Databricks-Machine-Learning-Professional套裝 - Databricks-Machine-Learning-Professional考試指南

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Databricks Databricks-Machine-Learning-Professional 考試大綱：

主題	簡介
主題 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
主題 2	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
主題 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
主題 4	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
主題 5	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
主題 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
主題 7	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
主題 8	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
主題 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
主題 10	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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最新的 ML Data Scientist Databricks-Machine-Learning-Professional 免費考試真題 (Q168-Q173):

問題 #168
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches. Which tool can be used to provide this type of continuous processing?

A. Spark UDFs
B. Structured Streaming
C. MLflow
D. Delta Lake

答案：A

問題 #169
A Machine Learning Engineer is responsible for maintaining a fraud detection model deployed on Databricks. They want to implement a retraining pipeline that automatically starts when the model's F1 score drops below a threshold or when input feature distributions change significantly.
Which two actions should the engineer take to implement this automated retraining? (Choose two.)

A. Schedule a recurring query on the Lakehouse monitoring table.
B. Set up a manual retraining schedule to run every week regardless of alerts.
C. Configure these alerts to send webhook notifications that trigger the model training job.
D. Use Databricks SQL to create alerts on model performance and data drift metrics stored in Delta tables.
E. Use MLflow to manually log metrics and retrain the model offline.

答案：C,D

解題說明：
Databricks Lakehouse Monitoring stores model performance and data drift metrics in Delta tables, which can be monitored using Databricks SQL alerts. By creating alerts on F1 score degradation or significant feature drift and configuring those alerts to send webhook notifications, the engineer can automatically trigger a retraining job whenever predefined conditions are met, enabling event-driven, automated retraining aligned with MLOps best practices.

問題 #170
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

A. mlflow.pyfunc.load_model(model_uri)
B. mlflow.spark.read_model(model_uri)
C. mlflow.spark.load_model(model_uri)
D. mlflow.sklearn.load_model(model_uri)

答案：C

解題說明：
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.

問題 #171
A Machine Learning Engineer is setting up Databricks Lakehouse Monitoring to issue alerts based on IoT sensors that monitor various water conditions at a high tech hydroponic farm.
Specifically:
- pH sensors
- Records acidity/alkalinity on a logarithmic scale from 0 to 14
- Pump status monitor
- Records the current status of the pump as "on", "off", "maintenance",
"fault", or "clean"
- Electrical Conductivity (EC) sensors
- Records nutrient level of the water as "very rich", "rich", "medium",
"poor", "very poor"
What are the appropriate drift metrics for each of the given sensor types?

A. pH: Kolmogorov-Smirnov (KS), pump status: Chi-squared, EC sensor: Jensen-Shannon distance.
B. pH: Chi-squared, pump status: Kolmogorov-Smirnov (KS), EC sensor: Wasserstein distance.
C. pH: Jensen-Shannon distance, pump status: Wasserstein distance, EC sensor: Kolmogorov- Smirnov (KS).
D. pH: Jenson-Shannon distance, pump status: Kolmogorov-Smirnov (KS), EC sensor: Chi-squared.

答案：A

解題說明：
pH readings are continuous numerical values, so the Kolmogorov-Smirnov test is appropriate for detecting distribution shifts. Pump status is a nominal categorical variable, making the chi- squared test suitable for identifying changes in category frequencies. Electrical Conductivity levels are categorical with ordered labels, and Jensen-Shannon distance effectively measures distributional changes in such categorical probability distributions.

問題 #172
A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?

A. spark.read.format("delta").table(path).drop("star_rating")
B. spark.sql("SELECT * EXCEPT star_rating FROM path")
C. spark.read.table(path).drop("star_rating")
D. spark.read.format("delta").load(path).drop("star_rating")
E. Delta tables cannot be modified

答案：C

問題 #173
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