Databricks-Machine-Learning-Professional無料問題集「Databricks Certified Machine Learning Professional」

質問 1

Which of the following describes concept drift?

（A）Concept drift is when there is a change in the distribution of the predicted target given by the model

（B）Concept drift is when there is a change in the relationship between input variables and target variables

（C）Concept drift is when there is a change in the distribution of a target variable

（D）Concept drift is when there is a change in the distribution of an input variable

（E）None of these describe Concept drift

正解：A 解答を投票する

質問 2

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

（A）All of these reasons

（B）JS is not normalized or smoothed

（C）JS is more robust when working with large datasets

（D）JS does not require any manual threshold or cutoff determinations

（E）None of these reasons

正解：C 解答を投票する

質問 3

A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?

（A）

（B）

（C）

（D）

正解：D 解答を投票する

質問 4

A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

（A）features_df.write.mode("feature").path("new_table")

（B）

（C）

（D）

（E）features_df.write.mode("fs").path("new_table")

正解：D 解答を投票する

質問 5

Which of the following is a simple statistic to monitor for categorical feature drift?

（A）Mode, number of unique values, and percentage of missing values

（B）Mode

（C）None of these

（D）Percentage of missing values

（E）Number of unique values

正解：A 解答を投票する

質問 6

A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?

（A）Edge/on-device

（B）Streaming

（C）Batch

（D）None of these strategies will meet the requirements.

（E）Real-time

正解：E 解答を投票する

Databricks-Machine-Learning-Professional 無料問題集「Databricks Certified Machine Learning Professional」

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