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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Section	Weight	Objectives
ML Ops	44%	- Automated retraining workflows - Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies
Model Development	44%	- Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines - Advanced MLflow usage - Distributed training and hyperparameter tuning
Model Deployment	12%	- Model rollout and version management - Custom model serving - Deployment strategies

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Databricks Certified Machine Learning Professional Sample Questions (Q77-Q82):

NEW QUESTION # 77
A data scientist has run the following line of code to create a Feature Store table:

Which statement correctly describes the data in the Feature Store table new_table?

A. The data in new_table is exactly the same as the data in df
B. The data in new_table is the customer-related features from df
C. There is no data in new_table
D. The data in new_table is the values in column customer_id from df

Answer: C

Explanation:
The fs.create_table() command defines metadata for a Feature Store table, such as its name, primary key, schema, and description - but it does not insert any data into the table. This command simply registers an empty table structure based on the provided schema (df.schema).
To populate the table with data, a separate fs.write_table() or equivalent write operation must be executed afterward.

NEW QUESTION # 78
A Machine Learning Engineer has a real-time fraud detection model deployed that approves or blocks millions of transactions daily. They need to deploy a new version of the model with improved detection accuracy to this high-traffic, business-critical application. Because any model downtime could result in lost revenue or customer dissatisfaction, the engineer must ensure zero downtime and minimal disruption for end users. Leadership also requires that any rollback to the previous version be immediate if issues are detected with the new model in production. Which deployment strategy meets these requirements?

A. Use a blue-green deployment for the new model, maintaining two separate production environments (one "blue," one "green") and switching user traffic to the new version only after confirming it is healthy, enabling instant rollback if needed.
B. Deploy the new model in parallel with the old version, monitor both for a set period, and then notify all stakeholders to manually switch over to the new version at a coordinated time.
C. Use a canary deployment by initially routing a small percentage of user traffic to the new model version, monitoring results, and gradually increasing exposure until all traffic uses the new version if no problems are detected.
D. Replace the current production model with the new version during a scheduled maintenance window, notify affected users of potential brief disruptions, and prepare to reroute requests to a backup if failures occur after deployment.

Answer: A

Explanation:
A blue-green deployment maintains two fully operational production environments and shifts traffic between them instantly. This approach provides zero downtime during deployment and allows immediate rollback to the previous model version if issues arise, which is critical for high- traffic, business-critical real-time systems.

NEW QUESTION # 79
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A. Tuning the file size
B. Bin-packing
C. Write as a Parquet file
D. Data skipping
E. Z-Ordering

Answer: A

NEW QUESTION # 80
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace spark.read with spark.readStream
B. Replace "inference" with the path to the location of the Delta table
C. Replace formatfdelta") with format("stream")
D. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
E. Replace predict with a stream-friendly prediction function

Answer: D

NEW QUESTION # 81
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
B. mlflow.register_model(model_uri, "model")
C. mlflow.register_model(f"runs:/{run_id}/model")
D. mlflow.register_model(model_uri, "best_model")
E. mlflow.register_model(run_id, "best_model")

Answer: D

NEW QUESTION # 82
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