2024 Latest Databricks-Machine-Learning-Professional DUMPS Q&As with Explanations Verified & Correct Answers
Databricks-Machine-Learning-Professional dumps Exam Material with 62 Questions
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NEW QUESTION # 35
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/Production/invocations
- B. The version number of the model version in Production is necessary to complete this task.
- C. https://<databricks-instance>/model/recommender/Production/invocations
- D. https://<databricks-instance>/model/recommender/stage-production/invocations
- E. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
Answer: B
NEW QUESTION # 36
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?
- A. MLflow
D Delta Lake - B. AutoML
- C. [Structured Streaming
- D. Spark UDFs
Answer: D
NEW QUESTION # 37
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?
- A. Replace formatfdelta") with format("stream")
- B. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
- C. Replace predict with a stream-friendly prediction function
- D. Replace "inference" with the path to the location of the Delta table
- E. Replace spark.read with spark.readStream
Answer: B
NEW QUESTION # 38
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?
- A. This can only be viewed in the MLflow Experiments UI
- B. client.pyfunc.load_model(model_uri)
- C. client.list_artifacts(run_id)["feature-importances.csv"]
- D. mlflow.sklearn.load_model(model_uri)
- E. mlflow.load_model(model_uri)
Answer: E
NEW QUESTION # 39
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?
- A. spark.read.format("mlflow-experiment").load(exp_id)
- B. spark.read.format("delta").load(exp_id)
- C. There is no way to programmatically return row-level results from an MLflow Experiment.
- D. client.list_run_infos(exp_id)
- E. mlflow.search_runs(exp_id)
Answer: B
NEW QUESTION # 40
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. Starting a testing job when a new model is registered
- B. Sending an email alert when an automated testing Job fails
- C. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- D. Sending a message to a Slack channel when a model version transitions stages
- E. None of these use cases require the use of an HTTP Webhook
Answer: C
NEW QUESTION # 41
Which of the following is a benefit of logging a model signature with an MLflow model?
- A. The model will have a unique identifier in the MLflow experiment
- B. The model can be deployed using real-time serving tools
- C. The schema of input data will be converted to match the signature
- D. The schema of input data can be validated when serving models
- E. The model will be secured by the user that developed it
Answer: C
NEW QUESTION # 42
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?
- A.
- B.
- C.
- D.
- E.
Answer: D
NEW QUESTION # 43
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:
Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?
- A. Replace list with webhooks in the endpoint URL
- B. There are no necessary changes
- C. Replace POST with PUT in the call to http request
- D. Replace list with view in the endpoint URL
- E. Replace POST with GET in the call to http request
Answer: A
NEW QUESTION # 44
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
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?
- A. mlflow.spark.log_model(sklearn_model, "model")
- B. mlflow.sklearn.log_model(sklearn_model, "model")
- C. mlflow.sklearn.track_model(sklearn_model, "model")
- D. mlflow.spark.track_model(sklearn_model, "model")
- E. mlflow.sklearn.load_model("model")
Answer: D
NEW QUESTION # 45
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A. client.transition_model_version_stage
- B. client.delete_model
- C. client.delete_model_version
- D. client.delete_registered_model
- E. client.update_registered_model
Answer: D
NEW QUESTION # 46
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?
- A. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
- B. Because the streaming deployment is always on, all types of data must be handled without producing an error
- C. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- D. None of these statements
- E. All of these statements
Answer: C
NEW QUESTION # 47
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?
- A.
- B.
- C.
- D.
- E. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
Answer: C
NEW QUESTION # 48
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?
- A. features_df.write.mode("fs").path("new_table")
- B.
- C.
- D. features_df.write.mode("feature").path("new_table")
- E.
Answer: C
NEW QUESTION # 49
A machine learning engineer has created a webhook with the following code block:
Which of the following code blocks will trigger this webhook to run the associate job?
- A.
- B.
- C.
- D.
- E.
Answer: B
NEW QUESTION # 50
Which of the following is a simple, low-cost method of monitoring numeric feature drift?
- A. Summary statistics trends
- B. Chi-squared test
- C. Jensen-Shannon test
- D. Kolmogorov-Smirnov (KS) test
- E. None of these can be used to monitor feature drift
Answer: A
NEW QUESTION # 51
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
- A. Batch
- B. Real-time
- C. None of these deployments
- D. On-device
- E. Streaming
Answer: E
NEW QUESTION # 52
A 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?
- A. There is no way to store values like this.
- B. log_model
- C. log_metric
- D. log_param
- E. log_artifact
Answer: E
NEW QUESTION # 53
A 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?
- A. Batch
- B. Streaming
- C. None of these strategies will meet the requirements.
- D. Edge/on-device
- E. Real-time
Answer: E
NEW QUESTION # 54
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?
- A. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df) - B. fs.score_batch(model_uri, spark_df)
- C. fs.score_model(model_uri, spark_df)
- D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - E. fs.score_batch(model_uri, df)
Answer: B
NEW QUESTION # 55
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?
- A. None. Staging. Production. Archived
- B. Staging. Production. Archived
- C. Staging. Production
- D. [None. Staging. Production
- E. Production
Answer: C
NEW QUESTION # 56
A 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?
- A. mlflow.models.schema.infer_schema
- B. mlflow.models.signature.infer_signature
- C. mlflow.models.Model.signature
- D. mlflow.models.Model.get_input_schema
- E. There is no way to obtain the input schema and the output schema of an unlogged model.
Answer: A
NEW QUESTION # 57
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?
- A. Replace update_registered_model with update_model_version
- B. There no changes necessary
- C. Add a Python model as an argument to update_registered_model
- D. Replace client.update_registered_model with mlflow
- E. Replace description with artifact
Answer: B
NEW QUESTION # 58
Which of the following lists all of the model stages are available in the MLflow Model Registry?
- A. None. Staging. Production. Archived
- B. Development. Staging. Production. Archived
- C. Staging. Production. Archived
- D. Development. Staging. Production
- E. None. Staging. Production
Answer: D
NEW QUESTION # 59
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