Databricks Databricks-Certified-Generative-AI-Engineer-Associate Exam Real Questions

Prepare and pass your Databricks Certified Generative AI Engineer Associate with free Databricks-Certified-Generative-AI-Engineer-Associate exam questions.

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Total 105 Questions | Updated On: Sep 13, 2024

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Question 1

As a Gen AI engineer, you are preparing to evaluate your language model both before and after deployment. Which of the following approaches best describes the key focus of online evaluation compared to offline evaluation?

A : Offline evaluation focuses on analyzing real user behavior data collected from live deployments, while online evaluation involves curating benchmark data and using task-specific metrics.

B : Offline evaluation is concerned with collecting user feedback through live interactions, whereas online evaluation relies on predefined benchmark datasets and task-specific metrics.

C : Online evaluation requires extensive preparation of benchmark datasets before deployment, while offline evaluation is centered on real-time monitoring of user interactions with the model.

D : Online evaluation involves deploying the app and assessing the model based on real user interactions and feedback, whereas offline evaluation is conducted using curated benchmark data and task-specific metrics.

Answer: D

Question 2

As a Gen AI engineer tasked with selecting a deployment strategy for an email marketing system that generates customer segmentation reports daily based on accumulated data, which deployment strategy would be most appropriate given the need to process data on a regular schedule and write results to persistent storage?

A : Batch Deployment

B : Real-Time Deployment

C : Edge Deployment

D : Streaming Deployment

Answer: A

Question 3

As a Gen AI engineer, you’re tasked with monitoring your AI system to ensure it performs effectively and cost-efficiently. Which of the following scenarios best represents an effective monitoring strategy for diagnosing potential issues in your AI model?

A : Monitoring the system's user interface for aesthetic design improvements and user experience enhancements

B : Regularly reviewing input data, prompt queries and responses to identify any inconsistencies or anomalies.

C : Focusing solely on analyzing performance/cost details without reviewing mid-training checkpoints or human feedback.

D : Evaluating the model’s performance only during final deployment phases to ensure it meets all business requirements

Answer: B

Question 4

As a Gen AI engineer working on batch inference with MLflow, which of the following methods are correctly used for performing batch inference in single-node and multi-node environments? Choose two correct answers.

A : Implement mlflow.pyfunc.spark_udf(spark, model_uri=...) and result_df = prod_df.withColumn("result", model_udf("input")) for multi-node batch inference.

B : Apply mlflow.pyfunc.spark_udf(spark, model_uri=...) and .predict() for single-node batch inference.

C : Utilize mlflow.pyfunc.load_model() and result_df = prod_df.withColumn("result", model_udf("input")) for multi-node batch processing.

D : Use mlflow.pyfunc.load_model() and .predict() for single-node batch inference.

Answer: A,D

Question 5

As a Gen AI Engineer, you have a task to decide if you need a vector database for your Q&A chat bot. In which of the following scenarios you should use a vector database?

A : When you need to handle large-scale data, perform fast semantic searches, and require persistent storage.

B : When you need to quickly prototype a solution with a smaller dataset that can be handled in-memory.

C : When you need to store and retrieve large amounts of structured data based on exact matches.

D : When you need to perform keyword-based searches on a relational database.

Answer: A

Page: 1 / 21

Total 105 Questions | Updated On: Sep 13, 2024

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