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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q187-Q192):

NEW QUESTION # 187
A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

A. Use MLflow to track the model parameters and metrics in development, then export the same training conditions in staging and production.
B. Use Databricks Repos to store the model code and install the required libraries in the notebook.
C. Use custom init scripts that install the necessary packages as part of the job.
D. Use the same Databricks Runtime (DBR) version across environments and define dependencies in a lock file that is installed as part of the job.

Answer: D

Explanation:
Using the same Databricks Runtime across environments ensures a consistent base environment, and installing dependencies from a lock file guarantees identical package versions during training. This combination follows best practices for deploy-code workflows by making the training environment reproducible and preventing dependency drift between development, staging, and production.

NEW QUESTION # 188
A Machine Learning Engineer needs to deploy a production ML workflow that includes an MLflow experiment for tracking model training runs, a registered model in Unity Catalog for version management, and a model serving endpoint for real-time inference. The team requires a unified configuration approach that ensures consistent deployment across development and production environments while adhering to infrastructure-as-code best practices. Which approach should the Machine Learning Engineer use to define all three components together?

A. Create separate REST API calls for each component and run them sequentially using a shell script.
B. Use Terraform to provision the infrastructure and then manually configure each ML component through the Databricks UI.
C. Use MLflow's deployment tools to create individual deployment configurations for each component, then orchestrate them using Databricks Jobs.
D. Define experiments, registered_models, and model_serving_endpoints resources in a Databricks Asset Bundle (DAB) configuration file.

Answer: D

Explanation:
Databricks Asset Bundles allow experiments, Unity Catalog-registered models, and model serving endpoints to be defined declaratively in a single configuration. This provides a unified, version-controlled, infrastructure-as-code approach that ensures consistent deployment across environments and aligns with MLOps best practices.

NEW QUESTION # 189
Why are Delta tables often used to store machine learning features?

A. They reduce model size
B. They support schema enforcement and time travel
C. They allow faster GPU training
D. They replace Spark DataFrames

Answer: B

Explanation:
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.

NEW QUESTION # 190
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. Increase the size of the current single-node cluster to handle the increase of data while minimizing the need for code refactoring
B. 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
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 # 191
A Data Scientist needs to analyze drift detection results from Databricks Lakehouse Monitoring.
The system has generated both profile metrics and drift metrics tables. The scientist needs to identify baseline drift in numerical features by comparing current data against a baseline from 6 months ago. Which combination of table columns and values indicates baseline drift in a numerical feature?

A. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
B. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
C. log_type = "BASELINE" in profile metrics table with population_stability_index > 0.2 in drift metrics table.
D. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.

Answer: B

Explanation:
Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.

NEW QUESTION # 192
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