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

質問 1

A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?

（A）MLflow APIs

（B）Databricks REST APIs

（C）Jobs cannot be created programmatically

（D）AutoML APIs

（E）MLflow Client

正解：B 解答を投票する

質問 2

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

（A）Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

（B）Indent the child run blocks within the parent run block

（C）Provide the same name to the run name parameter for all three run blocks

（D）Remove the nested=True argument from the child runs

（E）Add the nested=True argument to the parent run

正解：A 解答を投票する

質問 3

Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?

（A）client.delete_registered_model

（B）client.delete_model

（C）client.update_registered_model

（D）client.delete_model_version

（E）client.transition_model_version_stage

正解：A 解答を投票する

質問 4

A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

（A）Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale

（B）All of these statements

（C）Because the streaming deployment is always on, there is no practitioner to debug poor model performance

（D）Because the streaming deployment is always on, all types of data must be handled without producing an error

（E）None of these statements

正解：A 解答を投票する

質問 5

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 6

A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

（A）There is no longer a need for pipeline-like machine learning objects

（B）The same preprocessing logic will automatically be applied when calling fit

（C）The same preprocessing logic will automatically be applied when calling predict

（D）This approach has no impact when loading the logged Pvfunc model for downstream deployment

（E）The pvfunc model can be used to deploy models in a parallelizable fashion

正解：A 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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