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| Section | Weight | Objectives |
|---|---|---|
| Model Development | 44% | - Scalable ML pipelines with SparkML - Advanced MLflow usage - Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning |
| ML Ops | 44% | - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles - Testing and validation strategies - Automated retraining workflows |
| Model Deployment | 12% | - Model rollout and version management - Deployment strategies - Custom model serving |
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NEW QUESTION # 72
A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
In order to do this, they use the following incomplete piece of code:
Which option correctly states:
(i) The complete command to run model training;
(ii) The complete command to execute the prediction on test data;
(iii) The object type of the model object returned by the model
training command.
Answer: D
Explanation:
In Spark MLlib, a Pipeline is trained using the fit method, which applies all estimator stages to the training DataFrame and returns a PipelineModel. Predictions are generated by calling transform on the fitted PipelineModel, which applies the full pipeline (tokenization, feature hashing, and logistic regression) to the test DataFrame.
NEW QUESTION # 73
A Machine Learning Engineer needs to develop fraud detection models with Databricks. They need to ensure seamless collaboration between data engineers and data scientists while maintaining strict governance, version control, and traceability as models progress from development to production. So, they need to choose the Databricks feature that will enable centralized model lineage tracking, cross-workspace access control, and automated synchronization of model versions with their training data. Which Databricks feature will do this?
Answer: A
Explanation:
Unity Catalog provides centralized governance for models, including lineage tracking between models and their training data, cross-workspace access control, and consistent version management as models move from development to production. This enables secure collaboration, traceability, and enterprise-grade model governance across teams.
NEW QUESTION # 74
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?
Answer: D
NEW QUESTION # 75
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?
Answer: B
NEW QUESTION # 76
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 URls can be used to query the described model version?
Answer: C
Explanation:
In MLflow Model Serving, the correct URL pattern to query a specific stage of a registered model is https:///model/<model_name>/<stage>/invocations Thus, to query the model named recommender in the Production stage, the proper endpoint is
https:///model/recommender/Production/invocations.
This allows direct access to the active production version without needing its version number.
NEW QUESTION # 77
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