Databricks-Machine-Learning-Professional出題内容、Databricks-Machine-Learning-Professional資格参考書

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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Certificate Validity Period:	2 years
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Duration:	120 minutes
Real Exam Qty:	Approximately 45–60
Passing Score:	Not publicly disclosed
Exam Price:	$200 USD
Available Languages:	English
Exam Format:	Scenario-based questions, Multiple select, Multiple choice
Recommended Training:	Databricks Academy Machine Learning Training
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:	Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:	https://www.databricks.com/learn/certification

>> Databricks-Machine-Learning-Professional出題内容 <<

Databricks Databricks-Machine-Learning-Professional資格参考書、Databricks-Machine-Learning-Professional学習教材

当社の製品よりも高いプロファイルと低価格を備えた他の学習教材もあるかもしれませんが、Databricks-Machine-Learning-Professional学習教材の合格率は彼らのものよりもはるかに高いことを保証できます。そしてこれが最も重要です。以前のデータによると、Databricks-Machine-Learning-Professionalトレーニング質問を使用する人の98％〜99％が試験に合格しました。あなたが私たちに信頼を与えてくれるなら、私たちはあなたに成功を与えます。

Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
トピック 2	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
トピック 3	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
トピック 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
トピック 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
トピック 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
トピック 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
トピック 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
トピック 9	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
トピック 10	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q25-Q30):

質問 # 25
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").load(path).drop("star_rating")
B. spark.sql("SELECT * EXCEPT star_rating FROM path")
C. spark.read.format("delta").table(path).drop("star_rating")
D. spark.read.table(path).drop("star_rating")
E. Delta tables cannot be modified

正解：D

質問 # 26
A machine learning engineer has developed a machine learning pipeline that produces a scikit- learn model model and computes the RMSE rmse. MAE mae, and R-squared r2 values for the test set. They now want to log these values with the MLflow run. These values are stored in the dictionary metrics.
They run the following code block:

The code block produces an error.
Which changes to the code block will successfully complete the task?

A. Replace metrics with rmse, mae, r2
B. Replace mlflow.log_metric with mlflow.sklearn.log_metric
C. Replace metrics with model
D. Replace log_metric with log_metrics

正解：D

解説：
The method mlflow.log_metric() logs a single metric, while mlflow.log_metrics() is used to log multiple metrics at once from a dictionary. Since metrics is a dictionary containing rmse, mae, and r2, the correct function is mlflow.log_metrics(metrics).

質問 # 27
Why are Delta tables often used to store machine learning features?

A. They allow faster GPU training
B. They reduce model size
C. They replace Spark DataFrames
D. They support schema enforcement and time travel

正解：D

解説：
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.

質問 # 28
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(model_uri, "best_model")
B. mlflow.register_model(run_id, "best_model")
C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
D. mlflow.register_model(model_uri, "model")
E. mlflow.register_model(f"runs:/{run_id}/model")

正解：D

質問 # 29
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. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
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. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.

正解：D

解説：
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

質問 # 30
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