New Databricks-Machine-Learning-Professional Real Exam & Updated Databricks-Machine-Learning-Professional Dumps
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Databricks Certified Machine Learning Professional Sample Questions (Q188-Q193):
NEW QUESTION # 188
A data scientist wants to log outlier feature data from a CSV file at path outlier_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?
- A. mlflow.log_data(outlier_path, "outlier-features.csv")
- B.

- C. mlflow.log_artifact(outlier_path, "outlier-features.csv")
- D.

Answer: C
Explanation:
To log external files, such as a CSV containing outlier feature data, MLflow provides the mlflow.log_artifact() function. This function uploads the specified file or directory (outlier_path) as an artifact under the provided artifact path ("outlier-features.csv"). It is the correct way to associate data files with an MLflow run, whereas mlflow.log_model() is reserved for logging model objects, not arbitrary data.
NEW QUESTION # 189
A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
They are using the following code block:
Which change does the machine learning engineer need to make to this code block so it will successfully accomplish the task?
- A. There are no necessary changes
- B. Replace DELETE with POST in the call to http_request
- C. Replace list with delete in the endpoint URL
- D. Replace list with webhooks in the endpoint URL
Answer: C
Explanation:
To delete a webhook using the MLflow REST API, the correct endpoint is:
/api/2.0/mlflow/registry-webhooks/delete
In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.
NEW QUESTION # 190
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?
- A. There is no way to programmatically return row-level results from an MLflow Experiment.
- B. spark.read.format("delta").load(exp_id)
- C. mlflow.search_runs(exp_id)
- D. spark.read.format("mlflow-experiment").load(exp_id)
- E. client.list_run_infos(exp_id)
Answer: B
NEW QUESTION # 191
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?
- A. Statistical tests
- B. None of these can provide more robust drift monitoring than summary statistics
- C. Oversampling
- D. Correlations
Answer: A
Explanation:
Statistical tests (such as the Kolmogorov-Smirnov test or Wasserstein distance) provide a more robust and quantitative method for detecting numeric feature drift compared to simple summary statistics. These tests compare the full distributions of features between datasets, making them more sensitive to subtle changes in data behavior over time.
NEW QUESTION # 192
Why is Apache Spark useful for machine learning training?
- A. Web serving
- B. GPU rendering
- C. Data visualization
- D. Distributed data processing
Answer: D
Explanation:
Spark processes large distributed datasets efficiently.
NEW QUESTION # 193
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