FREE Azure AI Engineer Questions and Answers

Question 1

You are creating an AI system that will examine several millions of images. You must suggest a method for keeping the photos. The answer must be to reduce expenses. Which storage option would you recommend?

Accepted Answer

Azure Blob Storage

Answer

Azure Blob Storage is a highly scalable and cost-effective object storage solution specifically designed for unstructured data like images, videos, and documents. For storing millions of images, its tiered storage options (hot, cool, archive) allow for significant cost savings by moving less frequently accessed data to cheaper tiers. This makes it an ideal choice for large-scale image storage when cost reduction is a primary concern.

Question 2

You launch an application that analyzes the sentiment of data kept in Azure Cosmos DB. You just loaded a lot of data into the database. The information belonged to a client called Geeks, Ltd. You find that Geeks data queries take a while to finish. The answer must be to reduce expenses. What is the best approach to achieving the objective?

Accepted Answer

Change the partioning strategy

Answer

In Azure Cosmos DB, an inefficient partitioning strategy can lead to 'hot partitions' where a disproportionate amount of data or queries are directed to a single partition, causing performance bottlenecks and increased costs. By optimizing the partitioning strategy, data can be distributed more evenly across partitions, improving query performance for specific clients and potentially reducing the need for higher throughput (RUs), thereby lowering expenses.

Question 3

You are creating an AI-based picture categorization solution in Azure. It would help if you decided which processing platform would enable you to upgrade the logic in the future. Without batching, the answer must have the lowest latency for inferencing. Which compute target should you choose?

Accepted Answer

Field-programmable gate arrays (FPGAs)

Answer

FPGAs are reconfigurable hardware accelerators that offer extremely low latency for inferencing, especially for single-item (non-batched) predictions, due to their ability to execute custom logic directly in hardware. Their reconfigurability also allows for future upgrades to the AI model's logic without requiring a complete hardware redesign. This combination of low latency and flexibility makes FPGAs an excellent choice for real-time AI inferencing where speed and adaptability are critical.

Question 4

You must create an interactive website to upload photographs and pose several predetermined questions in response to each image.

Which services should you use?

Accepted Answer

Dynamically ask questions based on an uploaded image: Azure Bot Service
Analyze and classify an image: Computer vision

Answer

To achieve an interactive website that processes uploaded images and responds with questions, Azure Computer Vision is essential for analyzing and classifying the image content. Azure Bot Service can then be used to build the conversational interface, dynamically asking predetermined questions based on the insights gained from the Computer Vision API. This combination provides both the image understanding and the interactive dialogue capabilities required.

Question 5

You set up an extensive data workload infrastructure. It would help if you used Microsoft Machine Learning Server and Azure HDInsight. You want to launch rx function calls concurrently using RevoScaleR compute contexts. What are the three different computing contexts that the machine learning server supports?

Accepted Answer

1. SQL
2. Spark
3. Local parallel

Answer

Microsoft Machine Learning Server (formerly R Server) with RevoScaleR provides various compute contexts for executing `rx` functions, enabling scalable and parallel processing. The three primary distributed and parallel compute contexts supported for `rx` function calls are SQL Server (for in-database analytics), Apache Spark (for big data processing on clusters like HDInsight), and local parallel (for leveraging multiple cores on a single machine). These contexts allow for efficient execution of machine learning tasks on different data platforms.

AI - Engineer Practice Test

AI - Engineer Practice Test

FREE Azure AI Engineer Questions and Answers