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Databricks-Machine-Learning-Professional Exam Cram - Databricks-Machine-Learning-Professional Valid Vce Dumps

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic
Details

Topic 1

Identify live serving benefits of querying precomputed batch predictions
Describe Structured Streaming as a common processing tool for ETL pipelines

Topic 2

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

Topic 3

Identify which code block will trigger a shown webhook
Describe the basic purpose and user interactions with Model Registry

Topic 4

Identify the requirements for tracking nested runs
Describe an MLflow flavor and the benefits of using MLflow flavors

Topic 5

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

Topic 6

Describe concept drift and its impact on model efficacy
Describe summary statistic monitoring as a simple solution for numeric feature drift

Topic 7

Describe model serving deploys and endpoint for every stage
Identify scenarios in which feature drift and
or label drift are likely to occur

Topic 8

Describe the advantages of using the pyfunc MLflow flavor
Manually log parameters, models, and evaluation metrics using MLflow

Topic 9

Create, overwrite, merge, and read Feature Store tables in machine learning workflows
View Delta table history and load a previous version of a Delta table

>> Databricks-Machine-Learning-Professional Exam Cram <<

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Databricks Certified Machine Learning Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function can be used to deploy models without worrying about which library was used to create the model
B. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
C. python_function can be used to deploy models in a parallelizable fashion
D. python_function can be used to store models in an MLmodel file
E. python_function provides no benefits over the built-in library-specific model flavors

Answer: C

NEW QUESTION # 41
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/recommender/Production/invocations
B. The version number of the model version in Production is necessary to complete this task.
C. https://<databricks-instance>/model-serving/recommender/Production/invocations
D. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
E. https://<databricks-instance>/model/recommender/stage-production/invocations

Answer: B

NEW QUESTION # 42
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. JS does not require any manual threshold or cutoff determinations
B. None of these reasons
C. JS is not normalized or smoothed
D. All of these reasons
E. JS is more robust when working with large datasets

Answer: E

NEW QUESTION # 43
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Batch serving has built-in capabilities in Databricks Machine Learning
B. There is no advantage to using batch serving deployments over real-time serving deployments
C. Querying stored predictions can be faster than computing predictions in real-time
D. Testing is not possible in real-time serving deployments
E. Computing predictions in real-time provides more up-to-date results

Answer: A

NEW QUESTION # 44
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?

Answer: D

NEW QUESTION # 45
......

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