Get Updated Microsoft AI-300 PDF Dumps for Learning
Exam : AI-300
Title : Operationalizing Machine
Learning and Generative AI
Solutions
https://www.passcert.com/AI-300.html
1/8
, Get Updated Microsoft AI-300 PDF Dumps for Learning
1.Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as
much exam time as you would like to complete each case study. However, there might be additional case
studies or other exam sections. Manage your time to ensure that you can complete all the exam sections
in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient
time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case.
Case studies and associated questions might contain exhibits or other resources that provide more
information about the scenario described in the case. Information provided in an individual question does
not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and
change your answers before you move to the next exam section. After you leave this case study, you will
NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu
provides links to information such as business requirements, the existing environment, and problem
statements. Please read through all this information before answering any questions. When you are ready
to answer a question, select the "Question" button to return to the question.
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
2/8
Exam : AI-300
Title : Operationalizing Machine
Learning and Generative AI
Solutions
https://www.passcert.com/AI-300.html
1/8
, Get Updated Microsoft AI-300 PDF Dumps for Learning
1.Case Study
This is a case study. Case studies are not timed separately from other exam sections. You can use as
much exam time as you would like to complete each case study. However, there might be additional case
studies or other exam sections. Manage your time to ensure that you can complete all the exam sections
in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient
time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case.
Case studies and associated questions might contain exhibits or other resources that provide more
information about the scenario described in the case. Information provided in an individual question does
not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and
change your answers before you move to the next exam section. After you leave this case study, you will
NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu
provides links to information such as business requirements, the existing environment, and problem
statements. Please read through all this information before answering any questions. When you are ready
to answer a question, select the "Question" button to return to the question.
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
2/8
