This page was exported from IT Certification Exam Braindumps [ http://blog.braindumpsit.com ] Export date:Sat Apr 12 14:32:02 2025 / +0000 GMT ___________________________________________________ Title: Updated Jun-2024 Databricks-Machine-Learning-Professional Exam Practice Test Questions [Q28-Q49] --------------------------------------------------- Updated Jun-2024 Databricks-Machine-Learning-Professional Exam Practice Test Questions Verified Databricks-Machine-Learning-Professional dumps Q&As 100% Pass in First Attempt Guaranteed Updated Dump Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics: TopicDetailsTopic 1Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deploymentsTopic 2Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavorsTopic 3Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to driftTopic 4Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelinesTopic 5Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhookTopic 6Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta tableTopic 7Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift andor label drift are likely to occurTopic 8Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment   QUESTION 28Which of the following is a benefit of logging a model signature with an MLflow model?  The model will have a unique identifier in the MLflow experiment  The schema of input data can be validated when serving models  The model can be deployed using real-time serving tools  The model will be secured by the user that developed it  The schema of input data will be converted to match the signature QUESTION 29A 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() QUESTION 30Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?  fs.create_table  fs.write_table  fs.get_table  There is no way to accomplish this task with fs  fs.read_table QUESTION 31A 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) QUESTION 32A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name “model”. Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name “best_model”.Which of the following lines of code can they use to register the model to the MLflow Model Registry?  mlflow.register_model(model_uri, “best_model”)  mlflow.register_model(run_id, “best_model”)  mlflow.register_model(f”runs:/{run_id}/best_model”, “model”)  mlflow.register_model(model_uri, “model”)  mlflow.register_model(f”runs:/{run_id}/model”) QUESTION 33A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:1. Deploy a model to production and compute predicted values2. Obtain the observed (actual) label values3. _____4. Run a statistical test to determine if there are changes over timeWhich of the following should be completed as Step #3?  Obtain the observed values (actual) feature values  Measure the latency of the prediction time  Retrain the model  None of these should be completed as Step #3  Compute the evaluation metric using the observed and predicted values QUESTION 34A 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”) QUESTION 35Which of the following is a simple statistic to monitor for categorical feature drift?  Mode  None of these  Mode, number of unique values, and percentage of missing values  Percentage of missing values  Number of unique values QUESTION 36A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.Which of the following deployment strategies can be used to meet these requirements?  Edge/on-device  Streaming  None of these strategies will meet the requirements.  Batch  Real-time QUESTION 37A 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 QUESTION 38Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?  Streaming  Batch  Edge/on-device  None of these strategies will accomplish the task.  Real-time QUESTION 39A 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 QUESTION 40A 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 QUESTION 41Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?  Starting a testing job when a new model is registered  Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage  Sending an email alert when an automated testing Job fails  None of these use cases require the use of an HTTP Webhook  Sending a message to a Slack channel when a model version transitions stages QUESTION 42A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.Which of the following MLflow operations can be used to perform this task?  mlflow.models.schema.infer_schema  mlflow.models.signature.infer_signature  mlflow.models.Model.get_input_schema  mlflow.models.Model.signature  There is no way to obtain the input schema and the output schema of an unlogged model. QUESTION 43A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.Which of the following locations in Databricks will show these data visualizations?  The MLflow Model Registry Model paqe  The Artifacts section of the MLflow Experiment page  Logged data visualizations cannot be viewed in Databricks  The Artifacts section of the MLflow Run page  The Figures section of the MLflow Run page QUESTION 44In 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 QUESTION 45A 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 QUESTION 46A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.They have the following incomplete code block:Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?  “MODEL_VERSION_CREATED”  “MODEL_VERSION_TRANSITIONED_TO_PRODUCTION”  “MODEL_VERSION_TRANSITIONED_TO_STAGING”  “MODEL_VERSION_TRANSITIONED_STAGE”  “MODEL_VERSION_TRANSITIONED_TO_STAGING”, “MODEL_VERSION_TRANSITIONED_TO_PRODUCTION” QUESTION 47Which 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 QUESTION 48A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.They write the following incomplete code block:Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?  log_artifact  log_model  log_metric  log_param  There is no way to store values like this. QUESTION 49Which 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  Loading … Pass ML Data Scientist Databricks-Machine-Learning-Professional Exam With 62 Questions: https://www.braindumpsit.com/Databricks-Machine-Learning-Professional_real-exam.html --------------------------------------------------- Images: https://blog.braindumpsit.com/wp-content/plugins/watu/loading.gif https://blog.braindumpsit.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-06-10 14:07:50 Post date GMT: 2024-06-10 14:07:50 Post modified date: 2024-06-10 14:07:50 Post modified date GMT: 2024-06-10 14:07:50