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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Passing Score:	70%
Exam Format:	Multiple choice
Available Languages:	English
Real Exam Qty:	60
Exam Price:	USD 200
Exam Duration:	120 minutes
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 9	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 10	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

Databricks Certified Machine Learning Professional Sample Questions (Q20-Q25):

NEW QUESTION # 20
A machine learning engineer has developed the following custom model class with preprocessing logic to combine two columns:

However, instances of this class are unable to compute predictions.
Which set of changes will update the class so predictions can be computed while continuing to apply the preprocessing logic?

A. Remove the self.rf_model = rf_model line from the fit method
B. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the preprocess_input method
C. Replace self.rf_model.predict(input) with self.predict(input) in the predict method
D. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the predict method

Answer: D

Explanation:
The issue is that the predict() method does not apply the same preprocessing as the fit() method.
During training, the model uses preprocessed data (via self.preprocess_input()), but during prediction, it directly uses raw input. This mismatch causes prediction errors because the model expects preprocessed input.
By updating the predict() method to call self.preprocess_input(model_input.copy()), both training and prediction use consistent feature transformations, allowing predictions to be computed successfully.

NEW QUESTION # 21
A Data Scientist at an online gaming company is creating a model to predict player churn. The company currently collects terabytes of player activity logs daily, which are stored in Databricks and processed for daily reporting. The Data Scientist has completed feature engineering and the resulting data is saved as a Delta Table with a size of 500GB. They need to next build the model for the most performant and cost-effective performance for Databricks. Which approach will do this?

A. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a multi-node Databricks cluster.
B. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a single-node Databricks cluster.
C. Load the feature data as a Spark DataFrame and train the model using Spark's DeepspeedTorchDistributor on a multi-node Databricks cluster.
D. Load the feature data as a Spark DataFrame and train the model using SparkML's RandomForestClassifier on a multi-node Databricks cluster.

Answer: D

Explanation:
A 500GB Delta Table is far beyond what is practical to load into a single pandas DataFrame, and scaling pandas-based scikit-learn training across nodes is not the right fit for this workload. Using a Spark DataFrame with Spark ML's RandomForestClassifier leverages distributed data processing and distributed model training on a multi-node cluster, which is the most performant and cost-effective approach for large tabular datasets in Databricks.

NEW QUESTION # 22
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client.
Which of the following code blocks can they use to accomplish the task?

Answer: E

NEW QUESTION # 23
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. fs.score_batch(model_uri, spark_df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
C. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)
D. fs.score_model(model_uri, spark_df)
E. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)

Answer: A

NEW QUESTION # 24
A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

A. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.
B. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
C. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.
D. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.

Answer: A

Explanation:
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

NEW QUESTION # 25
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