Reliable Databricks-Machine-Learning-Professional Exam Syllabus - New Databricks-Machine-Learning-Professional Test Guide

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

Section	Weight	Objectives
Topic 1: Model Deployment	10%	- Model Serving 1. Databricks Model Serving 2. Custom serving solutions - Deployment Strategies 1. Real-time Deployment 2. Streaming Deployment 3. Batch Deployment
Topic 2: Model Lifecycle Management (MLOps)	43%	- Monitoring 1. Lakehouse Monitoring for drift detection 2. Model performance monitoring - Automated Workflows 1. Automated retraining workflows 2. CI/CD pipelines for ML - Testing Strategies 1. Model testing and validation 2. Data quality testing - Environment Management 1. Environment configuration and reproducibility 2. Databricks Asset Bundles (DABs)
Topic 3: Model Development	47%	- SparkML and Scaling 1. Building scalable ML pipelines with SparkML 2. Distributed training and hyperparameter tuning 3. Ray/Optuna distributed tuning - MLflow Advanced Features 1. Experiment tracking and model management 2. Model registry and versioning - Feature Store 1. Feature Store concepts and usage 2. Automated feature pipelines

Section

Weight

Objectives

Topic 1: Model Deployment

10%

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

- Deployment Strategies

1. Real-time Deployment
2. Streaming Deployment
3. Batch Deployment

Topic 2: Model Lifecycle Management (MLOps)

43%

- Monitoring

1. Lakehouse Monitoring for drift detection
2. Model performance monitoring

- Automated Workflows

1. Automated retraining workflows
2. CI/CD pipelines for ML

- Testing Strategies

1. Model testing and validation
2. Data quality testing

- Environment Management

1. Environment configuration and reproducibility
2. Databricks Asset Bundles (DABs)

Topic 3: Model Development

47%

- SparkML and Scaling

1. Building scalable ML pipelines with SparkML
2. Distributed training and hyperparameter tuning
3. Ray/Optuna distributed tuning

- MLflow Advanced Features

1. Experiment tracking and model management
2. Model registry and versioning

- Feature Store

1. Feature Store concepts and usage
2. Automated feature pipelines

>> Reliable Databricks-Machine-Learning-Professional Exam Syllabus <<

Databricks Certified Machine Learning Professional free sure questions & Databricks-Machine-Learning-Professional easy download preparation

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Databricks Certified Machine Learning Professional Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which of the following can be used to compare the relative prevalence of specific values in a single categorical variable between two datasets or time periods?

A. Number of unique values
B. Contingency tables
C. Mode
D. Jensen-Shannon distance

Answer: B

Explanation:
Contingency tables (also known as cross-tabulations) display the frequency distribution of categorical variables, allowing comparison of how specific category values occur across two datasets or time periods. This makes them effective for identifying categorical feature drift or shifts in value prevalence.

NEW QUESTION # 69
A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.
Which of the following model URIs can be used to query the described model version?

A. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
B. https://<databricks-instance>/model/recommender/stage-production/invocations
C. https://<databricks-instance>/model/recommender/Production/invocations
D. https://<databricks-instance>/model-serving/recommender/Production/invocations
E. The version number of the model version in Production is necessary to complete this task.

Answer: E

NEW QUESTION # 70
A Data Scientist has been performing hyperparameter tuning using Ray Tune with grid search.
After team discussions, they decide to switch to Bayesian optimization to more efficiently explore the parameter space.
Their current code is:

How can they implement this change?

Answer: C

Explanation:
Bayesian optimization in Ray Tune requires defining a continuous or discrete search space (such as tune.randint) and explicitly configuring a Bayesian search algorithm. Using tune.randint defines a probabilistic parameter domain, and setting search_alg to BayesOptSearch enables Bayesian optimization to efficiently explore the space based on past trial results, rather than exhaustively enumerating all values as in grid search.

NEW QUESTION # 71
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. 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.
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. 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.

Answer: D

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 # 72
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?

A. Statistical tests
B. Oversampling
C. Correlations
D. None of these can provide more robust drift monitoring than summary statistics

Answer: A

Explanation:
Statistical tests (such as the Kolmogorov-Smirnov test or Wasserstein distance) provide a more robust and quantitative method for detecting numeric feature drift compared to simple summary statistics. These tests compare the full distributions of features between datasets, making them more sensitive to subtle changes in data behavior over time.

NEW QUESTION # 73
......

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