Databricks Databricks-Machine-Learning-Professional

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

Reference: https://www.databricks.com/learn/certification/machine-learning-professional

Databricks Certified Machine Learning Professional Sample Questions:

Question #1
A Machine Learning Engineer is setting up a cluster for a deep learning training run, but has a number of settings options to choose from. Their cluster will be reused by other engineers on their team and their datasets vary in size from hundreds of MBs to hundreds of GBs. They need to choose a configuration that allows for performant, stable deep learning training without excessive costs. Which configuration will do this?

A. Using Databricks Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
B. Using ML Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
C. Using ML Runtime, use a large single node, GPU-enabled VM with auto termination set to 20 minutes to reduce costs
D. Using ML Runtime, use a moderate sized GPU VM for the driver and a variable number of memory optimized CPU VMs for the workers with autoscale enabled

Question #2
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. log_type = "BASELINE" in profile metrics table with population_stability_index > 0.2 in drift metrics table.
B. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
C. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
D. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.

Question #3
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.

B.

C.

D. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
E.

Question #4
A machine learning engineer needs to deliver predictions of a machine learning model in real- time. However, the feature values needed for computing the predictions are available one week before the query time. Which feature is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Querying stored predictions can be faster than computing predictions in real-time
B. Testing is not possible in real-time serving deployments
C. Batch serving has built-in capabilities in Databricks Machine Learning
D. There is no advantage to using batch serving deployments over real-time serving deployments
E. Computing predictions in real-time provides more up-to-date results

Question #5
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. Edge/on-device
B. Streaming
C. Batch
D. Real-time
E. None of these strategies will accomplish the task.

Solutions: