Use Databricks Machine Learning Professional Dumps to Obtain Databricks Certification

If you're serious about obtaining the ML Data Scientist certification, practicing with Databricks Databricks Machine Learning Professional dumps questions is a must. By doing so, you'll become familiar with the exam format, identify knowledge gaps, develop time management skills, boost your confidence, and increase your chances of success. So, start practicing Databricks Machine Learning Professional exam dumps today and give yourself the best chance of passing the Databricks Machine Learning Professional exam! Test free Databricks Certified Machine Learning Professional Databricks Machine Learning Professional exam dumps below.

Page 1 of 2

1. Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?

client.transition_model_version_stage

client.delete_model_version

client.update_registered_model

client.delete_model

client.delete_registered_model

 Loading...

2. 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?

Indent the child run blocks within the parent run block

Add the nested=True argument to the parent run

Remove the nested=True argument from the child runs

Provide the same name to the run name parameter for all three run blocks

Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

 Loading...

3. Which of the following describes label drift?

Label drift is when there is a change in the distribution of the predicted target given by the model

None of these describe label drift

Label drift is when there is a change in the distribution of an input variable

Label drift is when there is a change in the relationship between input variables and target variables

Label drift is when there is a change in the distribution of a target variable

 Loading...

4. 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()

 Loading...

5. 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?

Because the streaming deployment is always on, all types of data must be handled without producing an error

All of these statements

Because the streaming deployment is always on, there is no practitioner to debug poor model performance

Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale

None of these statements

 Loading...

6. Which of the following Databricks-managed MLflow capabilities is a centralized model store?

Models

Model Registry

Model Serving

Feature Store

Experiments

 Loading...

7. Which 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

 Loading...

8. 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

 Loading...

9. Which 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

 Loading...

10. 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?

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

 Loading...

Page 2 of 2

11. 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?

mlflow.spark.track_model(sklearn_model, "model")

mlflow.sklearn.log_model(sklearn_model, "model")

mlflow.spark.log_model(sklearn_model, "model")

mlflow.sklearn.load_model("model")

mlflow.sklearn.track_model(sklearn_model, "model")

 Loading...

12. 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

 Loading...

13. Which 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

 Loading...

14. 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?

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.

 Loading...

15. 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. At the same time, they would like to archive any model versions that are already in the Production stage.

Which of the following code blocks can they use to accomplish the task?

A)





B)





C)





D)

Option A

Option B

Option C

Option D

 Loading...

16. 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

 Loading...

17. 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?

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")

 Loading...

18. 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)





B)





C)

features_df.write.mode("fs").path("new_table")

D)

Option A

Option B

Option C

Option D

features_df.write.mode("feature").path("new_table")

 Loading...

 Loading...