Databricks Databricks-Machine-Learning-Professional試験勉強過去問 & Databricks-Machine-Learning-Professional受験料過去問

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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	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 2	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 3	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 4	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 5	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 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

>> Databricks Databricks-Machine-Learning-Professional試験勉強過去問 <<

試験Databricks-Machine-Learning-Professional試験勉強過去問 & 素晴らしいDatabricks-Machine-Learning-Professional受験料過去問 | 大人気Databricks-Machine-Learning-Professional認定デベロッパー

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q125-Q130):

質問 # 125
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A. Z-Ordering
B. Tuning the file size
C. Write as a Parquet file
D. Data skipping
E. Bin-packing

正解：B

質問 # 126
A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
They are using the following code block:

Which change does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace list with delete in the endpoint URL
B. There are no necessary changes
C. Replace DELETE with POST in the call to http_request
D. Replace list with webhooks in the endpoint URL

正解：A

解説：
To delete a webhook using the MLflow REST API, the correct endpoint is:
/api/2.0/mlflow/registry-webhooks/delete
In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.

質問 # 127
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

正解：B

質問 # 128
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data. Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

正解：C

質問 # 129
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

A. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
B. The version number of the model version in Production is necessary to complete this task.
C. https://<databricks-instance>/model/recommender/stage-production/invocations
D. https://<databricks-instance>/model/recommender/Production/invocations
E. https://<databricks-instance>/model-serving/recommender/Production/invocations

正解：B

質問 # 130
......

Databricks持ってきた製品があなたにふさわしくないと感じることはよくありますか？ Databricksラーニングガイドを使用することに決めた場合、問題に遭遇することは決してないことをお伝えしたいと思います。私たちの教材は、あなたが期待できないDatabricks-Machine-Learning-Professional高品質を持っています。学習教材のガイダンスで経験を積むと、以前よりもDatabricks-Machine-Learning-Professional時間を費やさず、明らかにDatabricks Certified Machine Learning Professional進歩を感じることができます。また、Databricksテストクイズは進歩に役立つことがわかります。

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