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

Topic	Details
Topic 1	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 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	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 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	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
Topic 10	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q89-Q94):

NEW QUESTION # 89
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"). 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.
B. 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.
C. 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.
D. 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.

Answer: B

Explanation:
Databricks Model Serving supports secure secret injection by referencing Databricks secret scopes directly in the served entity configuration. By mapping the secret to an environment variable using the {{secrets/scope/key}} syntax, the model can securely access the credential at runtime without exposing it in plain text. The PyFunc model can then read the environment variable and authenticate to the cloud provider's SDK, resolving the 401 error while following security best practices.

NEW QUESTION # 90
A Data Scientist needs to analyze drift detection results from Databricks Lakehouse Monitoring.
The system has generated both profile metrics and drift metrics tables. The scientist needs to identify baseline drift in numerical features by comparing current data against a baseline from 6 months ago. Which combination of table columns and values indicates baseline drift in a numerical feature?

A. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
B. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
C. log_type = "BASELINE" in profile metrics table with population_stability_index > 0.2 in drift metrics table.
D. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.

Answer: A

Explanation:
Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.

NEW QUESTION # 91
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

A. None of these
B. Kolmogorov-Smirnov (KS) test
C. Jenson-Shannon distance
D. Two-way Chi-squared Test
E. One-way Chi-squared Test

Answer: E

NEW QUESTION # 92
A Machine Learning Engineer is training a large-scale gradient boosting model using SparkML on a cluster of machines. The training job fails due to memory overflow on a single executor node after processing several iterations. The cluster resources are limited to executor nodes with 16 CPU cores and 64 GB RAM each. The engineer wants to continue training the model without changing hyperparameters or reducing the dataset size. They know Spark's architecture well and want to take advantage of its benefits. Which approach will allow the Machine Learning Engineer to solve this issue?

A. Increase the number of executor nodes and replicate the entire model on each node, then implement data parallelism to train on different mini-batches simultaneously.
B. Increase the number of executor nodes and implement model parallelism by splitting the gradient boosting model across executors so each node trains a part of the model.
C. Increase the number of executor nodes and implement data parallelism by partitioning the dataset across executors so each node trains the model on a subset of data.
D. Increase the number of executor nodes and replicate the entire model on each node, then apply model parallelism to train different parts of the model in parallel.

Answer: C

Explanation:
Spark ML algorithms, including gradient-boosted trees, are designed around data parallelism. By increasing the number of executor nodes, the dataset can be further partitioned so each executor processes only a subset of the data, reducing per-executor memory pressure while keeping the same model configuration and dataset size. This leverages Spark's distributed architecture without requiring model parallelism or hyperparameter changes.

NEW QUESTION # 93
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
B. Staging, Production, Archived
C. Production
D. None, Staging, Production, Archived
E. Staging, Production

Answer: E

NEW QUESTION # 94
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