Cloud-Digital-Leader Exam Dumps - Google Cloud Digital Leader exam

The correct answer is A. A specific field of AI that can be used to create content. Here's why:

Context of the Question: The question is asking about generative AI, a specialized area within the broader field of artificial intelligence.

Google Cloud Product Relevance:

Generative AI refers to a branch of artificial intelligence that focuses on generating new content, such as text, images, audio, and other data. It uses machine learning models, such as generative adversarial networks (GANs) and transformer models (like GPT), to create new data that mimics the patterns of existing data.

This type of AI is used in various applications, including text generation, content creation, image synthesis, and more.

Why Not Other Options:

B. An umbrella term that includes all types of machine learning algorithms: This is incorrect because generative AI is a specific field within AI, not an umbrella term for all AI types.

C. An AI model for labeling images: Generative AI creates content, while labeling images falls under supervised learning.

D. A machine learning model for language translation: Language translation is a different AI application and does not specifically involve generating new content.

Google Cloud Digital Leader References:

Refer to the Google Cloud Generative AI documentation for more information on generative AI models and applications.

The correct answer is B. Use BigQuery ML and create models using SQL. Here's why:

Context of the Question: The organization wants to predict property popularity for the upcoming busy season but does not have a data science team and relies on in-house database administration skills.

Google Cloud Product Relevance:

BigQuery ML enables data analysts and database administrators to build and run machine learning models using SQL queries directly within BigQuery. This makes it accessible to users familiar with SQL, eliminating the need for advanced data science skills.

BigQuery ML is ideal for this organization since it allows them to leverage their existing SQL skills to build predictive models without the complexity of custom machine learning frameworks or coding in languages like Python.

Why Not Other Options:

A. Use custom training in Vertex AI with TensorFlow: This requires expertise in TensorFlow and machine learning, which the organization lacks.

C. Build a model in AutoML using labeled data: AutoML does simplify the ML model-building process but still requires more knowledge than basic SQL. Also, BigQuery ML allows the use of existing SQL skills, which is preferred in this scenario.

D. Integrate pre-trained APIs into their application: Pre-trained APIs are suitable for common use cases like image recognition or natural language processing, not for custom predictions based on unique data like property popularity.

Google Cloud Digital Leader References:

Refer to BigQuery ML documentation for more details on using SQL to create and use ML models.