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

Section	Weight	Objectives
Model Development	44%	- Feature Store and automated feature pipelines - Advanced MLflow usage - Distributed training and hyperparameter tuning - Scalable ML pipelines with SparkML
Model Deployment	12%	- Model rollout and version management - Deployment strategies - Custom model serving
ML Ops	44%	- Testing and validation strategies - Automated retraining workflows - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles

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Databricks Certified Machine Learning Professional Sample Questions (Q116-Q121):

NEW QUESTION # 116
A Machine Learning Engineer is training a large-scale gradient boosting model using SparkML on a cluster of machines. The training job fails due to memory overflow on a single executor node after processing several iterations. The cluster resources are limited to executor nodes with 16 CPU cores and 64 GB RAM each. The engineer wants to continue training the model without changing hyperparameters or reducing the dataset size. They know Spark's architecture well and want to take advantage of its benefits. Which approach will allow the Machine Learning Engineer to solve this issue?

A. Increase the number of executor nodes and replicate the entire model on each node, then implement data parallelism to train on different mini-batches simultaneously.
B. Increase the number of executor nodes and replicate the entire model on each node, then apply model parallelism to train different parts of the model in parallel.
C. Increase the number of executor nodes and implement model parallelism by splitting the gradient boosting model across executors so each node trains a part of the model.
D. Increase the number of executor nodes and implement data parallelism by partitioning the dataset across executors so each node trains the model on a subset of data.

Answer: D

Explanation:
Spark ML algorithms, including gradient-boosted trees, are designed around data parallelism. By increasing the number of executor nodes, the dataset can be further partitioned so each executor processes only a subset of the data, reducing per-executor memory pressure while keeping the same model configuration and dataset size. This leverages Spark's distributed architecture without requiring model parallelism or hyperparameter changes.

NEW QUESTION # 117
A Machine Learning Engineer has deployed a fraud detection model that processes 10,000 transactions per hour. The model was trained on data from Q1 2024, but it's now Q4 2024. The ML team notices three concerning trends: (1) the model's precision has dropped from 92% to
78% over the past month, (2) the average transaction amount in recent data has increased from
$150 to $220 and (3) the relationship between transaction frequency and fraud likelihood has weakened significantly due to new payment methods being introduced. The engineer needs to implement a monitoring solution that can detect the root cause of the performance degradation, identify why the precision dropped, and be able to do this at the scale needed. Which monitoring pipeline component will do this?

A. Model Performance Monitoring Pipeline because it directly tracks precision metrics and can alert when thresholds are breached.
B. Model Health Monitoring Pipeline because it ensures the infrastructure can handle the 10,000 transactions per hour load.
C. Logging and Monitoring Pipeline because it captures all prediction requests and can identify patterns in the fraud predictions.
D. Drift Detection Pipeline because it can identify both the data drift (transaction amount changes) and concept drift (weakened relationship between features and target).

Answer: D

Explanation:
A drift detection pipeline is designed to diagnose the root causes of model performance degradation at scale. It can detect data drift, such as changes in the distribution of transaction amounts, and concept drift, where the relationship between input features and the fraud label changes due to new payment methods. This directly explains why precision dropped and provides actionable insight beyond simply observing metric degradation.

NEW QUESTION # 118
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

A. None of these
B. One-way Chi-squared Test
C. Kolmogorov-Smirnov (KS) test
D. Two-way Chi-squared Test
E. Jenson-Shannon distance

Answer: B

NEW QUESTION # 119
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: A

NEW QUESTION # 120
Which of the following describes concept drift?

A. Concept drift is when there is a change in the distribution of the predicted target given by the model
B. Concept drift is when there is a change in the distribution of a target variable
C. Concept drift is when there is a change in the relationship between input variables and target variables
D. Concept drift is when there is a change in the distribution of an input variable
E. None of these describe Concept drift

Answer: A

NEW QUESTION # 121
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