1. Get the user via a query (you can use a more specific WHERE clause; in my case, I am just querying based on the user’s name)

    SELECT Id FROM User WHERE Name = 'argen'
   

   

   Developer Console > Query Editor > Execute

2. Copy the Id from the query result and paste the following Apex code into the Anonymous Window (or simply run CTRL + E if you are on Windows or CMD + E if you are on Mac).

    String userId = '005***************'; // In your case, you will paste the full Id.
    String tempPassword = 'tempPassword123!'; // Password we set for the user.
    System.setPassword(userId, tempPassword);
   

   

   Click the “Execute” button at the bottom right.

3. You can log in with the new password. Please follow this approach only in Sandbox or Development environments. Also, make sure the user is active and not frozen.

Here are some study notes I've put together from Trailhead.com and Salesforce.com while preparing for my Salesforce Certified AI Associate Exam. These notes are just for learning and might not be perfect, so double-check everything with other sources. Some parts are copied directly.

Main Information

• Content: 40 multiple-choice questions

• Time allotted to complete the exam: 70 minutes

• Passing score: 65%

• Trailmix: Prepare for Your Salesforce AI Associate Credential

• Module: Salesforce AI Associate Certification Prep

• AI basics, its different types such as predictive analytics, machine learning, NLP, and computer vision

• Salesforce's Trusted AI Principles, particularly in the context of CRM systems like Salesforce and its suite of products

• The role of data quality, data preparation/cleansing, and data governance in training and fine-tuning AI models

• Ethical and responsible handling of data, including privacy, bias, security, and compliance considerations

• Ability to engage in meaningful discussions with stakeholders on how AI can be used to improve their business and differing scenarios, including identifying opportunities for AI-driven improvements and potential challenges

Exam Outline:

AI Fundamentals: 17%

• Explain the basic principles and applications of AI within Salesforce.

• Differentiate between the types of AI and their capabilities.

AI Capabilities in CRM: 8%

• Identify CRM AI capabilities.

• Describe the benefits of AI as they apply to CRM.

Ethical Considerations of AI: 39%

• Describe the ethical challenges of AI (for example, human bias in machine learning, lack of transparency, etc.).

• Apply Salesforce's Trusted AI Principles to given scenarios.

Data for AI: 36%

• Describe the importance of data quality.

• Describe the elements/components of data quality.

AI Fundamentals (17%)

Main Types of AI:

• Numeric Predictions

  • AI predictions take the value of 0 (not going to happen) to 1 (totally going to happen)

  • predict next quarter’s sales

• Classifications

  • flag fraudulent transactions

  • diagnose illnesses

  • identify toxic comments

• Robotic Navigation

  • the case of autonomous (hands-free) driving

  • can adapt to changing environmental conditions

  • rescue robots that can traverse disaster areas, such as a collapsed building

• Language Processing

  • NLP → Natural Language Processing.

  • NLP is a huge part of generative AI.

    • subcategory of AI that takes words and turns them into unique images, sounds, and words.

How machine learning works?

The core driver of AI. A process of using algorithms to tell something interesting about data without code. Computers learn from data with minimal programming.

• Natural Language Understanding (NLU) systems handle communication between people and machines

• Natural Language Processing (NLP) machines ability to understand what humans mean when they speak as naturally would to another human.

• Named Entity Recognition (NER) → data labeling. Breaking apart a sentence into segments that a computer can understand and respond to quickly.

• Deep Learning → refers to artificial neural networks being developed between data points in large databases. Connecting dots to give insights, draw conclusions.

All Relies on Data

→ Structured Data: organized, with labels on every column.

→ lets computers do supervised learning

→ Unstructured Data: unorganized, news article, unlabeled image file.

→ unsupervised learning, which is when AI tries to find connections in the data without knowing what’s looking for.

AI is incomplete without Neural Networks

Products SF

Bots → Transparent, Personable, Thorough, Iterative.

Why we need Neural Networks?

Training AI by adding extra layers to find hidden meaning in data is what’s called deep learning.

designing a neural network involves choosing the number of nodes, layers, and the appropriate math for the task it’s training for.

Imagine a skilled talent scout who’s looking for the next great baseball player. “I’ll know him when I see him,” they might say. They can’t explain how they’ll know, they just will. In the same way, our neural network can’t explain why certain factors are important.

AI for Biz

• Yes-and-No Predicitons and Answers

  → AI helps you answer these questions by analyzing historical data.

  → Scores are used.(0 to 100)

• Numeric Predicitions

  → how much revenue?

  → how many days it will take us to resolve this customer’s issue

• Classifications

  → use deep learning to operate on unstructured data like free text or images.

  → clustering: gathers insights from your data that you may not otherwise have noticed. if you are a clothing vendor, AI might learn that both rural older men and urban twentysomethings 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

  → people who bought a specific pair of shoes also often order a certain pair of socks.

  → recommend content like whitepapers to business users

• Workflow And Rules

  • → Workflow and rules aren’t technically part of AI, but they’re an essential part of how AI is used. Use workflow when the AI predicts that a customer is unlikely to renew.

• Summarization

  Gen AI helps summarize chunks of info into quick and easy to digest notes for you.

MARKETING

Send Time Optimization helps predict the best time to send a communication for the highest response rate, specific to each person.

Generative AI

Unlike traditional AI models, generative AI “doesn’t just classify or predict, but creates content of its own […] and, it does so with a human-like command of language.

“AI is only as good as the data you give it and you have to make sure that the datasets are representative.”

Typically by using one of two types of deep learning models:

1. Generative Adversarial Networks (GANs)

   made up of two neural networks: a generator and a discriminator.

   the two networks compete with each other, with the generatot creating an output is real or fake, the generator then fine-tunes its outut based on the discriminator’s feedback, and the cycle continues until stumps the discriminator.

   https://www.youtube.com/watch?v=TpMIssRdhco

2. Transformer Models

   like ChatGPT (Chat Generative Pretrained Transformer), create outputs based on sequential data (like sentences or paragraphs) rather than individual data points.

3. Variational Autoencoders (VAEs)

   rely on on two neural networks to generate new data based on sample data, and neural radiance fields (NeRFs), which is being used to create 2D and 3D images.

   Salesforce’s CodeGen → turn English prompts into executable code.

   Salesforce ProGen Project creates language models based around amino acids instead of letters and words, generative AI was able to produce proteins that have not been found in nature, and in many cases, are more functional.

   But IT leaders are on guard: Nearly six in 10 (59%) said they think generative AI outputs are inaccurate.

Generative AI vs Predictive AI

GenAI → utilizes complex modeling to add a creative element. GenAI software creates images, text, video and software code based on user prompts.

PredAI → uses large data reps to recognize patterns time. PredAI apps draw inferences and suggest outcomes and future trends.

Limitations of GenAI:

• Vulnerability to adversarial attacks

• Contextual ambiguity

• Potential biases

PredAI use cases: financial services, fraud detection, healthcare, marketing.

Natural Language Processing Basics

CS and Linguistics to give computers the ability to understand, interpret, and generate human language in a way that’s meaningful and useful to humans.

NLP was the Turing Test by Alan Turing, the test measures a machine’s ability to answer any questions in a way that’s indistinguishable from a human.

NLP has two subfields

Data processed from unstructured to structured is called natural language understanding (NLU).

Data processed the reverse way–from structured to unstructured–is called natural language generation (NLG).

Elements of natural language in English include:

• Vocabulary: The words we use

• Grammar: The rules governing sentence structure

• Syntax: How words are combined to form sentences according to grammar

• Semantics: The meaning of words, phrases, and sentences

• Pragmatics: The context and intent behind cultural or geographic language use

• Discourse and dialogue: Units larger than a single phrase or sentence, including documents and conversations

• Phonetics and phonology: The sounds we make when we communicate

• Morphology: How parts of words can be combined or uncombined to make new words

Parsing Natural Language

parsing, involve breaking down text or speech into smaller parts to classify them for NLP.

Parsing includes syntactic parsing, where elements of natural language are analyzed to identify the underlying grammatical structure,

and semantic parsing which derives meaning.

Syntactic parsing may include:

• Segmentation: Larger texts are divided into smaller, meaningful chunks. Segmentation usually occurs at the end of sentences at punctuation marks to help organize text for further analysis.

• Tokenization: Sentences are split into individual words, called tokens. In the English language, tokenization is a fairly straightforward task because words are usually broken up by spaces. In languages like Thai or Chinese, tokenization is much more complicated and relies heavily on an understanding of vocabulary and morphology to accurately tokenize language.

• Stemming: Words are reduced to their root form, or stem. For example breaking, breaks, or unbreakable are all reduced to break. Stemming helps to reduce the variations of word forms, but, depending on context, it may not lead to the most accurate stem. Look at these two examples that use stemming:

  “I’m going outside to rake leaves.”

  Stem = leave

  “He always leaves the key in the lock.”

  Stem = leave

• Lemmatization: Similar to stemming, lemmatization reduces words to their root, but takes the part of speech into account to arrive at a much more valid root word, or lemma. Here are the same two examples using lemmatization:

  “I’m going outside to rake leaves.”

  Lemma = leaf

  “He always leaves the key in the lock.”

  Lemma = leave

• Part of speech tagging: Assigns grammatical labels or tags to each word based on its part of speech, such as a noun, adjective, verb, and so on. Part of speech tagging is an important function in NLP because it helps computers understand the syntax of a sentence.

• Named entity recognition (NER): Uses algorithms to identify and classify named entities–like people, dates, places, organizations, and so on–in text to help with tasks like answering questions and information extraction.

Sentiment analysis: Involves determining whether a piece of text

Intent analysis: Intent helps us understand what someone wants or means based on what they say or write.

Context (discourse) analysis: Natural language relies heavily on context. The interpretation of a statement might change based on the situation, the details provided, and any shared understanding that exists between the people communicating.

AI from A to Z: the GenAI Glossary:

AI Capabilities in CRM (8%)

1. Salesforce Einstein

   1. AI Assistants

      1. Voice input

      2. Natural lang understanding

      3. Voice output (natural lang generation)

      4. Intelligent interpretation

Einstein Out-of-the-box Apps

Einstein Platform → Discover, Predict, Recommend, Automate, Generate

Einstein Prediction Builder → a simple point-click wizard that allows you to make custom predictions on your non-encrypted Salesforce data.

Einstein Next Best Action → 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 GPT → yk.

Einstein Bots

Needs to have Service Cloud License.

Einstein Next Best Action

AI Is Only as Good as the Data

Sales Cloud Einstein.

Einstein Lead Scoring - This feature applies the power of AI to analyze your history of lead conversions and find the true patterns in those conversions

Einstein Opportunity Insights offers smart predictions and follow-ups about different opportunities precisely when they’re needed

Einstein Account Insights helps your sales team maintain their relationships with customers by keeping the team informed about key business developments that affect customers.

Einstein Activity Capture, activity-related insights (such as when a contact mentions a competitor or is leaving their company) are identified from recent emails and events related to accounts.

Einstein Email Insights → Actionable intelligence in reps’ inboxes helps identify which customers need attention.

Einstein Automated Contacts → Contact records are automatically added to Salesforce, so reps spend less time on data entry.

Einstein Activity Capture → Having email and event data automatically logged on Salesforce records increases reps’ productivity and visibility into potential customers.

Einstein Readiness Assessor is a tool that tells you whether you meet the requirements for specific Sales Cloud Einstein feature

Einstein Discovery

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

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:

• Regression 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.

Ethical Model Development with Einstein Discovery

Disparate Impact → it means that the data reflects discriminatory practices toward a particular group.

Proxy values are other attributes in your dataset that are correlated with sensitive

variables.

A model is a sophisticated custom equation based on a comprehensive statistical understanding of past outcomes that's used to predict future outcomes.

Einstein Predicition Service: a public REST API service that lets you programmatically interact with Einstein Discovery–powered models and predictions.

• Get predictions on your data.

• Get suggested actions to take to improve predicted outcomes.

• Manage prediction definitions and models that are deployed in Salesforce.

• Manage bulk scoring jobs.

• Manage model refresh jobs.

Ethical Considerations of AI (39%)

Audience Targeting, Collect and Respect Preferences, Frequency Capping,

[Behavioral Messaging Scenarios]

Abandon Cart is the canonical retailer use case for behavioral messaging., Abandon Browse,

Price Drop → If you let a consumer know when the product goes on sale, your brand message is meaningful, relevant, and in the consumers’ interest. Also consider adding a feedback mechanism that allows the consumer to tell you,

New Item in Favorite Category or New Promotional Campaign →

Post purchase → Give customers a reason to come back to you

Frequency Capping

Relationship Design

Human-centered design (HCD)

method of creative problem-solving that leads to a desired outcome.

• User experience (UX) design—The creation of meaningful and relevant experiences for users.

• Service design—The creation of consistent user or customer experiences across multiple interactions.

• UX design and service design describe what someone is designing: a product or a service. HCD is how something is designed. HCD methods have long been applied to UX and service design. It’s a flexible and powerful practice. What you do with it is all about where you focus.

• Relationship Design is the creation of experiences that foster ongoing engagement and strengthen connections between people, companies, and communities over time.

Service design focuses on:

• How easily someone can accomplish their goals during an interaction, whether online or in person.

• How consistent the brand experience is for customers across distinct interactions—whether they’re online, in an app, on different devices, or in person.

• How organizations are set up to deliver great experiences to customers.

Relationship Design → Engagement, Connection, Social Values.

Success is at the intersection of desirability, feasibility, and viability.

Mindsets of Relationship Design

• **Compassion mindset—**to lead with strengthening connection

• **Courage mindset—**to push ourselves to be vulnerable

• **Intention mindset—**to engage with clear purpose

• **Reciprocity mindset—**to exchange value in service of longevity

Cycle of Exclusion

• Why we make: The motivations of the problem solver.

• Who makes it: The problem solver.

• How we make: The methods and resources the problem solver uses.

• Who uses it: The assumptions the problem solver makes about the people who use the solution.

• What we make: The solution or product that the problem solver creates.

Risk of a Hero Complex

A common pitfall to avoid is wanting to be the hero who saves the day with a design. Yes, great design can have a meaningful impact and you may even be called a superhero for creating it. But making assumptions about what products or solutions a group might need will likely lead to trouble.

Ability Bias → the habit of using our own abilities as the baseline to solve problems.

Disability About Mismatches

<aside> 💡 Disability ≠ personal health condition → Disability = mismatched human interactions

</aside>

<aside> 💡 The persona spectrum is what we use in inclusive design to solve for one person and then extend to many

</aside>

• [x] Relatioship Design

• [x] Ethics by Design

• [x] Inclusive Design

• [x] Values-Driven Design

  Values → key character traits or codes of conduct that individuals and organizations aspire to or embody.

• [x] Design as a Social Practice

• [x] Accountability in Design

• [x] Relationship Design at Scale

Trusted AI Principles: Responsible, Accountable, Transparent, Empowering, Inclusive

Consumers don’t trust AI systems but they expect companies to use them responsibly

Data for AI (36%)

Data Literacy

Tableau Products

• Tableau Prep Builder

• Tableau Desktop

• Tableau Server

• Tableau Cloud

• Tableau Public

• Tableau Desktop Public Edition

• Tableau Mobile

Asking Why

The 5 Whys technique, developed by Toyota Motors founder Sakichi Toyoda, proposes asking why? of a problem that’s been identified, and then continuing to ask why for each answer or explanation given. Although the primary goal of the technique is to determine the root cause of a defect, we can use this technique to dig into the causes of any outcome.

Discover Variables and Field Types

Qualitative variables are variables that cannot be measured numerically, such as categories or characteristics. Categorize. Segment.

1. Nominal: cannot be ranked. (banana, grapes, and apples → these are nominal variables because there is no implied ranked order among them.

2. Ordinal → can be ranked. Ordinal = ordered.

Quantitative variables are variables that can be measured numerically, such as the number of items in a set. Can be aggregated.

1. Discrete Variables: individually separate & distinct. E.g, 3 children, 6 children → when you can count them separately.

2. Continuous Variables: Continuous means forming an unbroken whole, without interruption. Volume of water in the ocean.

Variance: how data points vary from the mean.

Standard deviation: is the measure of the distribution of statistical data

Here are the major characteristics of a normal distribution.

• They are symmetrical around the mean.

• The mean and median are equal.

• The area under the normal curve is equal to 1.0 (or 100%).

• They are denser in the center and less dense in the tails.

• They are defined by two parameters, the mean and the standard deviation

Inference is the process of drawing conclusions about a population based on a sample of the dat

Guidelines to Recognize Misleading Charts

• The bar charts should have a zero baseline.

• get to know source (who, what, where, when, why)

• Axes (how the data is displayed in chart)

  • Horizontal axis → x-axis

  • Vertical axis → y-axis

• A scatterplot shows the relationship between two quantitative variables. The data is plotted as Cartesian coordinates, marking how far over and how far up each data point is.

• A line chart connects a series of quantitative values, and is often used to show a time series (where the x-axis is time). Also known as a line graph or line plot.

• A histogram depicts a distribution of data and the frequency of values in a data set as connected bars. The width of the bars is tied to the values on the x-axis. Statisticians, scientists, and analysts refer to the widths of each bar as bins.

• A box and whisker plot shows the distribution of data using percentiles. Also known as a box plot.

• An interval is the distance between the values (tick marks) on a quantitative axis.

• use SCAM Checklist: Source, Chart, Axes on charts, Message

EU Privacy Law

General Data Protection Regulation (GDPR)

Data Requirements

Einstein Discovery requires a CRM Analytics dataset with at least 3 columns: one outcome variable plus two explanatory or predictor variables. Einstein Discovery supports datasets with up to 50 variable.

Time frames, how much data to get, time series,

Data Quality Dimensions:

1. Completeness.

2. Validity

3. Uniqueness

4. Timeliness’

5. Consistency

6. Accuracy

lightning/refresh module and RefreshView API provides a way to refresh component data in LWC. Updates the data for a specific hierarchy of components. Can refresh data for Salesforce Platform containers, LWC and Aura.

Querying DOM elements with refs

Refs can be used to access elements in shadow DOM and light DOM. Refs locate DOM elements without a selector and only query elements contained inside the template. Previously, we could only use the querySelector() to locate specific DOM elements.

1. Add the lwc:ref and assign it a value.

2. Call this using this.refs

<template>
    <div lwc:ref="myDiv"></div>
</template>

export default class extends LightningElement {
  renderedCallback() {
    console.log(this.refs.myDiv);
  }
}

Using this.refs for a nonexistent ref, returns undefined. If duplicates are available, this.refs references the last directive. For a component with more than one template this.refs refers to the most recently rendered template.

Overlays with the new modal component

To create a modal component, import LightningModal from lightning/modal in your JavaScript file. Then, create a component class that extends LightningModal.

/* c/myModal.js */
import { api } from 'lwc';
import LightningModal from 'lightning/modal';
export default class MyModal extends LightningModal {
    handleOkay() {
        this.close('okay');
   }
}

This component doesn’t use a lightning-modal tag. Instead, the modal’s HTML template uses helper lightning-modal-* components to make the modal’s header, footer, and body. The lightning-modal-body component is required, and the others are optional.

<!-- c/myModal.html -->
<template>
    <lightning-modal-header label="My Modal Heading"></lightning-modal-header>
 <lightning-modal-body>This is the modal’s contents.</lightning-modal-body>
  <lightning-modal-footer>
      <lightning-button label="OK" onclick={handleOkay}></lightning-button>
  </lightning-modal-footer>
</template>

To reset your security token, follow these steps:

1. Go to your Personal Settings.

2. Search for "Reset My Security Token" on Quick Find Box

   Refer to the picture below for visual guidance.

Option 2:

If the option 'Reset My Security Token' is not visible within 'Personal Settings,' you can try the following method.

It is possible that the reason for not seeing the option could be that you are currently accessing from an IP address that falls within the Trusted IP Ranges. In such cases, Salesforce hides that option. However, you can still access it through the provided link.

Simply add "/_ui/system/security/ResetApiTokenEdit" after the base URL (e.g., https://......force.com).

Step 1:

Step 2:

Alternatively, you can use the following URLs (depending on the type of organization):

For prod/dev orgs: https://login.salesforce.com/_ui/system/security/ResetApiTokenEdit

For sandboxes:

https://test.salesforce.com/_ui/system/security/ResetApiTokenEdit

Please note that the second method might not work if you do not have my-domain enabled or if there are other factors affecting the redirect.

There are various methods, including the Force.com IDE, Workbench, and Ant Migration Tool, to remove Apex classes from Production.

This guide will demonstrate how to use the Visual Studio Code IDE to remove a class or trigger from the production.

1. Create a project with manifest

First, create a project on Visual Studio Code, that is connected to your production environment. After authorizing successfully, retrieve the manifest.xml file from the source. Make sure that manifest.xml contains ApexClass as a member. After retrieving, you will see all the classes under the folder /force-app/classes.

Now, go to the specific class you want to delete.

2. Edit className.cls-meta.xml File

Let's say you want to get rid of the class badApexClass.

Go to your .cls-meta.xml file and change the status from Active to Deleted

<?xml version="1.0" encoding="UTF-8"?>
<ApexClass xmlns="http://soap.sforce.com/2006/04/metadata">
    <apiVersion>55.0</apiVersion>
    <status>Active</status>
</ApexClass>

<?xml version="1.0" encoding="UTF-8"?>
<ApexClass xmlns="http://soap.sforce.com/2006/04/metadata">
    <apiVersion>55.0</apiVersion>
    <status>Deleted</status>
</ApexClass>

3. Run the following command using the terminal

sfdx force:source:deploy -m ApexClass:badApexClass -l RunLocalTests -u ProjectAliasName

4. Check the Output

You should see something similar to this on your terminal:

=== Deployed Source

 FULL NAME                   TYPE      PROJECT PATH                                                            
 ─────────────────────────── ───────── ─────────────────────────────────────────────────────────────────────── 
 badApexClass        ApexClass force-app/main/default/classes/badApexClass.cls                 
 badApexClass        ApexClass force-app/main/default/classes/badApexClass.cls-meta.xml        

=== Test Results Summary

Passing: 10
Failing: 0
Total: 10
Time: 12789
Deploy Succeeded.

Congratulations! You've successfully deleted that Apex class with minimal effort.