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Reference: https://www.databricks.com/learn/certification/machine-learning-professional
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Databricks Certified Machine Learning Professional Sample Questions:
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?
- A.
- B.
- C.
- D.
- E.
Correct Answer: E 🗳️
A Machine Learning Engineer has a computer vision model in Databricks Model Serving that obscures sensitive data from images. Internal teams use the model throughout the work week when they request access to new files. Recently model users complained that the model takes much longer in the morning. This coincides with when people arrive at work and request files for the day. When the engineer reviews the endpoint health metrics, they see P50 model latency peaks around 9AM at 20 seconds. Request rate also peaks at 9AM at 15 requests/second. The GPU utilization is over 60%, GPU memory usage over 50%, and provisioned concurrency at 4 throughout the day. What can the engineer do to reduce user wait time when request rate peaks at 9AM each morning?
- A. The endpoint is constrained by its ability to handle simultaneous requests. Enable "scale_to_zero" on the endpoint to spin up additional endpoints quickly to handle the requests.
- B. The endpoint is constrained by its ability to handle simultaneous requests. Scale the endpoint horizontally by editing the endpoint workload_size from "Small" to "Medium" to increase the model concurrency.
- C. The endpoint is constrained by the GPU. Scale the endpoint vertically by changing the endpoint workload_type from "GPU_SMALL" to "GPU_MEDIUM".
- D. The endpoint is constrained by its ability to handle simultaneous requests. Define a rate_1imit on the endpoint to spread out the requests over the course of the day.
Correct Answer: B 🗳️
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A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?
- A. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.
- B. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.
- C. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.
- D. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
Correct Answer: A 🗳️
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A data scientist has computed updated rows that contain new feature values for primary keys already stored in the Feature Store table features. The updated feature values are stored in the DataFrame features_df. They want to update the rows in features if the associated primary key is in features_df. If a row's primary key is not in features_df, they want the row to remain unchanged in features. Which code block can they use to perform this task using the Feature Store Client fs?
- A.
- B.
- C.
- D.
Correct Answer: B 🗳️
Explanation: Only visible for DumpsReview members. You can sign-up / login (it's free).
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which location in Databricks will show these data visualizations?
- A. The Artifacts section of the MLflow Experiment page
- B. The Figures section of the MLflow Run page
- C. Logged data visualizations cannot be viewed in Databricks
- D. The MLflow Model Registry Model paqe
- E. The Artifacts section of the MLflow Run page
Correct Answer: B 🗳️
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