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Question 1/101/10
Topic 1, Fabrikam Inc.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health
dashboards and predictive insights to regional hospital systems across the United States. Fabrikam
Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and
readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks
that run on a local server as the primary development environment. The data science team is
experiencing scalability, asset management and code management issues with the current
development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to
mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat
application for client support.
Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
Azure AI Search indexing curated analytical documents and reference materials
A small set of Python-based training scripts maintained by data scientists Azure OpenAI Service with
deployed foundational models
A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
Model training jobs are run manually from notebooks.
Experiment tracking is inconsistent
Model versions are registered without standardized metadata.
Deployment is performed manually by data scientists, with limited rollback capability.
The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts
rather than managed identities.
Compute targets are manually created and shared across experiments. This has led to resource
contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
Provide a conversational interface that answers analytics questions by using internal documents and
datasets.
Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
Enable repeatable and auditable model training and deployment processes.
Support experimentation to compare prompt strategies and fine-tuned models.
Align the model with the ranked preferences and optimize behavior for the long term.
Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and
environments.
Implement experiment tracking and model versioning for all training jobs.
Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
Deploy traditional machine learning models with support for staged rollout and rollback.
Improve RAG-based solution output quality.
Use the existing evaluation datasets that are based on real data with input-output pairs.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient
Issues and Constraints
Fabrikam Inc. must comply with internal security policies that require the company to restrict
network access and avoid long-lived secrets. The data science team has limited Azure DevOps
experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where
possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables
reliable training, evaluation, deployment, and iteration of generative AI models. The solution must
support experimentation and gradual rollout while ensuring governance, security, and operational
stability. The data science and platform teams must collaborate to deliver this solution by using Azure
Machine Learning and Microsoft Foundry capabilities.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health
dashboards and predictive insights to regional hospital systems across the United States. Fabrikam
Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and
readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks
that run on a local server as the primary development environment. The data science team is
experiencing scalability, asset management and code management issues with the current
development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to
mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat
application for client support.
Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
Azure AI Search indexing curated analytical documents and reference materials
A small set of Python-based training scripts maintained by data scientists Azure OpenAI Service with
deployed foundational models
A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
Model training jobs are run manually from notebooks.
Experiment tracking is inconsistent
Model versions are registered without standardized metadata.
Deployment is performed manually by data scientists, with limited rollback capability.
The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts
rather than managed identities.
Compute targets are manually created and shared across experiments. This has led to resource
contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
Provide a conversational interface that answers analytics questions by using internal documents and
datasets.
Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
Enable repeatable and auditable model training and deployment processes.
Support experimentation to compare prompt strategies and fine-tuned models.
Align the model with the ranked preferences and optimize behavior for the long term.
Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and
environments.
Implement experiment tracking and model versioning for all training jobs.
Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
Deploy traditional machine learning models with support for staged rollout and rollback.
Improve RAG-based solution output quality.
Use the existing evaluation datasets that are based on real data with input-output pairs.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient
Issues and Constraints
Fabrikam Inc. must comply with internal security policies that require the company to restrict
network access and avoid long-lived secrets. The data science team has limited Azure DevOps
experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where
possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables
reliable training, evaluation, deployment, and iteration of generative AI models. The solution must
support experimentation and gradual rollout while ensuring governance, security, and operational
stability. The data science and platform teams must collaborate to deliver this solution by using Azure
Machine Learning and Microsoft Foundry capabilities.
You need to standardize how Fabrikam Inc. manages machine learning assets.
Which action should you perform first?
Which action should you perform first?
Select the answer:Select the answer
1 correct answerA.
Register assets in the Azure Machine Learning registry.
B.
Create a shared Azure Machine Learning workspace.
C.
Deploy a managed online endpoint.
D.
Create a new Microsoft Foundry project.
Quiz
Question 2/102/10
Topic 1, Fabrikam Inc.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health
dashboards and predictive insights to regional hospital systems across the United States. Fabrikam
Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and
readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks
that run on a local server as the primary development environment. The data science team is
experiencing scalability, asset management and code management issues with the current
development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to
mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat
application for client support.
Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
Azure AI Search indexing curated analytical documents and reference materials
A small set of Python-based training scripts maintained by data scientists Azure OpenAI Service with
deployed foundational models
A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
Model training jobs are run manually from notebooks.
Experiment tracking is inconsistent
Model versions are registered without standardized metadata.
Deployment is performed manually by data scientists, with limited rollback capability.
The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts
rather than managed identities.
Compute targets are manually created and shared across experiments. This has led to resource
contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
Provide a conversational interface that answers analytics questions by using internal documents and
datasets.
Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
Enable repeatable and auditable model training and deployment processes.
Support experimentation to compare prompt strategies and fine-tuned models.
Align the model with the ranked preferences and optimize behavior for the long term.
Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and
environments.
Implement experiment tracking and model versioning for all training jobs.
Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
Deploy traditional machine learning models with support for staged rollout and rollback.
Improve RAG-based solution output quality.
Use the existing evaluation datasets that are based on real data with input-output pairs.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient
Issues and Constraints
Fabrikam Inc. must comply with internal security policies that require the company to restrict
network access and avoid long-lived secrets. The data science team has limited Azure DevOps
experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where
possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables
reliable training, evaluation, deployment, and iteration of generative AI models. The solution must
support experimentation and gradual rollout while ensuring governance, security, and operational
stability. The data science and platform teams must collaborate to deliver this solution by using Azure
Machine Learning and Microsoft Foundry capabilities.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health
dashboards and predictive insights to regional hospital systems across the United States. Fabrikam
Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and
readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks
that run on a local server as the primary development environment. The data science team is
experiencing scalability, asset management and code management issues with the current
development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to
mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat
application for client support.
Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
Azure AI Search indexing curated analytical documents and reference materials
A small set of Python-based training scripts maintained by data scientists Azure OpenAI Service with
deployed foundational models
A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
Model training jobs are run manually from notebooks.
Experiment tracking is inconsistent
Model versions are registered without standardized metadata.
Deployment is performed manually by data scientists, with limited rollback capability.
The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts
rather than managed identities.
Compute targets are manually created and shared across experiments. This has led to resource
contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
Provide a conversational interface that answers analytics questions by using internal documents and
datasets.
Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
Enable repeatable and auditable model training and deployment processes.
Support experimentation to compare prompt strategies and fine-tuned models.
Align the model with the ranked preferences and optimize behavior for the long term.
Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and
environments.
Implement experiment tracking and model versioning for all training jobs.
Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
Deploy traditional machine learning models with support for staged rollout and rollback.
Improve RAG-based solution output quality.
Use the existing evaluation datasets that are based on real data with input-output pairs.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient
Issues and Constraints
Fabrikam Inc. must comply with internal security policies that require the company to restrict
network access and avoid long-lived secrets. The data science team has limited Azure DevOps
experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where
possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables
reliable training, evaluation, deployment, and iteration of generative AI models. The solution must
support experimentation and gradual rollout while ensuring governance, security, and operational
stability. The data science and platform teams must collaborate to deliver this solution by using Azure
Machine Learning and Microsoft Foundry capabilities.
You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.’s issues,
constraints, and technical requirements.
What should you implement?
constraints, and technical requirements.
What should you implement?
Select the answer:Select the answer
1 correct answerA.
Training jobs that run on a single shared compute cluster
B.
Fixed-size compute cluster
C.
Dedicated compute clusters per experiment
D.
Managed compute targets with autoscaling
Quiz
Question 3/103/10
Topic 1, Fabrikam Inc.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health
dashboards and predictive insights to regional hospital systems across the United States. Fabrikam
Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and
readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks
that run on a local server as the primary development environment. The data science team is
experiencing scalability, asset management and code management issues with the current
development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to
mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat
application for client support.
Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
Azure AI Search indexing curated analytical documents and reference materials
A small set of Python-based training scripts maintained by data scientists Azure OpenAI Service with
deployed foundational models
A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
Model training jobs are run manually from notebooks.
Experiment tracking is inconsistent
Model versions are registered without standardized metadata.
Deployment is performed manually by data scientists, with limited rollback capability.
The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts
rather than managed identities.
Compute targets are manually created and shared across experiments. This has led to resource
contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
Provide a conversational interface that answers analytics questions by using internal documents and
datasets.
Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
Enable repeatable and auditable model training and deployment processes.
Support experimentation to compare prompt strategies and fine-tuned models.
Align the model with the ranked preferences and optimize behavior for the long term.
Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and
environments.
Implement experiment tracking and model versioning for all training jobs.
Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
Deploy traditional machine learning models with support for staged rollout and rollback.
Improve RAG-based solution output quality.
Use the existing evaluation datasets that are based on real data with input-output pairs.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient
Issues and Constraints
Fabrikam Inc. must comply with internal security policies that require the company to restrict
network access and avoid long-lived secrets. The data science team has limited Azure DevOps
experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where
possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables
reliable training, evaluation, deployment, and iteration of generative AI models. The solution must
support experimentation and gradual rollout while ensuring governance, security, and operational
stability. The data science and platform teams must collaborate to deliver this solution by using Azure
Machine Learning and Microsoft Foundry capabilities.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health
dashboards and predictive insights to regional hospital systems across the United States. Fabrikam
Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and
readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks
that run on a local server as the primary development environment. The data science team is
experiencing scalability, asset management and code management issues with the current
development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to
mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat
application for client support.
Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
Azure AI Search indexing curated analytical documents and reference materials
A small set of Python-based training scripts maintained by data scientists Azure OpenAI Service with
deployed foundational models
A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
Model training jobs are run manually from notebooks.
Experiment tracking is inconsistent
Model versions are registered without standardized metadata.
Deployment is performed manually by data scientists, with limited rollback capability.
The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts
rather than managed identities.
Compute targets are manually created and shared across experiments. This has led to resource
contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
Provide a conversational interface that answers analytics questions by using internal documents and
datasets.
Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
Enable repeatable and auditable model training and deployment processes.
Support experimentation to compare prompt strategies and fine-tuned models.
Align the model with the ranked preferences and optimize behavior for the long term.
Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
Use Azure Machine Learning workspaces to centrally manage data assets, models, and
environments.
Implement experiment tracking and model versioning for all training jobs.
Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
Deploy traditional machine learning models with support for staged rollout and rollback.
Improve RAG-based solution output quality.
Use the existing evaluation datasets that are based on real data with input-output pairs.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient
Issues and Constraints
Fabrikam Inc. must comply with internal security policies that require the company to restrict
network access and avoid long-lived secrets. The data science team has limited Azure DevOps
experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where
possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables
reliable training, evaluation, deployment, and iteration of generative AI models. The solution must
support experimentation and gradual rollout while ensuring governance, security, and operational
stability. The data science and platform teams must collaborate to deliver this solution by using Azure
Machine Learning and Microsoft Foundry capabilities.
You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
What should you recommend?
What should you recommend?
Select the answer:Select the answer
1 correct answerA.
Azure Machine Learning job output logs
B.
MLflow experiment tracking
C.
Application Insights logs
D.
Azure Monitor alerts
Quiz
Question 4/104/10
A team trains an MLflow model that scores customer churn risk. The model will be consumed by
different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in
the answer area. NOTE: Each correct selection is worth one point.
different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in
the answer area. NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answerThe correct answer is A.
Explanation:
You need to match each workload with the most appropriate managed inference option:
1. Synchronous predictions during customer interactions
This requires low-latency, real-time responses because the system needs a prediction immediately while the customer is interacting.
The correct managed inference option for this is:
Managed online endpoint
2. Scheduled scoring of files containing millions of records
This is a batch workload because it processes large volumes of records asynchronously and does not require an immediate response.
The correct managed inference option for this is:
Batch endpoint
Why this is the right match:
- Managed online endpoints are designed for real-time inference with low latency.
- Batch endpoints are designed for large-scale, asynchronous inference over files or datasets.
So the correct selections are:
- Synchronous customer interactions: Managed online endpoint
- Millions of records submitted in files: Batch endpoint
If you want, I can also explain why the other options are not correct.
Explanation:
You need to match each workload with the most appropriate managed inference option:
1. Synchronous predictions during customer interactions
This requires low-latency, real-time responses because the system needs a prediction immediately while the customer is interacting.
The correct managed inference option for this is:
Managed online endpoint
2. Scheduled scoring of files containing millions of records
This is a batch workload because it processes large volumes of records asynchronously and does not require an immediate response.
The correct managed inference option for this is:
Batch endpoint
Why this is the right match:
- Managed online endpoints are designed for real-time inference with low latency.
- Batch endpoints are designed for large-scale, asynchronous inference over files or datasets.
So the correct selections are:
- Synchronous customer interactions: Managed online endpoint
- Millions of records submitted in files: Batch endpoint
If you want, I can also explain why the other options are not correct.
Quiz
Question 5/105/10
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
Select the answer:Select the answer
1 correct answerA.
Azure Container Instances (ACI)
B.
Azure Machine Learning compute clusters
C.
Local deployment
D.
Azure Kubernetes Service (AKS)
Quiz
Question 6/106/10
A team manages an Azure Machine Learning workspace where they deploy models to online
endpoints.
The team needs to introduce a new version of a model to production without disrupting existing
users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
endpoints.
The team needs to introduce a new version of a model to production without disrupting existing
users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
Select the answer:Select the answer
1 correct answerA.
Deploy the model to a batch endpoint.
B.
Split traffic between deployments.
C.
Replace the existing endpoint.
D.
Route all traffic to the new deployment.
Quiz
Question 7/107/10
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Select the answer:Select the answer
1 correct answerA.
CSV
B.
TSV
C.
JSONL
D.
JSON
Quiz
Question 8/108/10
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
Select the answer:Select the answer
1 correct answerA.
ISO-8859-1
B.
UTF-16
C.
UTF-8
D.
ASCII
Quiz
Question 9/109/10
You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and
fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
You label examples of support tickets. You must improve classification accuracy by configuring and
fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
Select the answer:Select the answer
1 correct answerA.
Use prompt flow to generate multiple prompt templates for evaluation.
B.
Deploy the base model to an online endpoint before starting fine-tuning.
C.
Enable tracing for all inference calls in the evaluation pipeline.
D.
Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
Quiz
Question 10/1010/10
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt
variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
Select the answer:Select the answer
1 correct answerA.
Generate synthetic interaction data.
B.
Configure content filters.
C.
Apply a blocklist.
D.
Enable observability metrics.
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- Pass score:80%
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