2026 Realistic Databricks New Databricks-Machine-Learning-Professional Exam Fee Free PDF Quiz

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 2	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 6	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table

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Databricks Certified Machine Learning Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
A data scientist wants to remove the star_rating column from the Delta table at the location path.
To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

A. spark.read.table(path).drop("star_rating")
B. Delta tables cannot be modified
C. spark.read.format("delta").table(path).drop("star_rating")
D. spark.read.format("delta").load(path).drop("star_rating")
E. spark.sql("SELECT * EXCEPT star_rating FROM path")

Answer: A

NEW QUESTION # 47
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

A. mlflow.sklearn.load_model(model_uri)
B. mlflow.pyfunc.load_model(model_uri)
C. mlflow.spark.load_model(model_uri)
D. mlflow.spark.read_model(model_uri)

Answer: C

Explanation:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.

NEW QUESTION # 48
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. client.list_run_infos(exp_id)
B. spark.read.format("delta").load(exp_id)
C. There is no way to programmatically return row-level results from an MLflow Experiment.
D. mlflow.search_runs(exp_id)
E. spark.read.format("mlflow-experiment").load(exp_id)

Answer: B

NEW QUESTION # 49
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. None of these should be completed as Step #3
B. Retrain the model
C. Obtain the observed values (actual) feature values
D. Compute the evaluation metric using the observed and predicted values
E. Measure the latency of the prediction time

Answer: A

NEW QUESTION # 50
A Data Scientist at a company with rapidly increasing sales has deployed a scikit-learn model in production, which is retrained weekly on a single-node cluster. During the most recent retraining, the job failed due to an out-of-memory error. Upon investigation, the Data Scientist discovered that the training data had increased to 700GB as a result of the company's expanding customer base. Which approach will reliably resolve this issue in the long term?

A. Refactor the code to use the DeepSpeed library to shard the scikit-learn model across multiple nodes, enabling distributed model training on large volumes of data
B. Increase the size of the current single-node cluster to handle the increase of data while minimizing the need for code refactoring
C. Refactor the code to use MLlib across multiple nodes, enabling distributed model training on large volumes of data
D. Wrap the current model training code as a Spark UDF and apply it to the Spark DataFrame to distribute model training across multiple nodes.

Answer: C

Explanation:
Spark MLlib is designed for distributed model training on large-scale datasets and can natively handle hundreds of gigabytes of data across multiple nodes. Refactoring to MLlib enables the training workload to scale with data growth, avoids single-node memory limitations, and provides a reliable long-term solution as the company's data continues to expand.

NEW QUESTION # 51
......

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