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| Topic | Details |
|---|
| Topic 1 | - Describe concept drift and its impact on model efficacy
- Describe summary statistic monitoring as a simple solution for numeric feature drift
|
| Topic 2 | - Identify JIT feature values as a need for real-time deployment
- Describe how to list all webhooks and how to delete a webhook
|
| Topic 3 | - Identify live serving benefits of querying precomputed batch predictions
- Describe Structured Streaming as a common processing tool for ETL pipelines
|
| Topic 4 | - 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 5 | - Identify which code block will trigger a shown webhook
- Describe the basic purpose and user interactions with Model Registry
|
| Topic 6 | - Identify less performant data storage as a solution for other use cases
- Describe why complex business logic must be handled in streaming deployments
|
| Topic 7 | - 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 8 | - 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 9 | - Describe the advantages of using the pyfunc MLflow flavor
- Manually log parameters, models, and evaluation metrics using MLflow
|
| Topic 10 | - Describe model serving deploys and endpoint for every stage
- Identify scenarios in which feature drift and
- or label drift are likely to occur
|
| Topic 11 | - Identify that data can arrive out-of-order with structured streaming
- Identify how model serving uses one all-purpose cluster for a model deployment
|
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Databricks Certified Machine Learning Professional Sample Questions (Q78-Q83):
NEW QUESTION # 78
A Machine Learning Engineer is setting up Databricks Lakehouse Monitoring to issue alerts based on IoT sensors that monitor various water conditions at a high tech hydroponic farm.
Specifically:
- pH sensors
- Records acidity/alkalinity on a logarithmic scale from 0 to 14
- Pump status monitor
- Records the current status of the pump as "on", "off", "maintenance",
"fault", or "clean"
- Electrical Conductivity (EC) sensors
- Records nutrient level of the water as "very rich", "rich", "medium",
"poor", "very poor"
What are the appropriate drift metrics for each of the given sensor types?
- A. pH: Jensen-Shannon distance, pump status: Wasserstein distance, EC sensor: Kolmogorov- Smirnov (KS).
- B. pH: Kolmogorov-Smirnov (KS), pump status: Chi-squared, EC sensor: Jensen-Shannon distance.
- C. pH: Jenson-Shannon distance, pump status: Kolmogorov-Smirnov (KS), EC sensor: Chi-squared.
- D. pH: Chi-squared, pump status: Kolmogorov-Smirnov (KS), EC sensor: Wasserstein distance.
Answer: B
Explanation:
pH readings are continuous numerical values, so the Kolmogorov-Smirnov test is appropriate for detecting distribution shifts. Pump status is a nominal categorical variable, making the chi- squared test suitable for identifying changes in category frequencies. Electrical Conductivity levels are categorical with ordered labels, and Jensen-Shannon distance effectively measures distributional changes in such categorical probability distributions.
NEW QUESTION # 79
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?
- A. Computing predictions in real-time provides more up-to-date results
- B. Querying stored predictions can be faster than computing predictions in real-time
- C. There is no advantage to using batch serving deployments over real-time serving deployments
- D. Testing is not possible in real-time serving deployments
- E. Batch serving has built-in capabilities in Databricks Machine Learning
Answer: E
NEW QUESTION # 80
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. Use the MLlib's RegressionEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
- B. 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.
- C. Use the MLlib's RankingEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
- D. 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.
Answer: A
Explanation:
RegressionEvaluator is the Spark ML-native, distributed evaluation API for regression models.
Setting metricName to rmse computes root mean squared error directly on the distributed test DataFrame with minimal overhead, avoiding unnecessary data movement or custom computation logic.
NEW QUESTION # 81
A Data Scientist is training a model to predict whether a customer will purchase a new product.
They are experimenting with many algorithm types (e.g., Linear Regression, Logistic Regression, Decision Trees) and hundreds of hyperparameter combinations. They are using MLflow to track their runs, but managing and distinguishing between runs has become difficult. They need an MLFIow technique that will organize and trace these experiments and that will follow commonly- accepted design patterns. Which technique will fulfill these requirements?
- A. Use AutoML to automatically test a variety of different algorithms and models.
- B. Use MLflows Automatic Logging feature to reduce the number of explicit log statements within each run to increase code readability.
- C. Log runs for each algorithm type to different MLflow Tracking servers to ensure the runs do not get mixed up.
- D. Set each algorithm type as a parent run and use nested runs for different hyperparameter settings within that parent run.
Answer: D
Explanation:
Using parent runs for each algorithm and nested runs for the associated hyperparameter experiments follows a widely accepted MLflow experiment design pattern. This structure makes it easy to organize, compare, and trace large numbers of related runs, improving experiment management and interpretability when exploring many models and hyperparameter configurations.
NEW QUESTION # 82
A team is building a machine learning pipeline, which includes feature engineering, model training, model evaluation, and model deployment. Over the last year, the project has grown a lot, with multiple contributors working on it. Currently, they are manually testing their pipelines and functions. However, they are looking to build out a MLOps process and want to organize their unit tests. Which approach to organizing functions and unit tests should they take?
- A. Separate functions into dedicated modules and place all unit tests in a separate test suite or notebook.
- B. Place all functions and their corresponding unit tests in a single notebook or script.
- C. Organize functions and unit tests together in multiple notebooks, grouping them by feature or component.
- D. Write unit tests only after deployment to avoid slowing down development.
Answer: A
Explanation:
Separating production code into dedicated modules and organizing unit tests in a separate test suite follows standard software engineering and MLOps best practices. This structure improves maintainability, enables automated testing in CI/CD pipelines, supports collaboration among multiple contributors, and scales effectively as the project grows.
NEW QUESTION # 83
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