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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Machine Learning Professional Exam |
| Exam Number: | Databricks-Machine-Learning-Professional |
| Real Exam Qty: | 59 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not publicly disclosed (approximately 70%) |
| Exam Format: | Multi-select, Multiple choice |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Machine Learning Associate |
| Exam Price: | $200 USD |
| Recommended Training: | Advanced Machine Learning Operations Machine Learning at Scale |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Machine-Learning-Professional Sample Questions |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/machine-learning-professional |
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NEW QUESTION # 130
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?
Answer: D
NEW QUESTION # 131
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?
Answer: B
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 # 132
A Machine Learning Engineer is setting up Databricks Lakehouse Monitoring to issue alerts based on IoT sensors that monitor various water conditions at a high tech hydroponic farm.
Specifically:
- pH sensors
- Records acidity/alkalinity on a logarithmic scale from 0 to 14
- Pump status monitor
- Records the current status of the pump as "on", "off", "maintenance",
"fault", or "clean"
- Electrical Conductivity (EC) sensors
- Records nutrient level of the water as "very rich", "rich", "medium",
"poor", "very poor"
What are the appropriate drift metrics for each of the given sensor types?
Answer: C
Explanation:
pH readings are continuous numerical values, so the Kolmogorov-Smirnov test is appropriate for detecting distribution shifts. Pump status is a nominal categorical variable, making the chi- squared test suitable for identifying changes in category frequencies. Electrical Conductivity levels are categorical with ordered labels, and Jensen-Shannon distance effectively measures distributional changes in such categorical probability distributions.
NEW QUESTION # 133
A Machine Learning Engineer is using Lakehouse Monitoring to track the performance of ML models deployed in their environment. They want to monitor significant distributional drift in categorical features with a metric bounded on [0,1] for easy interpretation. Which statistical method should they use?
Answer: B
Explanation:
Jensen-Shannon Distance is well suited for measuring distributional drift in categorical features. It is symmetric, numerically stable, and bounded between 0 and 1, which makes it easy to interpret and ideal for monitoring categorical feature drift in Lakehouse Monitoring.
NEW QUESTION # 134
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?
Answer: E
NEW QUESTION # 135
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