Databricks-Machine-Learning-Professional無料問題、Databricks-Machine-Learning-Professional受験準備

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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 live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
トピック 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
トピック 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
トピック 4	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 5	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 6	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 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q45-Q50):

質問 # 45
A data scientist would like to switch from manually using MLflow logging to MLflow Autologging for all machine learning libraries used in a notebook.
They begin by adding mlflow.autolog()to the top of the below code block:

The data scientist is now trying to determine which line of code will kick off the MLflow Autologging process.
Which line of code within the above code block will start the MLflow Autologging process?

A. mlflow.sklearn.log_model(rf_model, "rf_model")
B. with mlflow.start_run(run_name="ML Project") as run:
C. mlflow.log_metric("training_mse", mse)
D. rf_model = rf.fit(X_train, y_train)

正解：D

解説：
The MLflow Autologging process is triggered when a model's training function is called. In this case, rf.fit(X_train, y_train) starts the autologging process because mlflow.autolog() automatically tracks parameters, metrics, and the model when a supported estimator (like RandomForestRegressor) is fitted. The context manager mlflow.start_run() only initiates an MLflow run, but the actual autologging begins during the .fit() execution.

質問 # 46
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?

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

正解：D

質問 # 47
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. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.
B. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.
C. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
D. 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.

正解：C

解説：
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.

質問 # 48
Which MLflow function logs files like plots or datasets?

A. mlflow.log_param()
B. mlflow.log_artifact()
C. mlflow.save_model()
D. mlflow.log_metric()

正解：B

解説：
Artifacts include:
charts
model files
datasets.

質問 # 49
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
B.
C.
D.
E.

正解：E

質問 # 50
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