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

Section	Objectives
MLOps, Monitoring, and Governance	- Governance and compliance 1. Model lifecycle governance 2. Feature Store usage and management - Model monitoring 1. Performance tracking in production 2. Data drift detection
Machine Learning Models and Algorithms	- Supervised learning methods 1. Classification and regression models 2. Model evaluation metrics - Unsupervised learning methods 1. Dimensionality reduction 2. Clustering techniques
Model Deployment and Serving	- Model deployment strategies 1. Batch inference vs real-time inference 2. Databricks Model Serving
MLflow and Experiment Tracking	- Experiment management 1. Model comparison and selection 2. Tracking runs and parameters - Model registry 1. Versioning and lifecycle management
Machine Learning Workflow on Databricks	- Data preparation and feature engineering 1. Feature engineering techniques 2. Data ingestion and cleaning in Databricks - End-to-end ML pipelines 1. Reusable ML workflows 2. Pipeline construction and orchestration

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Databricks Certified Machine Learning Professional Sample Questions (Q75-Q80):

NEW QUESTION # 75
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. Delta tables cannot be modified
B. spark.sql("SELECT * EXCEPT star_rating FROM path")
C. spark.read.format("delta").table(path).drop("star_rating")
D. spark.read.format("delta").load(path).drop("star_rating")
E. spark.read.table(path).drop("star_rating")

Answer: E

NEW QUESTION # 76
A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

Answer: D

Explanation:
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table's primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.

NEW QUESTION # 77
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 self.rf_model.predict(input) with self.predict(input) in the predict method
C. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the preprocess_input 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 # 78
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which statement is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. There is no longer a need for pipeline-like machine learning objects
B. This approach has no impact when loading the logged Pvfunc model for downstream deployment
C. The same preprocessing logic will automatically be applied when calling fit
D. The pvfunc model can be used to deploy models in a parallelizable fashion
E. The same preprocessing logic will automatically be applied when calling predict

Answer: A

NEW QUESTION # 79
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 SparkML's RandomForestClassifier on a multi-node Databricks cluster.
D. Load the feature data as a Spark DataFrame and train the model using Spark's DeepspeedTorchDistributor on a multi-node Databricks cluster.

Answer: C

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 # 80
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