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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 2
  • Identify that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment
Topic 3
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 4
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5
  • Identify a use case for HTTP webhooks and where the Webhook URL needs to come
  • Identify advantages of using Job clusters over all-purpose clusters
Topic 6
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 8
  • Create, overwrite, merge, and read Feature Store tables in machine learning workflows
  • View Delta table history and load a previous version of a Delta table
Topic 9
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 10
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 11
  • Test whether the updated model performs better on the more recent data
  • Identify when retraining and deploying an updated model is a probable solution to drift

Reference: https://www.databricks.com/learn/certification/machine-learning-professional

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Databricks Certified Machine Learning Professional Sample Questions:

A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company's cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider's Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
"cloud_creds" with key "db_key". These credentials can be used to authenticate to the cloud provider's SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

  • A. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Log the model to MLflow with the access token value as a custom MLflow artifact.
    Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider's SDK.
  • B. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Add "environment_vars":
    {"DB_ACCESS_TOKEN": ""} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
  • C. Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
    /.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.
  • D. Add "environment_vars": {"DB_ACCESS_TOKEN": "{{secrets/cloud_creds/db_key}}"} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).

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. mlflow.log_artifact
  • B. mlflow.log_metric
  • C. mlflow.log_model
  • D. mlflow.log_param
  • E. There is no way to store values like this.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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 lines of code can be used to fill in the blank so that the code block accomplishes the task?

  • A. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
  • B. "MODEL_VERSION_CREATED"
  • C. "MODEL_VERSION_TRANSITIONED_STAGE"
  • D. "MODEL_VERSION_TRANSITIONED_TO_STAGING",
    "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
  • E. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

A Data Scientist has trained a regression model using Spark's MLlib to predict housing prices.
The model needs to be evaluated to determine how well it predicts on a testing dataset. The Data Scientist wants to use a standard regression metric for the evaluation. They also want to evaluate the model with the least processing overhead required. Which approach will suit their needs?

  • A. Implement a Spark UDF to calculate the root mean squared error for each row in the testing dataset and aggregate these values to obtain the overall RMSE score.
  • B. Convert the testing dataset to a pandas DataFrame and use scikit-learn's root_mean_squared_error function to calculate the root mean squared error on the testing dataset.
  • C. Use the MLlib's RankingEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
  • D. Use the MLlib's RegressionEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).

A data scientist would like to switch from manually using MLflow logging to MLflow Autologging for all machine learning libraries used in a notebook.
They begin by adding mlflow.autolog()to the top of the below code block:

The data scientist is now trying to determine which line of code will kick off the MLflow Autologging process.
Which line of code within the above code block will start the MLflow Autologging process?

  • A. with mlflow.start_run(run_name="ML Project") as run:
  • B. mlflow.log_metric("training_mse", mse)
  • C. mlflow.sklearn.log_model(rf_model, "rf_model")
  • D. rf_model = rf.fit(X_train, y_train)
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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