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| Topic | Details |
|---|
| Topic 1 | - Describe model serving deploys and endpoint for every stage
- Identify scenarios in which feature drift and
- or label drift are likely to occur
|
| Topic 2 | - 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 3 | - 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 4 | - Describe the advantages of using the pyfunc MLflow flavor
- Manually log parameters, models, and evaluation metrics using MLflow
|
| Topic 5 | - 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 6 | - 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 (Q118-Q123):
NEW QUESTION # 118
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console. Which of the following lines of code can they use to accomplish this task?
- A. It is not possible to automatically log MLflow runs.
- B. spark.conf.set("autologging", True)
- C. mlflow.sklearn.autolog()
- D. mlflow.autolog()
- E. mlflow.spark.autolog()
Answer: B
NEW QUESTION # 119
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Model Serving
- B. Feature Store
- C. Models
- D. Model Registry
- E. Experiments
Answer: A
NEW QUESTION # 120
Which of the following describes the concept of MLflow Model flavors?
- A. A convention that MLflow Model Registry can use to organize its Models by project
- B. A convention that MLflow Experiments can use to organize their Runs by project
- C. A convention that MLflow Model Registry can use to version models
- D. A convention that deployment tools can use to wrap preprocessing logic into a Model
- E. A convention that deployment tools can use to understand the model
Answer: B
NEW QUESTION # 121
A machine learning engineer is monitoring label values for a production machine learning classification model. The engineer believes that the relative prevalence of the classes is becoming changing in more recent data. Which tool can the machine learning engineer use to assess their theory?
- A. Jenson-Shannon distance
- B. Kolmogorov-Smirnov (KS) test
- C. Two-way Chi-squared Test
- D. One-way Chi-squared Test
Answer: D
Explanation:
A One-way Chi-squared Test is used to compare observed class distributions with expected distributions to determine if there has been a significant shift. This makes it suitable for detecting label drift in classification tasks, where the relative prevalence of classes may be changing over time.
NEW QUESTION # 122
A Machine Learning Engineer has a large dataset with a customer_region column and wants to train separate models for each region, then generate predictions. They need to parallelize this group-specific model training process using Databricks and the Pandas Function API. Which approach will implement this solution?
- A. Use groupBy("customer_region") and apply a training function with applyInPandas().
- B. Use foreachBatch() to sequentially process each region's data.
- C. Use mapInPandas() to apply the training function across all partitions without grouping.
- D. Use collect() to gather all data and process regions using standard pandas groupby.
Answer: A
Explanation:
The Pandas Function API supports parallel, group-specific processing by using groupBy on the grouping column and applyInPandas to execute a custom training function independently for each group. This enables separate models to be trained per region in parallel across the cluster, with each function invocation receiving only the data for its group.
NEW QUESTION # 123
......
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