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Reference: https://www.databricks.com/learn/certification/machine-learning-professional
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Databricks Certified Machine Learning Professional Sample Questions:
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?
- A. fs.score_batch(model_uri, spark_df)
- B. fs.score_model(model_uri, spark_df)
- C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df) - E. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)
Correct Answer: A 🗳️
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats. Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?
- A. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- B. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
- C. All of these statements
- D. Because the streaming deployment is always on, all types of data must be handled without producing an error
- E. None of these statements
Correct Answer: D 🗳️
Which Spark ML class supports automated hyperparameter tuning?
- A. CrossValidator
- B. BinaryClassificationEvaluator
- C. ParamGridBuilder
- D. PipelineModel
Correct Answer: A 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
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. Kolmogorov-Smirnov (KS) test
- B. Two-way Chi-squared Test
- C. None of these
- D. One-way Chi-squared Test
- E. Jenson-Shannon distance
Correct Answer: D 🗳️
A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?
- A. Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.
- B. Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.
- C. Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.
- D. Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.
Correct Answer: D 🗳️
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