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| Topic | Details |
|---|
| Topic 1 | - Identify JIT feature values as a need for real-time deployment
- Describe how to list all webhooks and how to delete a webhook
|
| 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 less performant data storage as a solution for other use cases
- Describe why complex business logic must be handled in streaming deployments
|
| Topic 4 | - Identify the requirements for tracking nested runs
- Describe an MLflow flavor and the benefits of using MLflow flavors
|
| 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
|
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Databricks Certified Machine Learning Professional Sample Questions (Q58-Q63):
NEW QUESTION # 58
Which of the following is an obstacle related to streaming machine learning applications?
- A. All of these
- B. None of these
- C. End-to-end fault tolerance
- D. Out-of-order data
Answer: A
Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.
NEW QUESTION # 59
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?
Answer: E
NEW QUESTION # 60
A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?
- A. The model artifacts must be stored in DBFS rather than Unity Catalog volumes.
- B. The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.
- C. The model signature must be provided, as Unity Catalog requires all models to have signatures.
- D. The cluster must be configured with shared access mode instead of dedicated mode.
Answer: D
Explanation:
Unity Catalog model registration requires the cluster to run in shared access mode. Dedicated (single-user) clusters do not support registering models into Unity Catalog because governance, lineage, and access control enforcement rely on shared-mode execution. As a result, even if Unity Catalog is enabled, model registration will fail when attempted from a dedicated cluster.
NEW QUESTION # 61
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?
- A. Cloud-based in-memory instance of a Spark DataFrame
- B. Cloud-based low latency database
- C. On-premises low-latency database
- D. Cloud-based object storage
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 # 62
A Machine Learning Engineer is setting up a cluster for a deep learning training run, but has a number of settings options to choose from. Their cluster will be reused by other engineers on their team and their datasets vary in size from hundreds of MBs to hundreds of GBs. They need to choose a configuration that allows for performant, stable deep learning training without excessive costs. Which configuration will do this?
- A. Using ML Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
- B. Using ML Runtime, use a large single node, GPU-enabled VM with auto termination set to 20 minutes to reduce costs
- C. Using Databricks Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
- D. Using ML Runtime, use a moderate sized GPU VM for the driver and a variable number of memory optimized CPU VMs for the workers with autoscale enabled
Answer: A
Explanation:
Using the ML Runtime ensures deep learning frameworks and GPU drivers are preconfigured and optimized. A moderately sized CPU driver is sufficient for coordination, while GPU-enabled worker nodes handle the computationally intensive training workload. Enabling autoscaling allows the cluster to efficiently adapt to datasets ranging from hundreds of megabytes to hundreds of gigabytes, providing strong performance without overprovisioning and controlling costs in a shared team environment.
NEW QUESTION # 63
......
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