Databricks Machine Learning Professional Exam Questions 2026
Databricks Machine Learning Professional Exam Questions 2026
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Question 1:
Where can the data scientist view the visualizations generated by the machine learning pipeline in Databricks?
A.Within the MLflow Model Registry Model page
B.In the Artifacts section located on the MLflow Experiment page
C.Data visualizations cannot be directly accessed in Databricks
D.Within the Artifacts section available on the MLflow Run page
E.In the Figures section found on the MLflow Run page
Answer: D
Explanation:
In Databricks, when you run a machine learning pipeline and use MLflow to log metrics, parameters, and artifacts, the visualizations generated during the pipeline execution are typically stored as artifacts. These artifacts can be viewed within the Artifacts section available on the MLflow Run page. This allows data scientists to inspect the visualizations and other artifacts associated with each individual run of the pipeline.
Here are short comments on the other options:
A. Within the MLflow Model Registry Model page: The MLflow Model Registry Model page is primarily for managing model versions, metadata, and transitions between stages, not for viewing visualizations.
B. In the Artifacts section located on the MLflow Experiment page: The MLflow Experiment page displays experiment runs and associated metrics, parameters, and artifacts, but visualizations are typically viewed within the Artifacts section of individual run pages, not on the Experiment page.
C. Data visualizations cannot be directly accessed in Databricks: This statement is incorrect. Data visualizations can be accessed through various tools and methods within Databricks, including MLflow.
E. In the Figures section found on the MLflow Run page: MLflow does not have a specific “Figures“ section on the Run page. Visualizations are typically stored and accessed as artifacts within the Artifacts section.
Question 2:
In an existing machine learning pipeline, a machine learning engineer is manually refreshing a model. The pipeline employs the MLflow Model Registry named “project“. The engineer intends to introduce a new version of the model into “project“. Which of the following MLflow operations can the engineer utilize to achieve this objective?
A.Register a model:mlflow.register_model
B.Update a registered model: MlflowClient.update_registered_model
C.Add a model version: mlflow.add_model_version
D.Retrieve a model version: MlflowClient.get_model_version
E.The engineer needs to create an entirely new MLflow Model Registry model.
Answer: A
Explanation:
New Model: If the model itself is completely new (meaning it has never been registered in the “project“ Model Registry before), then mlflow.register_model is the way to go.
This will create a new model entry in the registry.
It simultaneously adds the first version of your new model.
Other options:
B. Update a registered model: MlflowClient.update_registered_model – This is best used for updating an existing model by transitioning a new version into a ‘Staging‘ or ‘Production‘ stage. Our scenario implies the model itself is completely new.
C. Add a model version: mlflow.add_model_version – This function doesn‘t exist in the MLflow API. The process involves either registering a new model or updating an existing one.
D. Retrieve a model version: MlflowClient.get_model_version – Its purpose is to retrieve information about an existing model version, not add a new one.
E. The engineer needs to create an entirely new MLflow Model Registry model. – This is excessive and unnecessary if the goal is to simply introduce a new model under an existing Model Registry.
https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.register_model
Question 3:
A data scientist has configured a machine learning pipeline to automatically log a data visualization with each run. They aim to view these visualizations in Databricks. Where in Databricks can these data visualizations be found?
A. III. Data visualizations cannot be viewed in Databricks through logging
B. V. The Figures section of the MLflow Run page
C. I. The MLflow Model Registry Model page
D. IV. The Artifacts section of the MLflow Run page
E. II. The Artifacts section of the MLflow Experiment page
Answer: D
Explanation:
The correct answer is IV. The Artifacts section of the MLflow Run page
Here‘s the breakdown:
MLflow Experiment Page: Within an MLflow Experiment, each run of your machine learning pipeline is logged.
Artifacts Section: Each run has an Artifacts section where additional files generated during the run are stored, including data visualizations.
Figures Section: There‘s no specific “Figures“ section in MLflow runs.
Why other options are incorrect:
I. The MLflow Model Registry Model page: Primarily focuses on model metadata and versions, not per-run artifacts like visualizations.
III. Data visualizations cannot be viewed in Databricks through logging: Incorrect; MLflow‘s logging specifically allows visualizing data within Databricks.
Question 4:
In the context of a machine learning engineering team experiencing slow querying of predictions stored in a Delta table due to sparse row distribution within data files, which optimization technique can expedite the query process by arranging similar records together while considering values in multiple columns?
A.Implementing Z-Ordering
B.Utilizing Bin-packing
C.Writing data as a Parquet file
D.Employing Data skipping E
E.Adjusting file size tuning
Answer: A
Explanation:
How Z-Ordering Helps:
Co-locality: Z-Ordering rearranges data within Delta Lake files to place rows with similar values across multiple columns closer together.
Efficient Filtering: This co-locality helps Delta Lake‘s data-skipping algorithms significantly. When you query based on those columns, Delta Lake can skip large chunks of files that don‘t contain relevant values.
Multi-Column Optimization: Z-Ordering allows optimization based on combinations of columns, enhancing query performance when your filters involve multiple columns relevant to the ordering.
Why Other Options Are Less Ideal:
B. Utilizing Bin-packing: Bin-packing focuses on optimizing storage space usage, not primarily targeting query performance based on data similarity.
C. Writing data as a Parquet file: Parquet is a columnar format that offers advantages, but it doesn‘t inherently solve the sparse row distribution issue. Z-Ordering works within the Parquet files themselves.
D. Employing Data skipping: Data skipping is a technique Delta Lake already leverages, but its effectiveness is greatly improved by Z-Ordering‘s co-locality.
E. Adjusting file size tuning: While file size impacts performance, it doesn‘t directly address the sparsity issue that leads to inefficient filtering.
Question 5:
Which deployment strategy is suitable for a machine learning engineer who needs to ensure fast results for one record at a time, with feature values only available upon delivery?
A.Utilizing Edge/on-device processing
B.Implementing a Streaming deployment approach
C.None of the available strategies will meet the specified requirements
D.Employing a Batch deployment method
E.Opting for Real-time processing
Answer: E
Explanation:
Key Requirements:
Single Record: The need is to work with one data point at a time.
Immediate Results: Fast results are crucial.
Unpredictable Feature Availability: Feature values arrive on-demand.
Real-time Processing Alignment:
Individual Record Handling: Real-time systems are inherently designed to process and respond to individual data points as they arrive.
Low Latency: Real-time processing prioritizes minimal delay between receiving input and producing the corresponding output.
Adaptability: Real-time models can handle features that become available dynamically, without the need for pre-defined batches.
Why Other Options Are Less Suitable:
A. Edge/on-device processing: While edge deployment can provide low latency, it might not be the best fit if features are not locally generated on the device. Network communication might become a bottleneck.
B. Streaming Deployment: Streaming focuses on continuous data flows, not necessarily single-record immediate responses.
C. None of the strategies…: This is incorrect, as real-time processing addresses the given requirements.
D. Batch Deployment: Batch processing works with groups of data and introduces delays, which conflicts with the need for immediate results on single records.
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Question 6:
In a scenario where a machine learning engineer needs to deliver predictions in real-time, with feature values available one week before the query time, what is a benefit of utilizing a batch serving deployment over a real-time serving deployment, where predictions are computed at query time?
A.Batch serving deployment integrates seamlessly with Databricks Machine Learning
B.No discernible advantage exists for batch serving deployments compared to real-time serving deployments
C.Real-time computation of predictions yields more current results
D.Real-time serving deployments do not allow for testing
E.Retrieving stored predictions can be quicker than computing predictions in real-time
Answer: E
Explanation:
Key Consideration: One-Week Lead Time
Since feature values are available a week in advance, you have the opportunity to pre-compute predictions and store them. This makes retrieval during real-time queries potentially much faster than on-the-fly computation.
Batch Serving for Pre-Computation:
Pre-processing and Predictions: Batch serving allows you to schedule jobs that process new data, update features, and generate predictions on a regular cadence (e.g., daily or weekly).
Storage: The pre-computed predictions are stored (database, cache, etc.), ready for retrieval.
Query Time: When a real-time query arrives, the system fetches the pre-computed prediction, leading to faster response times.
Why Other Options Are Less Relevant:
A. Batch serving deployment integrates seamlessly with Databricks Machine Learning: While Databricks ML supports batch serving, this doesn‘t provide an inherent advantage for the given scenario. Both batch and real-time serving can be deployed on Databricks.
B. No discernible advantage exists for batch serving deployments…: This is inaccurate. The one-week lead time for features creates a significant advantage for batch serving with pre-computed predictions.
C. Real-time computation of predictions yields more current results: True in general, but with a one-week lead time, the predictions generated beforehand would likely still be very current when needed.
D. Real-time serving deployments do not allow for testing: This is incorrect. Testing is crucial in both batch and real-time serving contexts.
Question 7:
A data scientist has developed a scikit-learn random forest model but has not yet logged it with MLflow. They aim to retrieve the input schema and the output schema of the model to document the expected input data type. Which MLflow operation can achieve this task?
A. V. There is no way to obtain the input schema and the output schema of an unlogged model.
B. II. mlflow.models.signature.infer_signature
C. I. mlflow.models.schema.infer_schema
D. III. mlflow.models.Model.get_input_schema
E. IV. mlflow.models.Model.signature
Answer: B
Explanation:
II. mlflow.models.signature.infer_signature is designed to automatically deduce the input and output schemas of a machine learning model based on its structure. Here‘s what it does:
Analyzes the model: It examines the model object to determine the expected input data types and output data types.
Infers the schema: It creates a schema definition based on the inferred data types.
Why other options are less suitable:
I. mlflow.models.schema.infer_schema: This function is used to infer a schema from a provided dataset, not directly from a model object.
III. & IV. There is no specific Model object and methods like get_input_schema or signature in MLflow‘s Python API for generic ML models. These might exist in specific flavors of MLflow models.
V. While it‘s harder to get the schema of an unlogged model, it‘s not impossible. mlflow.models.signature.infer_signature is the way to do it.
Question 8:
In the context of monitoring categorical input variables for a production machine learning application, a machine learning engineer observes an increase in missing values for a specific category in one of the variables. Which tool can the engineer utilize to evaluate this observation effectively?
A. II. One-way Chi-squared Test
B. IV. Jenson-Shannon distance
C. III. Two-way Chi-squared Test
D. I. Kolmogorov-Smirnov (KS) test
E. V. None of the options provided
Answer: A
Explanation:
Focus: The situation describes a change in the distribution of a single categorical variable (the one with increased missing values). The one-way Chi-squared test is specifically designed to compare the observed distribution of a categorical variable to an expected or historical distribution.
Purpose: This test can help determine whether the increase in missing values is statistically significant compared to a previous baseline distribution. If the change is significant, it might indicate a problem with data quality, feature processing, or even signal potential concept drift.
Why other options are less suitable:
I. Kolmogorov-Smirnov (KS) test: The KS test is primarily designed for comparing continuous distributions. While it can sometimes be adapted for categorical data, the Chi-squared test is a more direct and established tool for this purpose.
III. Two-way Chi-squared Test: This test is used to analyze the relationship between two categorical variables. Since this scenario focuses on a single variable, the one-way test is a simpler and more suitable approach.
IV. Jenson-Shannon distance: This measures similarity between probability distributions. It‘s less directly applicable to detecting a change in a single variable‘s distribution.
Question 9:
What is the purpose of the context parameter in the predict method of Python models for MLflow?
A. II. The context parameter aids in documenting the performance of a model post-deployment
B. III. The context parameter enables the inclusion of pertinent details of the business case to assist downstream users in understanding the model‘s purpose
C. IV. The context parameter permits the provision of the model with custom if-else logic tailored to the application‘s current scenario
D. I. The context parameter facilitates the specification of the version of the registered MLflow Model to be utilized based on the current scenario of the application
E. V. The context parameter allows the provision of access to objects like preprocessing models or custom configuration files for the model
Answer: E
Explanation:
The context parameter within an MLflow Python model‘s predict method provides a way to access artifacts that the model might need during the prediction phase. These artifacts could include:
Preprocessing objects: Encoders, scalers, or other objects used to transform raw input data into the format the model expects.
Configuration files: Files containing hyperparameters, model-specific settings, or any other configuration data required for proper inference.
External dependencies: References to external data sources or files that the model needs to function.
Why other options are incorrect
I. Specifying the model version is typically done through the model loading process (e.g., mlflow.pyfunc.load_model()) rather than in the predict method itself.
II. Performance documentation is usually handled within MLflow‘s tracking and logging capabilities, not directly through the context parameter.
III. Business case details, while important, would reside in the overall MLflow project metadata and documentation, not in the context parameter specifically.
IV. Custom if-else logic is generally embedded within the model‘s prediction code and not injected through the context parameter.
Question 10:
A senior machine learning engineer is configuring a machine learning pipeline. They have automated the process to transition a new version of a registered model to the Production stage in the Model Registry once it successfully passes all tests using the MLflow Client API. Which operation was utilized to transition the model to the Production stage?
A. client.update_model_stage
B. client.transition_model_version_stage
C. client.transition_model_version
D. client.update_model_version
Answer: B
Explanation:
Specificity: The transition_model_version_stage method is designed specifically to change the stage (e.g., Staging, Production, Archived) of a particular model version within the MLflow Model Registry.
Targeting Model Versions: Model versions are the individual units that progress through the Model Registry. Transitioning a specific version to the Production stage is the intended action.
Why Other Options Are Less Suitable:
A. client.update_model_stage: There‘s no such method in the MLflow Client API. Model stages are associated with model versions.
C. client.transition_model_version: While the concept is correct, this method name is not part of the MLflow Client API.
D. client.update_model_version: This method lets you update metadata (like description) of a specific model version but doesn‘t directly change its stage.
Example Code Snippet:
Python
import mlflow client = mlflow.tracking.MlflowClient() model_name = “my_model“ model_version = 2 # The version you want to transition new_stage = “Production“ client.transition_model_version_stage( name=model_name, version=model_version, stage=new_stage )
For a full set of 360 questions. Go to
https://skillcertpro.com/product/databricks-machine-learning-professional-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.