Building and Using Google Cloud AI and ML Solutions CLOUD-DIGITAL-LEADER Practice Questions (Page 4)

Questions 16–20

1. 16expert · hard

   A legal firm wants to build an AI system that drafts initial responses to client inquiries based on their historical case files and legal precedents. They need the system to cite specific sources from their own documents and adapt to their firm's writing style. They have a small ML team and want to differentiate their service. Which approach is most appropriate?

   • AUse the pre-trained Natural Language API to generate draft responses
   • BUse the Cloud Translation API to translate generic legal templates into client responses
   • CBuild a custom agent using the Gemini Enterprise Agent Platform (Vertex AI) grounded in their case files
   • DUse AutoML to train a text-generation model on their case files
   Reveal Solution Discussion Select an answer first
2. 17application · medium

   A news agency receives thousands of articles daily and wants to automatically tag each article with the people, organizations, and locations mentioned, so editors can quickly filter by topic. They have no ML team and want a quick, managed solution. What should they use?

   • AUse the Natural Language API to perform entity extraction on each article
   • BUse the Vision API to analyze the article images for entities
   • CUse the Text-to-Speech API to generate audio versions of the articles
   • DUse the Cloud Translation API to translate articles into a common language
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   A customer support team wants to automatically determine whether product reviews are positive, negative, or neutral. Which Google Cloud API is most appropriate for this task?

   • AVision API
   • BCloud Translation API
   • CSpeech-to-Text API
   • DNatural Language API
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   A retail company wants to add product recommendations to its website without hiring a dedicated ML team. Which Google Cloud AI/ML product category would let them use a ready-made solution with minimal ML expertise?

   • ACloud TPU
   • BPre-trained APIs
   • CAutoML
   • DCustom models with Vertex AI
   Reveal Solution Discussion Select an answer first
6. 20expert · hard

   A financial analytics firm has a team of data analysts who are experts in SQL but have no Python or ML background. They need to build a churn-prediction model from structured customer data stored in BigQuery. The model must be easy to maintain, and the team wants to avoid managing separate ML infrastructure. However, they also anticipate that in the future they may need to build a more complex deep-learning model on unstructured data. Which approach best balances their current and future needs?

   • AUse TensorFlow on Cloud TPU now to build both the churn model and future models
   • BStart with BigQuery ML for the churn model, and plan to use Vertex AI for future deep-learning needs
   • CUse AutoML Tables now and AutoML Vision for future needs
   • DUse the Natural Language API for the churn model and Vertex AI for future needs
   Reveal Solution Discussion Select an answer first