Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Practice Questions and Exam Preparation

Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Questions & Answers

• Question 1:

  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. Option A
  B. Option B
  C. Option C
  D. Option D
  E. Option E
  E. Option E

• Question 2:

  Which of the following describes concept drift?

  A. Concept drift is when there is a change in the distribution of an input variable
  B. Concept drift is when there is a change in the distribution of a target variable
  C. Concept drift is when there is a change in the relationship between input variables and target variables
  D. Concept drift is when there is a change in the distribution of the predicted target given by the model
  E. None of these describe Concept drift
  C. Concept drift is when there is a change in the relationship between input variables and target variables

• Question 3:

  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. Option A
  B. Option B
  C. Option C
  D. Option D
  E. Option E
  E. Option E

• Question 4:

  Which of the following lists all of the model stages are available in the MLflow Model Registry?

  A. Development, Staging, Production
  B. None, Staging, Production
  C. Staging, Production, Archived
  D. None, Staging, Production, Archived
  E. Development, Staging, Production, Archived
  D. None, Staging, Production, Archived

• Question 5:

  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.format(“delta”).table(path).drop(“star_rating”)
  C. Delta tables cannot be modified
  D. spark.read.table(path).drop(“star_rating”)
  E. spark.sql(“SELECT * EXCEPT star_rating FROM path”)
  A. spark.read.format(“delta”).load(path).drop(“star_rating”)

• Question 6:

  After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which of the following SQL commands can be used to accomplish this task?

  A. VERSION
  B. DESCRIBE
  C. HISTORY
  D. DESCRIBE HISTORY
  E. TIMESTAMP
  D. DESCRIBE HISTORY

• Question 7:

  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 launch of a new cost-efficient SQL endpoint
  B. CI/CD pipelines are not needed for machine learning pipelines
  C. The arrival of a new feature table in the Feature Store
  D. The launch of a new cost-efficient job cluster
  E. The arrival of a new model version in the MLflow Model Registry
  E. The arrival of a new model version in the MLflow Model Registry

• Question 8:

  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_version
  C. client.update_registered_model
  D. client.delete_model
  E. client.delete_registered_model
  E. client.delete_registered_model

• Question 9:

  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:

  image9

  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_artifact
  B. log_model
  C. log_metric
  D. log_param
  E. There is no way to store values like this.
  C. log_metric

• Question 10:

  A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon

  investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.

  Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

  A. Z-Ordering
  B. Bin-packing
  C. Write as a Parquet file
  D. Data skipping
  E. Tuning the file size
  A. Z-Ordering