The exhibit shows incremental increases by quarter leading to a cumulative running total , which is best represented with a waterfall chart .
Ribbon chart shows ranking changes over time, not cumulative totals.
Bar chart shows simple comparisons, not running totals.
Decomposition tree is for drill-down analysis, not running totals.
Reference: Waterfall chart in Power BI

Explanation:

https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/custom-speech-overview#how-does- it-work With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint.
- Create a project and choose a model. Use a Speech resource that you create in the Azure portal. If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data.
- Upload test data. Upload test data to evaluate the speech to text offering for your applications, tools, and products.
- Train a model. Provide written transcripts and related text, along with the corresponding audio data. Testing a model before and after training is optional but recommended.
- Deploy a model. Once you ' re satisfied with the test results, deploy the model to a custom endpoint. With the exception of batch transcription, you must deploy a custom endpoint to use a Custom Speech model.

Explanation:

Reference:
You are building a LUIS (Language Understanding) model. You want to add more contributors.
Options Review:
Conditional access policy in AAD (A): Controls sign-in/access security, not contributors.
Access control (IAM) for prediction resource (C): The prediction resource is for runtime queries, not authoring. Contributors are not added here.
Access control (IAM) for authoring resource (B): Correct. Authoring resources control who can edit/train
/publish models. You add contributors here.
The answer: B. the Access control (IAM) page for the authoring resources in the Azure portal Reference:Assign roles in LUIS

The Computer Vision API uses text recognition APIs to extract and recognize text information from images.
Read uses the latest recognition models, and is optimized for large, text-heavy documents and noisy images.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/cognitive-search-with-skillsets