This page was exported from Exam for engine [ http://blog.test4engine.com ] Export date:Mon Nov 18 3:34:51 2024 / +0000 GMT ___________________________________________________ Title: Latest Databricks-Machine-Learning-Professional Actual Free Exam Updated 62 Questions [Q28-Q44] --------------------------------------------------- Latest Databricks-Machine-Learning-Professional Actual Free Exam Updated 62 Questions Online Questions - Valid Practice Databricks-Machine-Learning-Professional Exam Dumps Test Questions Q28. Which of the following describes label drift?  Label drift is when there is a change in the distribution of the predicted target given by the model  None of these describe label drift  Label drift is when there is a change in the distribution of an input variable  Label drift is when there is a change in the relationship between input variables and target variables  Label drift is when there is a change in the distribution of a target variable Q29. Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?  client.transition_model_version_stage  client.delete_model_version  client.update_registered_model  client.delete_model  client.delete_registered_model Q30. Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?  python_function provides no benefits over the built-in library-specific model flavors  python_function can be used to deploy models in a parallelizable fashion  python_function can be used to deploy models without worrying about which library was used to create the model  python_function can be used to store models in an MLmodel file  python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments Q31. 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?      features_df.write.mode(“fs”).path(“new_table”)    features_df.write.mode(“feature”).path(“new_table”) Q32. Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?  The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application’s current scenario  The context parameter allows the user to document the performance of a model after it has been deployed  The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model  The context parameter allows the user to provide the model with completely custom if-else logic for the given application’s current scenario  The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files Q33. Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?  All of these reasons  JS is not normalized or smoothed  None of these reasons  JS is more robust when working with large datasets  JS does not require any manual threshold or cutoff determinations Q34. 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?    It is not possible to deploy a scikit-learn model on a Spark DataFrame.       Q35. Which of the following lists all of the model stages are available in the MLflow Model Registry?  Development. Staging. Production  None. Staging. Production  Staging. Production. Archived  None. Staging. Production. Archived  Development. Staging. Production. Archived Q36. A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?  df = fs.get_missing_features(spark_df, model_uri)fs.score_model(model_uri, df)  fs.score_model(model_uri, spark_df)  df = fs.get_missing_features(spark_df, model_uri)fs.score_batch(model_uri, df)df = fs.get_missing_features(spark_df)  fs.score_batch(model_uri, df)  fs.score_batch(model_uri, spark_df) Q37. A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.Which of the following tools can be used to provide this type of continuous processing?  Spark UDFs  [Structured Streaming  MLflowD Delta Lake  AutoML Q38. Which of the following Databricks-managed MLflow capabilities is a centralized model store?  Models  Model Registry  Model Serving  Feature Store  Experiments Q39. Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?  mlflow.shap.log_explanation  None of these operations can accomplish the task.  mlflow.shap  mlflow.log_figure  client.log_artifact Q40. A machine learning engineer is using the following code block as part of a batch deployment pipeline:Which of the following changes needs to be made so this code block will work when the inference table is a stream source?  Replace “inference” with the path to the location of the Delta table  Replace schema(schema) with option(“maxFilesPerTriqqer”, 1}  Replace spark.read with spark.readStream  Replace formatfdelta”) with format(“stream”)  Replace predict with a stream-friendly prediction function Q41. 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.Which of the following code blocks can they use to accomplish the task?           Q42. 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?  The pvfunc model can be used to deploy models in a parallelizable fashion  The same preprocessing logic will automatically be applied when calling fit  The same preprocessing logic will automatically be applied when calling predict  This approach has no impact when loading the logged Pvfunc model for downstream deployment  There is no longer a need for pipeline-like machine learning objects Q43. Which of the following is a probable response to identifying drift in a machine learning application?  All of these responses  Sunsetting the machine learning application  Rebuilding the machine learning application with a new label variable  Retraining and deploying a model on more recent data  None of these responses Q44. 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?  https://<databricks-instance>/model-serving/recommender/Production/invocations  The version number of the model version in Production is necessary to complete this task.  https://<databricks-instance>/model/recommender/stage-production/invocations  https://<databricks-instance>/model-serving/recommender/stage-production/invocations  https://<databricks-instance>/model/recommender/Production/invocations  Loading … Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics: TopicDetailsTopic 1Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavorsTopic 2Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature driftTopic 3Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clustersTopic 4Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift andor label drift are likely to occurTopic 5Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deploymentTopic 6Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deploymentsTopic 7Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta tableTopic 8Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow   Databricks-Machine-Learning-Professional Exam PDF [2024] Tests Free Updated Today with Correct 62 Questions: https://www.test4engine.com/Databricks-Machine-Learning-Professional_exam-latest-braindumps.html --------------------------------------------------- Images: https://blog.test4engine.com/wp-content/plugins/watu/loading.gif https://blog.test4engine.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-06-27 13:42:37 Post date GMT: 2024-06-27 13:42:37 Post modified date: 2024-06-27 13:42:37 Post modified date GMT: 2024-06-27 13:42:37