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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Security and governance
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
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NEW QUESTION # 115
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?
Answer: D
Explanation:
To improve the sampling convergence and effectiveness of your Bayesian hyperparameter sweep, you must decrease the value of max_concurrent_trials (for example, setting max_concurrent_trials = 4).
Decrease max_concurrent_trials
Bayesian sampling is a fundamentally sequential algorithm. It relies entirely on the outcomes of previously completed trials to calculate and select the next optimized set of hyperparameters.
The Problem: Setting max_concurrent_trials = 10 forces the optimization engine to kick off 10 trials at the exact same time. Because none of these 10 overlapping trials have completed or generated metrics yet, they cannot learn from one another. This high parallelism compromises the intelligence of the algorithm, turning your structured Bayesian sweep into a less effective random sampling process.
The Solution: Reducing max_concurrent_trials to a smaller number (such as 4) allows sets of trials to complete sequentially. This ensures the optimizer captures enough historical performance data to intelligently direct subsequent runs, achieving significantly better convergence.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 116
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the scoring_script parameter.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 117
You manage a Retrieval-Augmented Generation (RAG) system that uses Azure AI Search to retrieve documents from an indexed knowledge base.
The system must support the following retrieval requirements:
Queries that include exact policy identifiers must return matching documents even when semantic similarity is low.
Natural-language questions must prioritize semantically relevant documents even when keywords are not an exact match.
You need to configure the retrieval approach to meet the requirements.
How should you configure the retrieval behavior for each requirement? To answer, select the appropriate options in the answer area . NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Different query types require fundamentally different retrieval algorithms. For queries that include exact policy identifiers, keyword or BM25 search is the correct choice because BM25 scores documents based on term frequency and inverse document frequency - an exact match on a specific policy identifier such as POL-
2024-HR-042 scores very highly regardless of semantic context. This is the right approach when semantic similarity is low but exact term matching is critical. For natural-language questions where keywords may not be an exact match, semantic or vector search is the correct choice because vector embeddings capture meaning rather than exact tokens, finding relevant documents even when the user ' s vocabulary differs from the document ' s terminology. Azure AI Search supports both modes through its hybrid search capability, and the correct configuration maps each query type to its optimal retrieval algorithm.
Microsoft Learn Reference Topic: Configure hybrid search in Azure AI Search - BM25 keyword search vs.
semantic vector search
NEW QUESTION # 118
Hotspot Question
You manage a Microsoft Foundry project.
You plan to create a search index by using the Microsoft Foundry SDK.
You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.
How should you complete the item code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: SemanticConfiguration
In the Azure AI Search / Microsoft Foundry SDK, the SemanticConfiguration class defines a specific semantic configuration (such as the name and the prioritized fields) used to optimize semantic ranking.
Box 2: SemanticField
The SemanticField class is specifically used inside the SemanticPrioritizedFields object to reference the exact name of the field (in this case, "content") that you want to prioritize for titles, keywords, or content.
Reference:
https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic
NEW QUESTION # 119
Drag and Drop Question
A data science team plans to evaluate multiple algorithms and hyperparameters for a classification problem without manually authoring separate training scripts.
The team must run an automated machine learning (AutoML) job that compares models and identifies the best-performing configuration.
You need to configure and run an AutoML training job.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 120
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