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[General] Databricks's Exam Questions for Databricks-Machine-Learning-Professional Ensure

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【General】 Databricks's Exam Questions for Databricks-Machine-Learning-Professional Ensure

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
TopicDetails
Topic 1
  • Identify that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment
Topic 2
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 3
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 4
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 6
  • Test whether the updated model performs better on the more recent data
  • Identify when retraining and deploying an updated model is a probable solution to drift
Topic 7
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors

Databricks Certified Machine Learning Professional Sample Questions (Q169-Q174):NEW QUESTION # 169
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?
  • A. Retrain the model
  • B. Obtain the observed values (actual) feature values
  • C. Compute the evaluation metric using the observed and predicted values
  • D. Measure the latency of the prediction time
  • E. None of these should be completed as Step #3
Answer: E

NEW QUESTION # 170
Which of the following is an obstacle related to streaming machine learning applications?
  • A. None of these
  • B. All of these
  • C. Out-of-order data
  • D. End-to-end fault tolerance
Answer: B
Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 171
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. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.
  • C. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
  • D. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
Answer: C
Explanation:
Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.

NEW QUESTION # 172
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?
  • A. HISTORY
  • B. TIMESTAMP
  • C. DESCRIBE HISTORY
  • D. DESCRIBE
  • E. VERSION
Answer: C

NEW QUESTION # 173
Which MLflow function logs files like plots or datasets?
  • A. mlflow.log_artifact()
  • B. mlflow.log_metric()
  • C. mlflow.save_model()
  • D. mlflow.log_param()
Answer: A
Explanation:
Artifacts include:
charts
model files
datasets.

NEW QUESTION # 174
......
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