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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
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NEW QUESTION # 37
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. [None. Staging. Production
- C. Staging. Production
- D. Staging. Production. Archived
- E. Production
Answer: C
NEW QUESTION # 38
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 # 39
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?
- A. mlflow.pyfunc.read_model(model_uri)
- B. mlflow.sklearn.load_model(model_uri)
- C. mlflow.sklearn.read_model(model_uri)
- D. mlflow.spark.load_model(model_uri)
- E. mlflow.pyfunc.load_model(model_uri)
Answer: E
NEW QUESTION # 40
Which of the following statements describes streaming with Spark as a model deployment strategy?
- A. The inference of batch processed records as soon as a trigger is hit
- B. The inference of incrementally processed records as soon as trigger is hit
- C. The inference of all types of records in real-time
- D. The inference of incrementally processed records as soon as a Spark job is run
- E. The inference of batch processed records as soon as a Spark job is run
Answer: D
NEW QUESTION # 41
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, all types of data must be handled without producing an error
- B. None of these statements
- C. All of these statements
- D. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- E. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
Answer: D
NEW QUESTION # 42
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. client.list_run_infos(exp_id)
- B. mlflow.search_runs(exp_id)
- C. There is no way to programmatically return row-level results from an MLflow Experiment.
- D. spark.read.format("delta").load(exp_id)
- E. spark.read.format("mlflow-experiment").load(exp_id)
Answer: D
NEW QUESTION # 43
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?
- A. Ensuring that a built-in model flavor is used for the model logging
- B. Starting a manual child run within the objective function
- C. Startinq a manual parent run before calling fmin
- D. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
- E. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
Answer: C
NEW QUESTION # 44
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. fs.score_batch(model_uri, spark_df)
- C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df) - E. fs.score_model(model_uri, spark_df)
Answer: B
NEW QUESTION # 45
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?
- A. REST APIs
- B. None of these tools
- C. Cloud-based compute
- D. Autoscaling clusters
- E. Containers
Answer: C
NEW QUESTION # 46
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. Sending a message to a Slack channel when a model version transitions stages
- C. Starting a testing job when a new model is registered
- D. None of these use cases require the use of an HTTP Webhook
- E. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
Answer: E
NEW QUESTION # 47
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?
- A. The arrival of a new feature table in the Feature Store
- B. CI/CD pipelines are not needed for machine learning pipelines
- C. The launch of a new cost-efficient job cluster
- D. The launch of a new cost-efficient SQL endpoint
- E. The arrival of a new model version in the MLflow Model Registry
Answer: C
NEW QUESTION # 48
Which of the following is a simple, low-cost method of monitoring numeric feature drift?
- A. Chi-squared test
- B. Kolmogorov-Smirnov (KS) test
- C. Jensen-Shannon test
- D. None of these can be used to monitor feature drift
- E. Summary statistics trends
Answer: E
NEW QUESTION # 49
A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?
- A. spark.read.format("delta").load(path).drop("star_rating")
- B. spark.read.table(path).drop("star_rating")
- C. Delta tables cannot be modified
- D. spark.read.format("delta").table(path).drop("star_rating")
- E. spark.sql("SELECT * EXCEPT star_rating FROM path")
Answer: B
NEW QUESTION # 50
A 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 values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?
- A. None of these should be completed as Step #3
- B. Retrain the model
- C. Obtain the observed values (actual) feature values
- D. Measure the latency of the prediction time
- E. Compute the evaluation metric using the observed and predicted values
Answer: A
NEW QUESTION # 51
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. MLflow APIs
- B. Jobs cannot be created programmatically
- C. Databricks REST APIs
- D. MLflow Client
- E. AutoML APIs
Answer: C
NEW QUESTION # 52
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 C
- B. Option D
- C. Option A
- D. Option B
- E. Option E
Answer: C
NEW QUESTION # 53
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. log_model
- B. There is no way to store values like this.
- C. log_artifact
- D. log_param
- E. log_metric
Answer: C
NEW QUESTION # 54
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:
The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?
- A. Indent the child run blocks within the parent run block
- B. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
- C. Add the nested=True argument to the parent run
- D. Remove the nested=True argument from the child runs
- E. Provide the same name to the run name parameter for all three run blocks
Answer: B
NEW QUESTION # 55
Which of the following lists all of the model stages are available in the MLflow Model Registry?
- A. None. Staging. Production. Archived
- B. None. Staging. Production
- C. Development. Staging. Production
- D. Staging. Production. Archived
- E. Development. Staging. Production. Archived
Answer: C
NEW QUESTION # 56
Which of the following describes label drift?
- A. Label drift is when there is a change in the distribution of a target variable
- B. Label drift is when there is a change in the relationship between input variables and target variables
- C. Label drift is when there is a change in the distribution of an input variable
- D. None of these describe label drift
- E. Label drift is when there is a change in the distribution of the predicted target given by the model
Answer: C
NEW QUESTION # 57
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: B
NEW QUESTION # 58
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Experiments
- B. Feature Store
- C. Models
- D. Model Registry
- E. Model Serving
Answer: E
NEW QUESTION # 59
A 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?
- A. The Artifacts section of the MLflow Experiment page
- B. Logged data visualizations cannot be viewed in Databricks
- C. The MLflow Model Registry Model paqe
- D. The Figures section of the MLflow Run page
- E. The Artifacts section of the MLflow Run page
Answer: D
NEW QUESTION # 60
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