This page was exported from Exam for engine [ http://blog.test4engine.com ] Export date:Mon Nov 18 2:51:24 2024 / +0000 GMT ___________________________________________________ Title: Pass Your Next Databricks-Machine-Learning-Professional Certification Exam Easily & Hassle Free [Q10-Q26] --------------------------------------------------- Pass Your Next Databricks-Machine-Learning-Professional Certification Exam Easily & Hassle Free Free Databricks Databricks-Machine-Learning-Professional Exam Question Practice Exams Q10. A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model.Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?  Staging. Production. Archived  Production  None. Staging. Production. Archived  Staging. Production  [None. Staging. Production Q11. 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 Q12. 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 Q13. After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.Which of the following SQL commands can be used to accomplish this task?  VERSION  DESCRIBE  HISTORY  DESCRIBE HISTORY  TIMESTAMP Q14. A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model “project”. The machine learning engineer would like to add a new version of the model to “project”.Which of the following MLflow operations can the machine learning engineer use to accomplish this task?  mlflow.register_model  MlflowClient.update_registered_model  mlflow.add_model_version  MlflowClient.get_model_version  The machine learning engineer needs to create an entirely new MLflow Model Registry model Q15. A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.Which of the following lines of code can they use to accomplish this task?  mlflow.sklearn.autolog()  mlflow.spark.autolog()  spark.conf.set(“autologging”, True)  It is not possible to automatically log MLflow runs.  mlflow.autolog() Q16. Which of the following is a simple, low-cost method of monitoring numeric feature drift?  Jensen-Shannon test  Summary statistics trends  Chi-squared test  None of these can be used to monitor feature drift  Kolmogorov-Smirnov (KS) test Q17. 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?  All of these statements  Because the streaming deployment is always on, there is no practitioner to debug poor model performance  None of these statements  Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale  Because the streaming deployment is always on, all types of data must be handled without producing an error Q18. A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.Which of the following operations can be used to load the model as an sklearn object for batch deployment?  mlflow.spark.load_model(model_uri)  mlflow.pyfunc.read_model(model_uri)  mlflow.sklearn.read_model(model_uri)  mlflow.pyfunc.load_model(model_uri)  mlflow.sklearn.load_model(model_uri) Q19. 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 Q20. In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?  The launch of a new cost-efficient SQL endpoint  CI/CD pipelines are not needed for machine learning pipelines  The arrival of a new feature table in the Feature Store  The launch of a new cost-efficient job cluster  The arrival of a new model version in the MLflow Model Registry Q21. 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.       Q22. A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.Which of the following code blocks will accomplish this task inside of an existing MLflow run block?A)B)C) mlflow.log_data(importance_path, “feature-importance.csv”)D) mlflow.log_artifact(importance_path, “feature-importance.csv”)E) None of these code blocks tan accomplish the task.  Option A  Option B  Option C  Option D  Option E Q23. A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project “model” and they’d like to add the text in the model_description variable.The team is using the following line of code:Which of the following changes does the team need to make to the above code block to accomplish the task?  Replace update_registered_model with update_model_version  There no changes necessary  Replace description with artifact  Replace client.update_registered_model with mlflow  Add a Python model as an argument to update_registered_model Q24. A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?  client.list_run_infos(exp_id)  spark.read.format(“delta”).load(exp_id)  There is no way to programmatically return row-level results from an MLflow Experiment.  mlflow.search_runs(exp_id)  spark.read.format(“mlflow-experiment”).load(exp_id) Q25. Which of the following describes concept drift?  Concept drift is when there is a change in the distribution of an input variable  Concept drift is when there is a change in the distribution of a target variable  Concept drift is when there is a change in the relationship between input variables and target variables  Concept drift is when there is a change in the distribution of the predicted target given by the model  None of these describe Concept drift Q26. A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.Which of the following tools can the machine learning engineer use to assess their theory?  Kolmogorov-Smirnov (KS) test  One-way Chi-squared Test  Two-way Chi-squared Test  Jenson-Shannon distance  None of these  Loading … Ace Databricks-Machine-Learning-Professional Certification with 62 Actual 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: 2024-03-08 11:08:53 Post date GMT: 2024-03-08 11:08:53 Post modified date: 2024-03-08 11:08:53 Post modified date GMT: 2024-03-08 11:08:53