This content provides a comprehensive overview and study guide for the Google Cloud Generative AI Leader certification exam, detailing its modules, key concepts, Google Cloud offerings, and strategies for successful implementation.
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Hello everyone, my name is Dya and today
I'm here to talk about Google Cloud
Generative AI leader certification exam.
I recently earned the certification and
I'm here to share what helped and uh
some study materials. So let's jump
right in. In this exam there are five
modules that you need to study. The
first module is about the generative AI
uh beyond the chatbot. Then the second
is about unlocking the foundational
concepts and Genai navigate the
landscape. Genai apps transform your
work and Genai agents transform your organization.
organization.
So I have also linked uh some of the uh
study guide and how to register and even
some sample uh questions uh how you can
prepare for the exam that will be helpful.
helpful.
So let's start from the study guide.
The first topic is about introduction
uh to the genai leader
and it the exam focuses your knowledge
in four key areas. It's the found uh uh
fundamentals of generative AI which is
the 30% of the exam and then Google
cloud geni offerings 35% of the exam
techniques to improve genai model output
20% of the exam and business strategies
for successful geni solution it's about
15% of the exam and uh please note that
this study guide is meant to be a
starting point and the exhaustive list
of resources I have linked the resources
U of each topic in this uh
video and you will be able to take that
in detail but let's go in the uh
fundamentals of generative AI. So you
will see um these layers it looks like
the layers of onion. So what is the
outside layer? It's uh artificial
intelligence. What is artificial
intelligence? is the building machine
that can perform tasks that typically
require human intelligence such as
learning and problem solving and
decision. Within that layer of AI, you
will find machine learn. It is a sub
field of AI where machines learn from
data to perform a specific task. Then
within that machine learning, you will
find genai which is an application of
machine learning that focuses on
creating new content. uh the content can
be images, videos. So um coding is an
example of u u generative AI. So we can
use that to generate new content. And
what is a deep learning? Deep learning
is a subset of machine learning that
uses artificial neural networks with
many layers to abstract complex patterns
from data. And then um we will talk
about the foundation models. It's
powerful machine learning model trained
on massive amount of data um allowing
them to develop a broad understanding of
the world and a large language model
it's a foundation model that is designed
to understand and generate human language
language
uh so what is uh genai it can create uh
new content and ideas uh genai
applications can be multimodal enabling
them to process and generate different
type of data like text images, code
simultaneously and genai can be used to
create new content, summarize, discover
and automate the task. Then uh let's
jump in the foundation model. Uh large
uh AI models are trained on massive data
sets allowing them to be adapted to many
tasks. Uh they are basis of genai. What
are the foundation model features? They
are trained on diverse data. They're
flexible to wide range of use cases.
Adaptable to specify
specialized domains through addition
targeted training. Um we will now let's
talk about prompting. Prompting is a
method of interacting with the
foundation model and guiding them. It
involves providing them with
instructions or inputs to generate the
desired output. And prompt engineering
is an art um and science of creating
effective input known as prompts uh for
generative AI model to maximize their
value and tailor the responses to
specific need. And now uh we'll talk
about two types of uh data label data
and unlabelled data. Label data is like
uh that has associated tags such as
name, type, number and unlabelled data
it's kind of law raw unprocessed
information that hasn't been tagged and
it lacks meaning by itself such as
unorganized photos or streams of audio
recordings. Machine learning has uh
three primary learning approaches.
Supervised learning, unsupervised
learning and reinforcement learning.
Supervised learning, it trains model on
labelled data to predict the output for
new input. Unsupervised learning uses
unlabelled data to find natural
groupings and patterns. And
reinforcement learning learns through
interaction and feedback to maximize
reward and uh minimize penalty.
Um now let's talk about the fundamentals
of generative AI. Uh data is in is
information that can come in many forms.
number, date, text, description, even
images and sounds. So structured data is
organized and easy to search, often
stored in relational databases.
Unstructured data that lacks predefined
structure and require sophisticated
analysis uh techniques. Quality data
data is that is accurate, complete and
consistent and relevant. Accessible data
data for model training needs to be
readily available usable in proper
format. Genai landscape uh geni powered
application uh it's a userfacing part of
generative AI. This is the layer that
allows users to interact and leverage
the capabilities of AI. Then what is an
agent? An agent is a piece of software
that learns how to best achieve a goal
based on input and tools available to
it. So the tool is important here. It's
a distinguishing um term when you come
um to agent. uh there will be many
questions about it and I think in my
exam there were at least uh two to three
questions about agent and uh the
differentiating factor was the tool
uh platform it's a layer offers APIs
data management capabilities and model
deployment tool it bridges the gap
between models and agents while
simplifying the complexity and
infrastruure Structure management model
is a complex algorithm trained a vast
train on vast amount of data. It learns
patterns and relationships in data
allowing it to generate new content,
translate languages, answer questions
and much more. What is infrastructure?
This layer provides core computing
resources needed for Genai. uh this
includes physical hardware like servers,
GPUs, TPUs and software needed to store
and run AI models and training data.
Come to the machine learning life cycle.
Uh the first part is data ingestion and
preparation. It is the process of
collecting, cleaning and transforming
raw data into usable uh format for
analysis or model training. And then for
model training uh what does that mean?
It's a process of creating your machine
learning model using that data. And
model deployment, it's a process of
making the train model available for
use. Uh model management, it's the
process of managing and maintaining your
models over time. Let's talk about some
of the Google offerings. Uh what is
Gemini? Gemini supports multimodal
understanding, advanced conversational
AI, content creation, and question
answering. What's GMA? GMA offers
developers user-friendly customizable
solution for local deployments and
specialized AI applications.
Imagine it's textto image diffusion
model that generates highquality images
from textual descriptions and vo it
generates video content based on text
description or still images.
Um now we will learn about gen Google
cloud generative AI offering and Google
uh genai tools are integrated across
Google uh Google's ecosystem and Google
ensures you stay updated with the latest
AI advancements. Google provides an
ecosystem that puts security and ethics
at the front line. Google provides an
enterprisegrade foundation that you can
build build on and Google open approach
gives you flexibility and choice in your
AI solution. There was a question about
this um uh
open approach in the exam.
Um Google tooling for personal uh productivity.
productivity.
Let's talk about Gemini. It's a Google
uh Genai model that pro powers many uh
different solution. The Gemini app is
Google's generative AI chatbot that
provides assistance with task such as
writing, summarizing, translating, and
creating images. Uh with Gemini
Advanced, companies can access extra
features and enterprise grade
protection. Gemini for Google Workspace
integrates Genaii into familiar
workspace apps, allowing you to do
things like compose email and Gmail,
generate images and slides, and
summarize notes and meet. Gemini for
Google Cloud is your AI assistant for
Google Cloud. It can help you write,
debug code, manage and optimize cloud
applications, analyze data in BigQuery,
and strengthen your uh security posture.
Beyond Gemini tools, uh we have notebook
LM. It allows you to upload your files
and then act as a research assistant
summarizing key points, answering
questions and generate ideas all while
staying grounded with your source
material. So there will be a question
about notebook LM. So it allows you to
upload your own content and then it acts
as a research assistant
and um it it stays grounded on the
material that you have uploaded.
Uh what is Vortex AI? It's a Google
cloud uh offering that um uh has unified
machine learning platform. It empowers
you to build, train and deploy machine
learning application. Uh Google AI,
Vert.Ex AI gives you access to genai
models such as Gemini and lets you tune
them to meet your needs and then deploy
them. Vertex AI search, search and
recommendation solution for uh business
uh use it with Gemini API with Google AI
studio or Vert.ex AI Studio. Uh Google
AI Studio is available free of charge
and is meant for quick AI prototyping.
Vert.ex AI studio is bu is for building
and deploying production ready AI
applications at scale. So there was a
question uh about this where like you
know how these two are different. Vertex
AI is uh studio is kind of for
enterprise level and this uh Google AI
studio is available for uh free of
charge and it's meant for you know quick
uh AI prototyping.
um customer uh engagement suite uh tools
to support your company in engaging with
customers effectively. They can be built
on top of Google's contact center as a
service CCA.
There was a question on this as well. An
enterprisegrade contact center solution
that is native to cloud. Uh
conversational agents act as effective
chat bots uh to your customers. Agent
assist support your live human contact
center agents and conversational insight
gain insight into all your communication
with the customer. So agent assist I
think this was one of the topic that
there were a couple of questions on it
was to support uh the live human contact
agent. Um, Gemini enterprise uh
integrate customized search and
conversational agent that can access and
understand data from various internal
sources into your organizations or
internal websites or dashboards. Tooling
uh there are like couple of things very
important again from the exam point of
view. What are extensions? They are used
to connect with external services via
APIs. What are functions? they uh define
a specific action or task. Data stores
provide access to the information and
plugins are like you know you add new
skills and integrations.
Uh Google cloud generative AI is
offering is continued here. Uh what are
agents? And um a geni agent is an
application that tries to achieve a goal
by observing the world and acting upon
it using the tools that are at its uh
disposal. Uh the components of an agent
is like a reasoning loop, tool and
model. A reasoning loop is an iterative
process where an agent observes,
interprets, reasons and acts often using
prompt engineering. Uh the tools are the
functionalities that allow the agent to
interact with its environment such as
accessing and processing or interacting
with the hardware and model is the brain
of the AI system which consists of
various algorithms that uh learn
patterns from data and can make
predictions or gen uh new uh generate
new uh content. Uh types of agent
deterministic, generative and hybrid.
Deterministic or also called as
traditional agent agents uh that are
built with predefined paths and actions.
Uh generative agents are that are
defined with natural language uh using
LLMs to give real conversational field
uh feel to your chatbot and hybrid
agents. They are these like combine both
deterministic and generative
capabilities and uh this is a
combination. It makes them more
powerful. agents can um serve several
different functions. Some of the
examples are customer service agent,
employee productivity, uh creative
agents, code agent, data agent, security
agent and I think there was one um agent
for uh uh Google maps uh that also came
um in the exam
uh platform the foundation for building
and scaling AI initiatives uh as Google
cloud's unified machine learning
platform vertex AI is designed to
streamline the entire machine learning
workflow. It provides the infrastructure
tools and pre-trained models you need to
build, deploy and uh manage your machine
learning and generative AI solution.
With Vertex AI machine learning op
tools, uh AI teams can better
collaborate to monitor and improve their
models model. Now, Vert.xi XAI gives you
options for how to handle AI models for
your project uh through model garden and
model builder. Model garden you can pick
from existing Google third-party or open
source uh models and model builder is uh
where you train and use your own models
and go fully custom to create and train
models at a scale using an machine
learning framework or use AutoML to
create and train models with minimal uh
technical knowledge and effort
infrastructure. It provides the core
computing resources uh needed for geni.
This includes the physical hardware such
as uh servers, GPUs, TPUs along with the
essential software needed to train,
store and run AI model, AI on the edge,
you can run uh AI solutions on
infrastructure devices or servers closer
to where the action is happening. And
Google uh provides tools such as uh run
uh light runtime to help developers
deploy AI model on edge devices and
Gemini Nano is uh Google's most
efficient and compact AI model is
specifically designed to run on devices.
Um now let's come to the Google cloud
generative AI offerings APIs. Uh
speechtoext API. The API convert speech
into text. It also transcribes audio and
video content. Textto speech API. It
converts text to natural sounding
speech. The API also creates voice um
user interfaces and personalized
communication. Translation API. The
translation API translates text,
documents, websites, audios and video
files. Document translation API. It
translates format formatted documents
while keeping the original layout.
uh document AI API. The document AI API
extracts data from uh the document and
automates data capture and document
processing. API can be used to summarize
document cloud vision API. This API
analyzes the image content tagging
images based on detected objects and
text. It can also identify faces and
landmarks. The API also supports uh
cases like content moderation and visual
search. Uh cloud video intelligence API
allows developers to analyze the video
content and extract meaningful info uh
content recommendation, video search and
media analysis. And natural language API
helps uh derive insights from
unstructured text, understand the
sentiment of the text, classify content
and extract important entities building
applications uh from your agents. You
can access Gemini API tool uh via tools
like Google cloud developer tools like
cloud run functions and cloud run and
low code and no code tooling like app
scripts and app sheet.
Now techniques to improve geni model
output. So they are prompting
techniques. Um zeros short asking the
model to complete a task uh with no
prior example. So in zero short you do
not give any example. Um one shot you
provide the model with one example to
learn from. Few short you provide the
model uh with a few or multiple examples
to learn from. and role. You assign a
persona to the model to influence its
style, tone uh and focus. Say for
example uh you ask uh in your prompt act
as a market researcher to do some to uh
something. So that is a role of market
researcher and prompt chaining engaging
in back and forth uh conversation with
the AI. So these are some prompting
techniques to improve the model output.
Uh let's uh talk about the reasoning
loop. Uh prompt engineering technique.
Uh react, reason and act. Allow the LLM
to reason and take action on a user
query. Uh chain of thought uh guide an
LLM through a problem solving process by
providing examples with intermediate uh
reasoning steps and meta prompting using
prompting to guide uh the AI model to
generate, modify and interpret other props.
props.
model guidance and refinement uh
grounding. There will definitely be a
question or a few questions on grounding
and retrieval augmented generation. This
is a very important topic. Um so uh
basically if you do not want uh your
model to hallucinate or it should only
use um the uh uh the content or the
knowledge that you have uploaded uh when
it is responding uh to your queries. it
is very important to ground it on that
data. So grounding is connecting the
AI's output to verifiable sources of
information and retrieval augmented
generation. Um you can say it um it it
comprises of four parts. First is
retrieval. The LLM retrieves the
relevant information from external
sources using tooling. Augmented
augmentation the retrieve information is
incorporated into the prompt uh to the
LLM generation the LLM processes the
prompt and then generates a response and
iteration it's optional the LLM can
iterate on a retrieval process as
necessary. So a person is uh sending a
prompt to the model the model queries um
the vector data and then it actually
responds um back. So when you do
retrieval augmented generation, it's uh
one of the way to ground and make your
Now let's come to streamlining uh
prompting workflows. Uh, reusing
prompts, saving prompts as templates for
repeated use, leveraging prompt
chaining, continuing conversation within
the same chatboard to maintain context,
using saved info in Gemini, storing
specific information for model to use
consistently. Gems. Gems are
personalized AI assistant within Gemini.
They use personalized responses tailored
to specific instruction. They also
streamline workflows such as uh
templates, prompts, and guided
interaction. Uh sampling uh parameters
uh setting that influence the AI model
behavior allowing for customized uh
results. Uh token count. This represents
meaningful chunks of text like words and
punctuation. Temperature. This parameter
controls the creativity or randomness of
the model's word uh choices during text
generation. Uh top p nucleus sampling
the cumulative probability of the most
likely tokens considered during text
generation. This is another way of
controlling randomness of the model
output. Uh safety settings. These
settings allow you to filter out
potentially harmful or inappropriate
content from the model's output output
length. This determines the maximum
length of uh generated output.
Now let's come to the techniques to
improve genai um AI model. Uh continuing
that the foundation uh model limitation
data dependency the foundation model
performance relies heavily on data.
biased or incomplete data will affect
their output. Knowledge cutoff AI models
are trained up to specific knowledge
cutff date meaning the uh there might be
uh lack of information about events
after that point. Bias LLMs learn from
large data sets which may contain biases
even subtle biases can have a magnified
impact on the model output. Fairness.
Assessing the fairness of geni ai models
is a key aspect of responsible
development. Hallucination when AI model
produces outputs that aren't accurate or
based on real information. This is
called hallucination. We talked about
it. Using grounding, we can prevent uh
some of the hallucination. Edge cases.
rare and atypical scenarios can expose
um models weaknesses leading to
unexpected um results.
Um so now we will talk about um human in
the loop. Uh the this is a process where
human input and feedback are directly
integrated into machine learning
workflows. Uh content moderation human
in the loop ensures user generated
content is moderately moderated
contextually catching harmful material
algorithm um might overlook. Sensitive
application human in the loop provides
critical oversight in fields like
healthcare and finance ensuring accuracy
and reducing risk from automated
systems. High-risk decision making uh
for high stakes decisions human in the
loop can help uh safeguard accuracy and
accountability through human review and
machine learning model output. Uh
pre-generation review human uh experts
review and validate machine learning
output before deployment catching errors
and biases before user impact.
Postgeneration review, continuous review
and feedback after deployment might help
machine learning model improve and adapt
uh to changing context and user needs.
Um additional terms context window the
amount of data um amount of text the
model can consider fine-tuning. It's a
technique used to enhance the pre-train
or foundation models performance for a
specific task or domains. Let's come to
managing your model. Um, Google Cloud
offers tools for managing the entire
life cycle of machine learning models.
This includes versioning. Uh, versioning
is keeping track of various versions of
the model within the Vert.Ex AI model
registry. Um, performance tracking, it's
uh to review the model metrics to check
the model's performance. Drift
monitoring, it's to watch uh for changes
in model accuracy over time with Vert.ex
Vertex AI model monitoring data
management it you it's used for um
Vert.x Vertex AI feature store to manage
and data features u the model uses the
storage um use vortex AI model garden to
store and organize models in one place
automate use vortex AI pipelines to
automate machine learning task now let's
come to business strategies for
successful genai solution before
starting your genai pro uh project
consider the
uh scale, how many users will be there?
Customization, how specialized is the
Genaii solution, user interaction, how
many users will engage, privacy, how
sensitive is the data, latency, what is
the response time you can have?
Connectivity, what is your connectivity?
Resources, people uh do you have access
uh to people who have AI expertise or
money? Uh what is your budget and time?
What is your project uh timeline? Genai
strategy uh there were two strategies
like top down and bottom up. So in
Google we uh Google recommends um a mix
of both uh it has to come from top down
and also bottom up. uh so it's uh a
combined approach a top- down and bottom
up uh approach where the leadership sets
a vision and a strategy and employees
identify practical applications by
providing feedback. Uh so what are the
advantages of this uh combined uh a geni
strategy? Uh it's u it has uh the focus
you pro prioritize focused geni
implementation with clear business value
exploration encourage experimentation
and collaboration to discover valuable
genai application. Responsible AI
establish ethical guidelines and ensure
secure and responsible AI development
resourcing. Invest in data strategy.
Leverage existing uh resources and
develop AI talent impact. Um measure GI
impact on business goals and demonstrate
tangible benefit. Uh continuously refine
um geni solution based on feedback uh
and data. Secure AI protect your AI
applications from harm. Um and using the
uh secure AI framework. It helps uh
organization manage AI ML model risks
and ensure security. Google cloud secure
by design infrastructure helps uh
support security across AIML life cycle.
Various uh tools help protect data
models and application. Identity and
access management IM for controlling
resources access. Security command
center for security posture visibility
workload monitoring tool to help build
and maintain secure AI system. Factors
when choosing a a model for your use
case modality. Choose a generative AI
model whose input and output data types
modality align with your application
specific needs whether it's text, image,
audio or video. context v uh window you
may need to balance an AI model ability
to generate coherent and relevant
responses against uh the increased
computational cost performance a model
accuracy speed and efficiency are
critical factors consider the trade-offs
uh between performance and cost
availability and uh reliability choose a
model that is consistently available and
performs reliably under load consider
factors like uptime guarantees redency
and disaster recovery mechanism. Uh what
is responsible AI? Ensuring your AI dev
applications don't cause harm and are
used uh in an ethical manner.
Responsible AI needs to be considered
throughout the entire AI life cycle from
uh data preparation and model training
and deployment to ongoing uh monitoring.
to plan for your Genai strategy.
Establish a clear vision, prioritize use
cases, invest in capabilities, manage
change, measure value, and champion
responsible AI.
Now, uh if you want to create your own
um genai uh study guide, you can go to
notebook LM, upload the study materials
there, and you will be able to create um
a study guide. you can uh create um you
know questions, quizzes uh and uh it is
going to help you and I hope uh this
overview of Genai leader was helpful and
you are able to learn and earn your
certification using this guide and uh
let us know in comment if uh you earn it
and we will be happy to celebrate and
congratulate you on your achievement.
ment. See you in the next video for
another U certification preparation. Bye.
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