Answer-first summary for fast verification
Answer: Employ AutoML Natural Language to extract custom entities for classification, leveraging its ability to automatically identify and extract custom entities with minimal preprocessing., Combine the use of AutoML Natural Language for entity extraction with a custom classification algorithm for finer control over the classification process, balancing between development time and customization.
**Correct Answers: C - Employ AutoML Natural Language to extract custom entities for classification, and E - Combine the use of AutoML Natural Language for entity extraction with a custom classification algorithm for finer control over the classification process.** **Why C?** - **Minimal Data Preprocessing:** AutoML Natural Language is optimized for text data, requiring little to no preprocessing, which aligns perfectly with your scenario where calls are already transcribed. - **Custom Entity Extraction:** It automatically identifies and extracts custom entities from your text, serving as features for classification, thereby bypassing the need for manual feature engineering. - **Built-in Classification:** Beyond entity extraction, AutoML Natural Language can directly perform classification, offering a comprehensive solution. - **Efficiency:** It accelerates model building and training, significantly reducing development time. **Why E?** - **Balanced Approach:** Combining AutoML Natural Language for entity extraction with a custom classification algorithm allows for a balance between minimizing development time and achieving finer control over the classification process. - **Scalability and Cost-Effectiveness:** This approach leverages the strengths of both AutoML and custom development, ensuring the solution is both scalable and cost-effective. **Other Options Considered:** - **A - Cloud Natural Language API:** While capable of entity extraction, it may not match AutoML Natural Language's efficiency or ease of use. - **B - Custom Model:** Constructing a model from scratch demands more time and expertise, potentially overcomplicating the task. - **D - AI Platform Training Built-in Algorithms:** These might necessitate additional manual feature engineering and tuning, prolonging the development process.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
You are tasked with modernizing the contact center for a large technology company to classify incoming calls by product, aiming to expedite routing to the correct support team. The calls have already been transcribed using the Speech-to-Text API. Your primary objectives are to minimize both data preprocessing and development time while ensuring the solution is scalable and cost-effective. Given these constraints, which approach should you take to build the model? Choose the best option.
A
Utilize the Cloud Natural Language API to extract custom entities for classification, considering its ease of integration and predefined models.
B
Develop a custom model from scratch to identify product keywords from the transcribed calls and then apply a classification algorithm, offering maximum customization but requiring significant development effort.
C
Employ AutoML Natural Language to extract custom entities for classification, leveraging its ability to automatically identify and extract custom entities with minimal preprocessing.
D
Use the AI Platform Training built-in algorithms to create a custom model, which may require additional feature engineering but offers flexibility in model selection.
E
Combine the use of AutoML Natural Language for entity extraction with a custom classification algorithm for finer control over the classification process, balancing between development time and customization.