Updated Apr-2024 Test Engine to Practice Databricks-Machine-Learning-Professional Test Questions
Databricks-Machine-Learning-Professional Real Exam Questions Test Engine Dumps Training With 62 Questions
Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
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NEW QUESTION # 22
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 # 23
Which of the following describes the concept of MLflow Model flavors?
- A. A convention that deployment tools can use to wrap preprocessing logic into a Model
- B. A convention that MLflow Experiments can use to organize their Runs by project
- C. A convention that deployment tools can use to understand the model
- D. A convention that MLflow Model Registry can use to organize its Models by project
- E. A convention that MLflow Model Registry can use to version models
Answer: B
NEW QUESTION # 24
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?
- A. Containers
- B. Autoscaling clusters
- C. Cloud-based compute
- D. None of these tools
- E. REST APIs
Answer: C
NEW QUESTION # 25
A 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?
- A. mlflow.register_model(run_id, "best_model")
- B. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
- C. mlflow.register_model(model_uri, "best_model")
- D. mlflow.register_model(model_uri, "model")
- E. mlflow.register_model(f"runs:/{run_id}/model")
Answer: D
NEW QUESTION # 26
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A. client.delete_model
- B. client.delete_registered_model
- C. client.update_registered_model
- D. client.delete_model_version
- E. client.transition_model_version_stage
Answer: B
NEW QUESTION # 27
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. fs.score_batch(model_uri, df)
- B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df) - D. fs.score_model(model_uri, spark_df)
- E. fs.score_batch(model_uri, spark_df)
Answer: E
NEW QUESTION # 28
A 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?
- A. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- B. "MODEL_VERSION_TRANSITIONED_STAGE"
- C. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- D. "MODEL_VERSION_CREATED"
- E. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
Answer: E
NEW QUESTION # 29
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. [Structured Streaming
- C. Spark UDFs
- D. AutoML
Answer: C
NEW QUESTION # 30
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 whether they are deployed in batch, streaming, or real-time environments
- B. python_function can be used to store models in an MLmodel file
- C. python_function can be used to deploy models without worrying about which library was used to create the model
- D. python_function provides no benefits over the built-in library-specific model flavors
- E. python_function can be used to deploy models in a parallelizable fashion
Answer: E
NEW QUESTION # 31
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. client.list_run_infos(exp_id)
- C. mlflow.search_runs(exp_id)
- D. There is no way to programmatically return row-level results from an MLflow Experiment.
- E. spark.read.format("delta").load(exp_id)
Answer: E
NEW QUESTION # 32
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?
- A. client.log_artifact
- B. mlflow.log_figure
- C. mlflow.shap.log_explanation
- D. mlflow.shap
- E. None of these operations can accomplish the task.
Answer: D
NEW QUESTION # 33
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. There no changes necessary
- B. Replace description with artifact
- C. Replace update_registered_model with update_model_version
- D. Add a Python model as an argument to update_registered_model
- E. Replace client.update_registered_model with mlflow
Answer: A
NEW QUESTION # 34
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?
- A. Jobs cannot be created programmatically
- B. AutoML APIs
- C. Databricks REST APIs
- D. MLflow Client
- E. MLflow APIs
Answer: C
NEW QUESTION # 35
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.
- A. Option E
- B. Option D
- C. Option C
- D. Option B
- E. Option A
Answer: E
NEW QUESTION # 36
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?
- A. There is no way to store values like this.
- B. mlflow.log_model
- C. mlflow.log_param
- D. mlflow.log_metric
- E. mlflow.log_artifact
Answer: D
NEW QUESTION # 37
Which of the following describes concept drift?
- A. Concept drift is when there is a change in the distribution of a target variable
- B. None of these describe Concept drift
- C. Concept drift is when there is a change in the distribution of an input variable
- D. Concept drift is when there is a change in the distribution of the predicted target given by the model
- E. Concept drift is when there is a change in the relationship between input variables and target variables
Answer: D
NEW QUESTION # 38
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: A
NEW QUESTION # 39
Which 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?
- A. fs.get_table
- B. There is no way to accomplish this task with fs
- C. fs.create_table
- D. fs.write_table
- E. fs.read_table
Answer: C
NEW QUESTION # 40
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. Sending an email alert when an automated testing Job fails
- B. None of these use cases require the use of an HTTP Webhook
- C. Starting a testing job when a new model is registered
- D. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- E. Sending a message to a Slack channel when a model version transitions stages
Answer: D
NEW QUESTION # 41
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Model Registry
- B. Model Serving
- C. Experiments
- D. Feature Store
- E. Models
Answer: B
NEW QUESTION # 42
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.sklearn.load_model("model")
- B. mlflow.sklearn.log_model(sklearn_model, "model")
- C. mlflow.spark.track_model(sklearn_model, "model")
- D. mlflow.sklearn.track_model(sklearn_model, "model")
- E. mlflow.spark.log_model(sklearn_model, "model")
Answer: C
NEW QUESTION # 43
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