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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment | 12% | - Custom model serving - Deployment strategies - Model rollout and version management |
| ML Ops | 44% | - Automated retraining workflows - Environment management with Databricks Asset Bundles - Testing and validation strategies - Model monitoring and drift detection with Lakehouse Monitoring |
| Model Development | 44% | - Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning - Advanced MLflow usage |
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NEW QUESTION # 17
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?
Answer: C
NEW QUESTION # 18
A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
They are using the following code block:
Which change does the machine learning engineer need to make to this code block so it will successfully accomplish the task?
Answer: C
Explanation:
To delete a webhook using the MLflow REST API, the correct endpoint is:
/api/2.0/mlflow/registry-webhooks/delete
In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.
NEW QUESTION # 19
A Data Scientist needs to perform inference on a continuously updated Delta table called sales_data using an MLflow-registered Spark ML pipeline model (catalog.prod.sales_forecaster).
Predictions must be written to a Delta table forecast_results, which must be updated with low latency leveraging a cluster with three executors. They want to maximize the efficient use of their cluster when doing this. Which approach will suit their needs?
Answer: D
Explanation:
This approach uses a Spark Structured Streaming read from the continuously updated Delta table and applies an MLflow-registered Spark UDF for inference. The model execution is distributed across the three executors, enabling parallel, low-latency scoring as new data arrives. Writing the results with writeStream efficiently updates the forecast_results Delta table incrementally, maximizing cluster utilization and aligning with best practices for continuous, scalable batch- stream inference in Databricks.
NEW QUESTION # 20
A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?
Answer: A
Explanation:
Real-time basket contents are request-specific and only available at inference time, making them unsuitable for precomputed feature tables or online stores. Implementing a custom pyfunc model allows the predict() method to directly consume and process the basket contents included in the request payload, enabling real-time feature engineering while keeping the logic tightly coupled with model inference in Databricks Model Serving.
NEW QUESTION # 21
A machine learning engineer has a machine learning pipeline where predictions are updated annually. The final prediction dataset contains millions of rows, and that dataset is irregularly accessed. Which solution should the machine learning engineer use to maintain cost efficiency?
Answer: D
Explanation:
For data that is large in size, infrequently accessed, and updated only periodically, cloud-based object storage (such as AWS S3, Azure Blob Storage, or Google Cloud Storage) is the most cost- efficient option. It offers durable, scalable, and inexpensive storage compared to in-memory or low-latency databases, which are optimized for frequent access and real-time performance rather than long-term, infrequent retrieval.
NEW QUESTION # 22
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