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

Section	Objectives
Topic 1: Machine Learning Workflow on Databricks	- End-to-end ML pipelines 1. Reusable ML workflows 2. Pipeline construction and orchestration - Data preparation and feature engineering 1. Feature engineering techniques 2. Data ingestion and cleaning in Databricks
Topic 2: Model Deployment and Serving	- Model deployment strategies 1. Databricks Model Serving 2. Batch inference vs real-time inference
Topic 3: MLflow and Experiment Tracking	- Experiment management 1. Model comparison and selection 2. Tracking runs and parameters - Model registry 1. Versioning and lifecycle management
Topic 4: Machine Learning Models and Algorithms	- Supervised learning methods 1. Model evaluation metrics 2. Classification and regression models - Unsupervised learning methods 1. Clustering techniques 2. Dimensionality reduction
Topic 5: MLOps, Monitoring, and Governance	- Model monitoring 1. Performance tracking in production 2. Data drift detection - Governance and compliance 1. Feature Store usage and management 2. Model lifecycle governance

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Databricks Certified Machine Learning Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
A Machine Learning Engineer has previously built a feature table for model training and inference using a batch mode approach:

They have been informed that they now require these features to be available in "real-time", with latency on the order of a minute. Their manager has informed them there is now a Kafka stream from which they can stream live data, and they need to have this ingested and available for low- latency feature lookups.
Which change to their existing code will achieve this?

A. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
B. Change the incoming_df to be a dataframe based on a readStream() from the kafka source, the write_table() method will provide a low-latency lookup on this data.
C. Change the incoming_df to be a dataframe based on a readStream() from the Kafka source and publish the table as an online table with the streaming option set to True.
D. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.

Answer: C

Explanation:
To achieve real-time availability with minute-level latency, the feature data must be continuously ingested from Kafka and published to an online table. Using a streaming DataFrame created with readStream from the Kafka source and enabling the online table with streaming allows incremental updates to be synchronized to the online store, supporting low-latency feature lookups for real-time inference.

NEW QUESTION # 19
A Data Scientist is developing a model training pipeline on Databricks and needs to track custom performance metrics during training. They want to log a custom evaluation score (team_score), a single hyperparameter, and a confusion matrix plot as part of their MLflow experiment. Which code snippet correctly logs all three types of information in MLflow?

A. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact("confusion_matrix.png")
B. mlflow.log_param("max_depth", "5")
mlflow.log_metric("team_score", "0.92")
mlflow.log_artifact("confusion_matrix.png")
C. mlflow.log_metric({"team_score": 0.92})
mlflow.log_param(["max_depth", 5])
mlflow.log_file("confusion_matrix.png")
D. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact(open("confusion_matrix.png"))

Answer: A

Explanation:
This snippet correctly uses MLflow's APIs to log each item in its expected form: a single hyperparameter with log_param, a numeric custom metric with log_metric, and a file-based artifact such as a confusion matrix image by passing its file path to log_artifact. This is the standard and correct way to track parameters, metrics, and artifacts in an MLflow experiment.

NEW QUESTION # 20
A machine learning engineer is in the process of implementing a feature drift monitoring solution.
They are planning to use the following steps:
1. Measure the distributions of each feature variable in the training
set
2. Deploy a model to production
3. Measure the distributions of each feature variable in inference
4. _______
Which action should be completed as Step #4?

A. Measure the latency of the model's prediction time
B. Run a statistical test to determine if there are changes in the feature variable distribution over time
C. Retrain the model based on any new feature variables that have been added to the feature set
D. Obtain the observed values (actual) feature values and compare to the predicted values

Answer: B

Explanation:
The final step in a feature drift monitoring solution is to run a statistical test (e.g., Kolmogorov- Smirnov test) to determine whether the feature distributions in production have significantly diverged from those in the training set. This helps detect drift and maintain model reliability.

NEW QUESTION # 21
A retail company wants to better forecast their sales of each SKU in every store in order to more accurately distribute their products. To achieve this, a Data Scientist proposes scaling their existing forecasting model to forecast individually for each combination of SKU and Store ID.
They have a cluster with 12 executors available in order to execute this. The current model is written using Pandas and the Prophet library for forecasting, with a Python function that receives a Pandas Data Frame with historic sales data as a parameter to train the forecasting model. For their next iteration, they want to improve the efficiency of this approach while using the least amount of effort and leveraging all available resources. Which approach will do this?

A. 1. Rewrite the function using the Generalized Linear Regression models available in Spark MLlib, which allows parameterizing to reflect a similar behavior to that of Prophet;
2. Perform a GROUP BY on the Spark DataFrame with the training data to group by Store and SKU;
3. Apply the Spark UDF to the grouped DataFrame with the apply function.
B. 1. Decorate the existing training function with the @pandas_udf;
2. On the Spark DataFrame with all training data perform a GROUP BY by Store and SKU;
3. Apply the decorated function to the grouped DataFrame with the apply function.
C. 1. Use a Spark query to identify all combinations of SKU and Store ID;
2. Run a loop through every combination calling the existing Python function using Spark repartition(12) function so it distributes across all available executors.
D. 1. Use Spark repartition(12) to scale across all executors;
2. Run to Pandas to convert the repartitioned DataFrame to Pandas so it can run on the original existing function;
3. Add the Store ID and SKU to the Prophet model as exogenous variables.

Answer: B

Explanation:
A grouped Pandas UDF lets you keep the existing Prophet-based pandas training function while distributing execution across the cluster. Grouping the Spark DataFrame by SKU and Store ID and applying the function runs one model per group in parallel on the executors, improving efficiency and leveraging all available resources with minimal refactoring.

NEW QUESTION # 22
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_param
B. mlflow.log_model
C. mlflow.log_artifact
D. mlflow.log_metric
E. There is no way to store values like this.

Answer: D

NEW QUESTION # 23
......

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