Tips on passing Salesforce AI Associate Certification

 

🌟 Motivation to Pursue the Salesforce AI Associate Certification 🌟

The world of technology is in a state of perpetual evolution, and one of the most transformative forces driving this change is Artificial Intelligence (AI). As businesses and organizations strive to remain competitive and innovative, the integration of AI into their operations has become not just an advantage but a necessity. It is with this conviction and aspiration for growth and excellence that I find my motivation to pursue the Salesforce AI Associate Certification.
Here are my key motivators:

Certainly, here are three concise points highlighting the motivation to pursue the Salesforce AI Associate Certification:

1. Professional Advancement:The certification offers opportunities for career growth, credibility, and access to exciting job prospects in the ever-evolving tech landscape.

2. Innovation Impact:Earning this certification empowers individuals to contribute to innovation by leveraging AI to solve real-world business challenges.

3. Personal Fulfillment:Pursuing the certification is a fulfilling journey of continuous learning, personal growth, and being part of a thriving tech community.

"Successfully took the Salesforce Certified AI Associate exam and thrilled to announce that I passed! 🎉 For those preparing, I'm sharing my notes below.

To ace the exam, I highly recommend completing these two Trailhead modules:

1. Prepare for Your Salesforce AI Associate Credential

2. Certification Preparation for Salesforce AI Associate

These modules cover the essential high-level AI and Einstein capabilities you'll need to excel on the exam. Best of luck on your certification journey! 

🚀 #SalesforceAI #CertificationSuccess"

Main Types of AI Capabilities

1. Numeric Predictions
2. Classifications
3. Robotic Navigation
4. Language Processing

Yes-and-No Predictions

The first ingredient is yes-and-no predictions. Yes-and-no predictions allow you to answer questions like, “Is this a good lead for my business?” or “Will this prospect open my email?” AI helps you answer these questions by scanning historical data you’ve stored in your system.
Yes-and-no predictions generally come in the form of a probability (for example, “Mary Smith has a 67% chance of opening this type of email). But sometimes probabilities are converted into scores. Scores are just a different representation of the likelihood of “yes”; they can be represented as numbers on a numeric scale (for example, 0 to 100) or even as the number of stars on a five-star rating survey. Keep in mind that these scores are just showing the same probability in a different way.

Numeric Predictions

Next, are numeric predictions. Numeric predictions often power predictive forecasting solutions (for example, “How much revenue will this new customer bring in?”), but they are also used in other contexts like customer service (for example, “How many days will it take us to resolve this customer’s issue?”). Numeric predictions also use your historical data to arrive at these numbers.

Classifications

Next, we have classifications. Classifications frequently use “deep learning” capabilities to operate on unstructured data like free text or images. The idea behind classification is to extract useful information from unstructured data and answer questions like, “How many soda cans are in this picture?” It can even take a statement like, “I’d like to buy another pair of the same shoes I bought last time,” and use that to kick off a workflow that can look up the last shoe order and place the same pair of shoes in their online shopping cart.
Classification using deep learning is very robust, even when the unstructured data is arriving in different forms. Take the previous example of shoes. You could just say, “I want another pair of those shoes” or “Give me another one of those.” No matter how the request is phrased, the underlying deep learning engine built into an AI platform can generally understand them all, in much the same way that your brain can.
Another type of classification—which may or may not use deep learning—is called clustering. This type of AI ingredient gathers insights from your data that you may not otherwise have noticed. For example, if you are a clothing vendor, AI might learn that both rural older men and urban twenty somethings like to buy a certain type of sweater. Where your intuition might tell you that these are two totally different groups, the data shows they behave similarly with respect to the products they buy, and you may want to market to those two groups in a similar way.

Recommendations

Last, are recommendations. Recommendations are key when you have a large set of items that you’d like to recommend to users. Many ecommerce websites apply recommendation strategies to products; they can detect that people who bought a specific pair of shoes also often order a certain pair of socks. When a user puts those shoes in their cart, AI automatically recommends the same socks.
Recommendations are not just for products. Marketers use the same technique to recommend content like whitepapers to business users. Employers might use recommendations with their HR recruiting system to recommend job postings to job candidates.

Workflow and Rules

Workflow and rules aren’t technically part of AI, but they’re an essential part of how AI is used. Take the following example. Let’s say that AI predicts a given customer has a 25% likelihood of not renewing their contract. Just knowing this is not enough—you need to do something about it. That’s where workflow and rules come into play. In this example, your workflow might mean kicking off a retention campaign when the AI predicts that a customer is unlikely to renew.
With these four fundamental AI ingredients, you can produce custom AI applications that meet a variety of business needs. In the next unit, you learn how to get started with your own AI solutions.

Most Important component of AI

1. NLU (Natural Language Understanding)
2. NLP ( Natural Language Processing)
3. NER (Named Entity Recognition)
4. Deep learning

What is natural language understanding (NLU)?

Natural language understanding (NLU) is a branch of artificial intelligence (AI) that uses computer software to understand input in the form of sentences using text or speech. NLU enables human-computer interaction by analyzing language versus just words.

NLU enables computers to understand the sentiments expressed in a natural language used by humans, such as English, French or Mandarin, without the formalized syntax of computer languages. NLU also enables computers to communicate back to humans in their own languages.
A basic form of NLU is called parsing, which takes written text and converts it into a structured format for computers to understand. Instead of relying on computer language syntax, NLU enables a computer to comprehend and respond to human-written text.
One of the main purposes of NLU is to create chat- and voice-enabled bots that can interact with people without supervision. Many startups, as well as major IT companies, such as Amazon, Apple, Google and Microsoft, either have or are working on NLU projects and language models.

What is natural language processing (NLP)?

Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

What is named entity recognition (NER) and how can I use it?

Named entity recognition (NER) — sometimes referred to as entity chunking, extraction, or identification — is the task of identifying and categorizing key information (entities) in text. An entity can be any word or series of words that consistently refers to the same thing. Every detected entity is classified into a predetermined category. For example, an NER machine learning (ML) model might detect the word “super.AI” in a text and classify it as a “Company”.
NER is a form of natural language processing (NLP), a subfield of artificial intelligence. NLP is concerned with computers processing and analyzing natural language, i.e., any language that has developed naturally, rather than artificially, such as with computer coding languages.

How NER works?

At the heart of any NER model is a two step process:

1. Detect a named entity
2. Categorize the entity

Beneath this lie a couple of things.

What is deep learning?

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.
Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

Generative AI vs. Predictive AI

At their foundation, both generative AI and and predictive AI use machine learning. However, generative AI turns machine learning inputs into content whereas predictive AI uses machine learning in an attempt to determine the future and prevent bad outcomes by using data to identify early warning signs.

Among the key differences between generative AI and predictive AI:

Creativity – generative AI is creative and produces things that have never existed before. Predictive AI lacks the element of content creation.

Inferring the future – predictive AI is all about using historical and current data to spot patterns and extrapolate potential futures. Generative AI also spots patterns but combines them into unique new forms.

Different algorithms – generative AI uses complex algorithms and deep learning to generate new content based on the data it is trained on. Predictive AI generally relies on statistical algorithms and machine learning to analyze data and make predictions.

Both generative AI and predictive AI use artificial intelligence algorithms to obtain their results. You can see this difference shown in how they are used. Generative AI generally finds a home in creative fields like art, music and fashion. Predictive AI is more commonly found in finance, healthcare and marketing – although there is plenty of overlap.
Now let’s take a deeper look at both generative AI and predictive AI.

What is Generative AI?

Generative AI functionality is all about creating content. It combines algorithms and deep learning neural network techniques to generate content that is based on the patterns it observes in other content.

Although the output of generative AI is classified as original material, in reality it uses machine learning and other AI techniques based on the earlier creativity of others – this is a major criticism of generative AI. This emerging AI technology taps into massive repositories of content and uses that information to mimic human creativity.
Generative AI systems use standard machine learning techniques as part of the creative process. Generative AI can do things like analyze the entire works of Dickens or Rollins or Hemingway and produce an original novel that seeks to simulate their style and writing patterns.
Thus, generative AI goes a stage beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI offers a tech-based foray into the world of creativity.

Generative AI Use Cases

By producing fresh content, generative AI is being used to augment but not replace the work of writers, graphic designers, artists and musicians. It is particularly useful in the business realm in areas like product descriptions, variations to existing designs or helping an artist explore different concepts. Among its most common use cases:

1. Text – generative AI can generate credible text on various topics. It can compose business letters, provide rough drafts of articles and compose annual reports.
2. Images – generative AI can also output realistic images from text prompts, create new scenes and simulate a new painting.
3. Video – generative can compile video content from text automatically and put together short videos using existing images.
4. Music – generative AI can compile new musical content by analyzing a music catalog and rendering a new composition.
5. Product design – generative AI can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a new version.
6. Personalization – generative AI can personalize experiences for users such as product recommendations, tailored experiences and feeding material that closely matches their preferences.

What is Predictive AI?

Predictive AI studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions. Predictive AI’s value is shown in the ways it can detect data flow anomalies and extrapolate how they will play out in the future in terms of results or behavior; enhance business decisions by identifying a customer’s purchasing propensity as well as upsell potential; and improve business outcomes.

Significantly, predictive AI can enlighten management on future trends, opportunities and threats. It can be used to recommend products, upsell, improve customer service and fine-tune inventory levels.
Predictive AI adds another dimension and greater accuracy to the processes of management. Used correctly, it increases the chance of success and achieving positive business and outcomes, particularly in the area of inventory management.
Through accurate predictions and improved decision-making, predictive AI can help organizations glean far more value from the data they collect and use it to their competitive business advantage.

Predictive AI Use Cases

Predictive AI has a great many use cases. Some of the top ones include financial forecasting, fraud detection, healthcare and marketing.

1. Financial Services – predictive AI enhances financial forecasts. By pulling data from a wider data set and correlating financial information with other forward-looking business data, forecasting accuracy can be greatly improved.
2. Fraud Detection – predictive AI can be used to spot potential fraud by sensing anomalous behavior. In banking and e-commerce, there might be an unusual device, location or request that doesn’t fit with the normal behavior of a specific user. A login from a suspicious IP address, for example, is an obvious red flag.
3. Healthcare – predictive AI is already in use in healthcare. It is finding use cases such as predicting disease outbreaks, identifying higher-risk patients and spotting the most successful treatments.
4. Marketing – predictive AI can more closely define the most appropriate channels and messages to use in marketing. It can provide marketing strategists with the data they need to write impactful campaigns and thereby bring about greater success.

Predictive AI, therefore, is finding innumerable use cases across a wide range of industries. If managers knew the future, they would always take appropriate steps to capitalize on how things are going to turn out. Anything that improves the likelihood of knowing the future has high value in business.

Common Concerns About Generative AI

Generative AI is going to lead to many changes in how we interact with computers. With any disruptive technology, it’s important to understand its limitations and causes for concern. Here are a few of the main concerns with generative AI.

1. Hallucinations: Remember that generative AI is really another form of prediction, and sometimes predictions are wrong. Predictions from generative AI that diverge from an expected response, grounded in facts, are known as hallucinations. They happen for a few reasons, like if the training data was incomplete or biased, or if the model was not designed well. So with any AI generated text, take the time to verify the content is factually correct.
2. Data security: Businesses can share proprietary data at two points in the generative AI lifecycle. First, when fine-tuning a foundational model. Second, when actually using the model to process a request with sensitive data. Companies that offer AI services must demonstrate that trust is paramount and that data will always be protected.
3. Plagiarism: LLMs and AI models for image generation are typically trained on publicly available data. There’s the possibility that the model will learn a style and replicate that style. Businesses developing foundational models must take steps to add variation into the generated content. Also, they may need to curate the training data to remove samples at the request of content creators.
4. User spoofing: It’s easier than ever to create a believable online profile, complete with an AI generated picture. Fake users like this can interact with real users (and other fake users), in a very realistic way. That makes it hard for businesses to identify bot networks that promote their own bot content.
5. Sustainability: The computing power required to train AI models is immense, and the processors doing the math require a lot of actual power to run. As models get bigger, so do their carbon footprints. Fortunately, once a model is trained it takes relatively little power to process requests. And, renewable energy is expanding almost as fast as AI adoption!

What Is Einstein Discovery?

Salesforce Einstein Discovery augments your business intelligence with statistical modeling and supervised machine learning in a no-code-required, rapid-iteration environment.

Einstein Discovery enables you to:

• Identify, surface, and visualize insights into your business data.
• Predict future outcomes and suggest ways to improve predicted outcomes in your workflows.

Note: Einstein Discovery requires either the CRM Analytics Plus license or Einstein Predictions license, both of which are available for an extra cost.

Target Business Outcomes to Improve

Begin by selecting a business problem you want to solve, typically monitored as a key performance indicator (KPI). Einstein Discovery-powered solutions address these use cases:

• Regressions for numeric outcomes represented as quantitative data (measures), such as currency, counts, or any other quantity.
• Binary classification for text outcomes with only two possible results. These are typically yes or no questions that are expressed in business terms, such as churned or not churned, opportunity won or lost, employee retained or not retained, and so on.
• Multiclass classification for text outcomes with 3 to 10 possible results. For example, a manufacturer can predict, based on customer attributes, which of five service contracts a customer is most likely to choose.

Data Quality

https://trailhead.salesforce.com/content/learn/modules/data_quality/data_quality_improve_quality

With as little friction as possible, Einstein allows all Salesforce users to:

• Discover insights that bring new clarity about your company’s customers.
• Predict outcomes so your users can make decisions with confidence.
• Recommend the best actions to make the most out of every engagement.
• Automate routine tasks so your users can focus on customer success.
• New: Generate tailored content - from emails, to knowledge articles, to code.

Sales Cloud Einstein

• Boost win rates by prioritizing leads and opportunities most likely to convert.
• Discover pipeline trends and take action by analyzing sales cycles with prepackaged best practices.
• Maximize time spent selling by automating data capture.
• Generate relevant outreach automatically with CRM data.

Service Cloud Einstein

• Accelerate case resolution by automatically predicting and populating fields on incoming cases to save time and reduce repetitive tasks.
• Increase call deflection by resolving routine customer requests on real-time digital channels like web and mobile chat or mobile messaging.
• Reduce handle time by collecting and qualifying customer info for seamless agent handoff.
• Solve issues faster by giving your agents intelligent, in-context conversation suggestions and knowledge recommendations.
• Create tailored service replies, knowledge articles, and work summaries automatically with CRM data.

Marketing Cloud Einstein

• Know your audience more deeply by uncovering consumer insights and making predictions.
• Engage more effectively by suggesting when and on which channels to reach out to customers.
• Create personalized messages and content based on consumer preferences and intent.
• Be more productive by streamlining marketing operations.
• Generate subject lines and web campaigns automatically with CRM data.

Commerce Cloud Einstein

• Increase revenue by showing shoppers the best products for them, and eliminate the time-consuming activity of manually merchandising each individual page.
• Create highly visual dashboards to get a snapshot of your customer’s buying patterns and use these dashboards to power up your merchandising.
• Personalize the explicit search (search via the search box), implicit search (browsing in the storefront catalog), and category pages for every shopper, saving your customers time and bringing your business more revenue.
• Generate smart product descriptions automatically to increase conversions.

Einstein Bots

Einstein Bots allow you to build a smart assistant into your customers’ favorite channels like chat, messaging or voice. Einstein Bots use Natural Language Processing (NLP) to provide instant help for customers by answering common questions or gathering the right information to handoff the conversation seamlessly to the right agent for more complex questions or cases.

Einstein Prediction Builder

Einstein Prediction Builder is a simple point-click wizard that allows you to make custom predictions on your non-encrypted Salesforce data, fast. You can create predictions for any part of your business—across sales, service, marketing, commerce, IT, finance, and even HR—with clicks, not code.

Einstein Next Best Action

Einstein Next Best Action (NBA) allows you to use rules-based and predictive models to provide anyone in your business with intelligent, contextual recommendations and offers. Actions are delivered at the moment of maximum impact—surfacing insights directly within Salesforce.

Einstein Discovery

Like Einstein Prediction Builder, Einstein Discovery also predicts outcomes without requiring your own data scientist.

Einstein GPT

The latest Salesforce Einstein innovation is Einstein GPT, the world’s first trusted generative AI for CRM. The rise of generative AI has sparked one of the most significant technological shifts in business since the introduction of the internet. Generative AI, such as ChatGPT, is set to transform how organizations and their customers interact, leading to more personalized, collaborative, and conversational connections.
Einstein GPT allows businesses to generate personalized and relevant content by grounding large language models (LLMs) in their CRM data safely and securely.

It’s built on Hyperforce, our trusted infrastructure platform, to address data privacy and compliance concerns with our best-in-class security guardrails. Einstein GPT is also highly customer aware with pre-built connections to Data Cloud, offering real-time insights on the billions of customer events that occur daily. That means that every piece of content generated, whether it’s an email, a report, a knowledge article or a piece of code, is hyper-relevant to your customers.

Recommended Sales Cloud Einstein Rollout Tasks

Stage	Task	Details
Pre-enablement	Understand business challenges and priorities.	Here’s your chance to get the inside scoop on the real-life issues sales reps have. Talk with the leadership team and get their input about what’s most important.
	Decide which Sales Cloud Einstein features to start with.	After you prioritize your business needs, it’s easy to figure out which features to start with. Don’t try to adopt all features at once, but do think about the future.
	Define goals and criteria for success.	Make sure everyone is aligned with what you want to get out of Sales Cloud Einstein and how you’ll know if you’re hitting the mark.
	Select an initial group of users.	Identify a small but varied group of users to test the features. They’ll help you iron out the wrinkles.
	Make sure the Salesforce org is ready for Einstein.	Data is key, so make sure yours is clean and plentiful. We have tools to evaluate your data readiness.
Enablement	Turn on the features.	There are a few things you need to do in Setup, but we guide you along the way.
	Test in a sandbox.	Know what you’re getting into. A sandbox is great for testing how Sales Cloud Einstein features work with your existing architecture, workflows, and Lightning components.
	Start communicating with and training your users.	Tell your users what you want them to get out of Sales Cloud Einstein, and then show them how to do it.
	Assign users to Sales Cloud Einstein.	After the features are set up and your team is ready, you can open the door to Einstein.
Post-enablement	Get feedback from users.	Give users ways to share their feedback, thoughts, and questions. Try Chatter groups, check-in meetings, surveys, you name it. Just get them talking (or typing).
	Expand your deployment.	Learn from small user groups, then make necessary adjustments, and assign more licenses. You might also be ready to start using more Sales Cloud Einstein features.

Data Quality reasons

1. Missing Records
2. Duplicate Records
3. No Data Standards
4. Incomplete Records
5. Stale Data

In fact, bad data is consistently linked with:

1. Lost revenue
2. Missing or inaccurate insights
3. Wasted time and resources
4. Inefficiency
5. Slow info retrieval
6. Poor customer service
7. Reputational damage
8. Decreased adoption by reps

Good Data Rocks Your World

It turns out that good data lets your company:

• Prospect and target new customers
• Identify cross-sell and upsell opportunities
• Gain account insights
• Increase efficiency
• Retrieve the right info fast
• Build trust with customers
• Increase adoption by reps
• Plan and align territories better
• Score and route leads faster