3. It avoids the need to specify the model name in the subsequent required call to mlflow.register_model.
  4. It registers the new version of the model_name model in the MLflow Model Registry.
Correct answer: D
Question 7
Which of the following causes the performance speed of using the pandas API on Spark to be slower than that of native Spark DataFrames for large datasets?
  1. The usage of an InternalFrame to track metadata
  2. The increased amount of required code
  3. The lack of data distribution
  4. The eager evaluation of all processing
Correct answer: A
Question 8
A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning, and they have identified the best model in the experiment. Now, they want to view the source code used to generate the best run.
Which of the following approaches can the data scientist use to view the code that produced the best model?
  1. They can click on the “View notebook for best model” button in the AutoML Experiment page
  2. They can click on the link in the “Model” field for the appropriate row in the AutoML Experiment page's table
  3. They can click on the link in the “Start Time” field for the appropriate row in the AutoML Experiment page's table
  4. They can click on the “Share” button in the AutoML Experiment page
Correct answer: A
Question 9
A machine learning engineer has tested a new Staging version of a model registered to the MLflow Model Registry. The model has passed all of its tests. As a result, the machine learning engineer wants to request that this model be put into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?
  1. The model page in the MLflow Model Registry
  2. The model version page in the MLflow Model Registry
  3. The run page in the Experiments observatory
  4. The experiment page in the Experiments observatory
Correct answer: B
Question 10
Which of the following describes boosting for machine learning models?
  1. Boosting is the ensemble process of training machine learning models sequentially with each model learning from the previously trained models.
  2. Boosting is the ensemble process of training machine learning models sequentially with each model being trained on a unique subset of the data.
  3. Boosting is the ensemble process of training a machine learning model for each sample in a set of bootstrapped samples of the training data, and then appending the model estimates as a feature variable on the training set which is used to train another model.
  4. Boosting is the ensemble process of training a machine learning model for each sample in a set of bootstrapped samples of the training data and aggregating the predictions of each model to obtain a final estimate.
Correct answer: A
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