Databricks-Machine-Learning-Professional Study Group - Databricks-Machine-Learning-Professional Interactive Practice Exam

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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. Pipeline construction and orchestration 2. Reusable ML workflows - 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: 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
Topic 4: MLflow and Experiment Tracking	- Experiment management 1. Tracking runs and parameters 2. Model comparison and selection - Model registry 1. Versioning and lifecycle management
Topic 5: Machine Learning Models and Algorithms	- Unsupervised learning methods 1. Dimensionality reduction 2. Clustering techniques - Supervised learning methods 1. Classification and regression models 2. Model evaluation metrics

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Databricks Certified Machine Learning Professional Sample Questions (Q73-Q78):

NEW QUESTION # 73
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. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?

Answer: B

NEW QUESTION # 74
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. spark.read.format("delta").load(path).drop("star_rating")
B. Delta tables cannot be modified
C. spark.read.format("delta").table(path).drop("star_rating")
D. spark.read.table(path).drop("star_rating")
E. spark.sql("SELECT * EXCEPT star_rating FROM path")

Answer: D

NEW QUESTION # 75
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a run with run ID run_id for the experiment with experiment ID exp_id, the data scientist wants to programmatically return the logged metrics for run_id. They have an active MLflow Client client and an active Spark session spark. Which lines of code can be used to return the logged metrics for run_id?

A.
B. client.qet_run(exp_id.run_id).data.metrics
C. client.get_run(run_id).data.metrics
D. spark.read.format("mlflow-run").load(run_id)

Answer: C

Explanation:
The correct way to retrieve logged metrics for a specific run using the MLflow Client is client.get_run(run_id).data.metrics. This returns a dictionary of all metrics logged for that run. The method in the image (mlflow.search_runs(...)) is for querying across multiple runs, not for accessing a specific run's metrics.

NEW QUESTION # 76
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, all types of data must be handled without producing an error
B. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
C. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
D. All of these statements
E. None of these statements

Answer: C

NEW QUESTION # 77
A machine learning engineering team has decided that they need to have predictions be made available for querying in continuous, equal-sized increments. A computation can be included in one of the increments when all of its feature values are in the inference Spark DataFrame. Which of the following tools can be used to provide this type of continuous inference?

A. Structured Streaming
B. Delta Lake
C. MLflow
D. Spark UDFs

Answer: A

Explanation:
Structured Streaming in Apache Spark enables continuous inference by processing incoming data in microbatches or continuous increments. It ensures that each computation occurs only when all required feature values are available in the inference DataFrame, supporting real-time or near-real-time prediction pipelines with consistent, equal-sized processing intervals.

NEW QUESTION # 78
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