Online Databricks-Machine-Learning-Professional Test - Databricks-Machine-Learning-Professional Minimum Pass Score

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| Section | Weight | Objectives |
|---|
| Topic 1: Model Deployment | 12% | - Model rollout and version management - Deployment strategies - Custom model serving
|
| Topic 2: Model Development | 44% | - Feature Store and automated feature pipelines - Advanced MLflow usage - Scalable ML pipelines with SparkML - Distributed training and hyperparameter tuning
|
| Topic 3: ML Ops | 44% | - Testing and validation strategies - Automated retraining workflows - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles
|
>> Online Databricks-Machine-Learning-Professional Test <<
Databricks-Machine-Learning-Professional Minimum Pass Score & Databricks-Machine-Learning-Professional Latest Dumps
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Databricks Certified Machine Learning Professional Sample Questions (Q34-Q39):
NEW QUESTION # 34
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?
Answer: A
NEW QUESTION # 35
Concept drift is when there is a change in which element?
- A. The distribution of the predicted target given by the model
- B. The distribution of a target variable
- C. The distribution of an input variable
- D. The relationship between input variables and target variables
Answer: D
Explanation:
Concept drift occurs when the underlying relationship between input features and the target variable changes over time. This means that even if the feature and label distributions remain stable, the way features influence the target (the predictive relationship) has shifted, leading to reduced model accuracy unless retraining occurs.
NEW QUESTION # 36
A Data Scientist is using Spark ML to train a model for detecting fraudulent transactions (1:
fraudulent, 0: non-fraudulent). To maximize the model's overall AUC (ROC) score, the Data Scientist wants to perform hyperparameter tuning on a Random Forest classifier. Due to budget constraints, the tuning process must be completed efficiently to avoid excessive compute costs.
Which approach will fulfill their needs?
Answer: D
Explanation:
TrainValidationSplit evaluates each hyperparameter combination using a single train/validation split, which is significantly more compute-efficient than k-fold cross-validation. Using a BinaryClassificationEvaluator aligns directly with optimizing AUC (ROC) for a binary fraud detection task, making this approach well suited for maximizing model performance under budget and compute constraints.
NEW QUESTION # 37
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 implement model parallelism by splitting the gradient boosting model across executors so each node trains a part of the model.
- 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 data parallelism by partitioning the dataset across executors so each node trains the model on a subset of data.
- D. 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.
Answer: C
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 # 38
What is commonly monitored in deployed ML models?
- A. Notebook formatting
- B. Code style
- C. GPU usage only
- D. Model drift and prediction quality
Answer: D
Explanation:
Production ML systems monitor:
data drift
model drift
prediction accuracy.
NEW QUESTION # 39
......
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