想要通過Microsoft的AI-300考試並取得AI-300的認證資格嗎？Testpdf可以保證你的成功。準備考試的時候學習與考試相關的知識是很有必要的。但是，更重要的是，要選擇適合自己的高效率的工具。Testpdf的AI-300考古題就是適合你的最好的學習方法。這個高品質的考古題可以讓你看到不可思議的效果。如果你擔心自己不能通過考試，快點擊Testpdf的網站瞭解更多的資訊吧。
| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 2: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 4: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
AI-300 認證是 Microsoft 認證體系中增長最快的領域，也是一個國際性的廠商中比較難的認證考試。不過不用擔心，Testpdf 就是一個能使 AI-300 認證考試的通過率提高的一個網站，我們的 Microsoft AI-300 考題指南由我們的專業團隊破解AI-300 考試系統數據包，經過資深IT認證講師和技術專家精心編輯整理。包括了當前 AI-300 考試所有單選題、複選題、實作題、拖拉題等題型。可以幫助考生順利通過考試。
問題 #108
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true.
Otherwise, select No.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
問題 #109
A team manages prompts used by a generative AI application built on Microsoft Foundry. Prompt updates are frequent, and prior changes have caused unexpected behavior in production.
The team requires that new prompt versions can be tested and reviewed without affecting production behavior.
You need to implement a source control practice that allows safe prompt experimentation.
What should you do?
答案：B
解題說明：
Creating a feature Git branch for each prompt change is an excellent and highly recommended action.
Prompts are code. Managing them through a standard software development lifecycle prevents production failures and allows for safe experimentation.
Isolation: Changes stay completely separate from the working production version.
Collaboration: Teams can review prompt tweaks together via Pull Requests (PRs).
Traceability: You can easily see who changed a prompt, when, and why.
Rollbacks: If a prompt performs poorly, you can instantly revert to a prior Git commit.
Reference:
https://mirascope.com/blog/prompt-management-system
問題 #110
A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?
答案：D
解題說明：
To best improve retrieval accuracy when documents are irrelevant or incomplete, you should adjust Chunk size and overlap.
Fixes Irrelevant Retrievals: If your chunks are too large, they pack in multiple unrelated topics.
This adds noise, dilutes the semantic vector, and causes the system to pull in massive blocks of text where only a single sentence was actually relevant. Tuning the size ensures chunks stay highly specific.
Fixes Incomplete Retrievals: If your chunks are too small or lack sufficient overlap, vital context gets completely cut off at the boundaries. Adding an optimal overlap (typically 10%-20%) ensures that a sentence split across a boundary preserves its surrounding meaning, keeping the context whole.
Incorrect:
[Not B]
Temperature parameter: This is a generative LLM setting that dictates the creativity or randomness of the output text. It has absolutely zero control over the vector search database or the specific documents picked during the initial retrieval phase.
[Not C]¨
Token limits: This caps how much context you can pass into the prompt window or how much text the model can generate. While a higher limit lets you pass more documents, it does not magically fix the underlying data quality or prevent the retrieval algorithm from pulling in wrong files.
[Not D]
Embedding strategy: While choosing a stronger model or fine-tuning embeddings changes how text is numerically represented, it cannot overcome poorly structured data. Even the best embedding model cannot successfully capture semantic meaning if your data ingestion pipeline is feeding it randomly sliced, incomplete paragraphs Reference:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12649634/
問題 #111
A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?
答案：A
解題說明：
Speed of recovery is the central requirement. Rolling back traffic to the previous deployment is the fastest possible action: because the previous deployment still exists on the same endpoint, you simply update the traffic weights - setting the old deployment to 100% and the new deployment to 0% - using a single Azure ML CLI command or SDK call that completes in seconds without reprovisioning any compute. Deleting and redeploying (option B) requires tearing down the endpoint, waiting for deprovisioning, recreating it, re- deploying the model, and waiting for containers to start - potentially 10 to 30 minutes. Changing authentication type (option C) does not affect application errors caused by a bad model. Increasing compute size (option D) does not fix model logic errors. The entire reason Azure ML supports multiple concurrent deployments with traffic splitting is precisely to enable this instant rollback pattern.
Microsoft Learn Reference Topic: Roll back deployments on managed online endpoints - Azure Machine Learning safe deployment practices
問題 #112
Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: uri_folder
The best data asset type for this scenario is a File data asset (uri_file or uri_folder).
In Azure Machine Learning, image files used for training computer vision models are best managed as file-based data assets because machine learning frameworks read individual images directly from storage paths rather than tabular rows.
Preserves Formats: Keeps images in their native formats (PNG, JPEG).Direct Access: Allows training scripts to mount or download files easily.
*-> Folder Mapping: A uri_folder references the entire directory containing your images.
Efficiency: Avoids the overhead of parsing unstructured binary data into a table.
Box 2: azureml
To point a uri_folder data asset to your Azure Blob Storage locations in Azure Machine Learning, you should use the azureml:// URI scheme.
This is the recommended, modern standard that leverages Azure ML datastores for secure tokenless access.
Alternatively, you can use direct Azure Storage URI schemes depending on your configuration Recommended Scheme: Azure ML Datastore This scheme abstracts the storage credentials by referencing an Azure ML Datastore.
Format: azureml://datastores/<datastore_name>/paths/<path_to_folder>/
Example: azureml://datastores/myblobdatastore/paths/images/training_set/ Incorrect:
[not wasbs]
Azure Blob Storage (wasbs)
Used when connecting via the legacy Windows Azure Storage Blob driver.
Format:
wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_folder>/
[Not abfss]
Azure Data Lake Storage Gen2 (abfss)
Used if your Blob Storage accounts have the hierarchical namespace enabled.
Format:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path_to_folder>/ Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
問題 #113
......
總體來說，Testpdf 的模擬試題還是比較實用的，知識點也比較明確，據廣大考生反應，真正的 AI-300 考題都是我們考題網裡面的原題，而且題目的答案也比較隱晦一些，不懂不明白那個知識。或沒有認真看題目，是不可能選到正確答案的，如果你通過我們的 Microsoft AI-300 考題模擬，就能在 AI-300 考試中輕鬆過關，讓自己更加接近成功之路。
AI-300題庫更新: https://www.testpdf.net/AI-300.html