Databricks-Machine-Learning-Professional最新考證，Databricks-Machine-Learning-Professional考試

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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Real Exam Qty:	59
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Price:	$200 USD
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Exam Format:	Multi-select, Multiple choice
Passing Score:	Not publicly disclosed (approximately 70%)
Recommended Training:	Advanced Machine Learning Operations Machine Learning at Scale
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

>> Databricks-Machine-Learning-Professional最新考證 <<

Databricks-Machine-Learning-Professional考試，Databricks-Machine-Learning-Professional認證

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

主題	簡介
主題 1	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
主題 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
主題 3	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 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
主題 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
主題 6	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 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

最新的 ML Data Scientist Databricks-Machine-Learning-Professional 免費考試真題 (Q51-Q56):

問題 #51
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)
B. fs.score_batch(model_uri, df)
C. fs.score_batch(model_uri, spark_df)
D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
E. fs.score_model(model_uri, spark_df)

答案：C

問題 #52
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. log_model
B. log_artifact
C. log_param
D. There is no way to store values like this.
E. log_metric

答案：B

問題 #53
A Data Scientist is preparing a Spark ML pipeline on a customer dataset with numeric features age, annual_income, and transaction_count, each varying widely. Because the chosen algorithm requires inputs normalized to the [0,1] range, they need to apply the appropriate Spark ML transformer to these features. Which Spark ML transformer should the Data Scientist use to scale all features to the [0,1] range?

A. StandardScaler
B. RobustScaler
C. MinMaxScaler
D. MaxAbsScaler

答案：C

解題說明：
MinMaxScaler rescales each numeric feature to a fixed range, typically [0, 1], by subtracting the minimum value and dividing by the feature's range. This makes it the appropriate Spark ML transformer when an algorithm explicitly requires inputs normalized to the [0,1] interval.

問題 #54
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.sklearn.load_model("model")
B. mlflow.sklearn.log_model(sklearn_model, "model")
C. mlflow.spark.track_model(sklearn_model, "model")
D. mlflow.sklearn.track_model(sklearn_model, "model")
E. mlflow.spark.log_model(sklearn_model, "model")

答案：C

問題 #55
A Data Scientist is tasked with developing models to forecast product demand. The company offers 5000 different product types, and the Data Scientist must generate weekly forecasts for each type. They have access to two years of historical purchase data and are given ample project budget.
For their next project, they want to build 5000 separate Random Forest models, one for each product type. They aim to train all the models as quickly as possible with minimal setup.
Which approach meets these requirements?

A. Create a Databricks Workflow with 5000 tasks. Each task is configured to accept a product ID as a parameter which will then train a model based on the specified product ID.
B. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
C. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.
D. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.

答案：B

解題說明：
The pandas function API with grouped map allows data to be grouped by product type and applies a custom training function independently to each group. This approach enables massive parallelism across the cluster with minimal orchestration or setup, making it well suited for rapidly training thousands of independent models in parallel.

問題 #56
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