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How does AI which CRM help sales representatives better understand previous customer
interactions?
A. Creates, localizes, and translates product descriptions B. Triggers personalized service replies C. Provides call summaries
Answer: C
Explanation: “Providing call summaries is how AI with CRM helps sales representatives
better understand previous customer interactions. Call summaries are a feature that uses
natural language processing (NLP) to analyze voice conversations between sales
representatives and customers and generate summaries or transcripts of the calls. Call
summaries can help sales representatives better understand previous customer
interactions by providing key information, insights, or action items from the calls.”
Question # 2
Cloud Kicks wants to improve the quality of its AI model's predictions with the use of a
large amount of data.
Which data quality element should the company focus on?
A. Accuracy B. Location C. Volume
Answer: A
Explanation: To improve the quality of AI model predictions, Cloud Kicks should focus on
the accuracy of the data. Accurate data ensures that the insights and predictions generated
by AI models are reliable and valid. Data accuracy involves correcting errors, filling missing
values, and verifying data sources to enhance the quality of information fed into the AI
systems. Focusing on data accuracy helps in minimizing prediction errors and enhances
the decision-making process based on AI insights. For more details on the importance of
data quality in AI models, Salesforce provides extensive guidance in their documentation,
which can be found at Data Quality and AI.
Question # 3
What should an organization do to enforce consistency across accounts for newly entered
records?
A. Merge all duplicate accounts into a single record when duplicate entries are detected. B. Input the data exactly as it appears from the source, such as the company’s website orsocial media, C. Implement naming conventions or a predefined list of user-selectable values fororganization-wide records.
Answer: C
Explanation: To ensure consistency across accounts for newly entered records,
organizations should implement naming conventions or a predefined list of user-selectable
values. This approach standardizes data entry, reducing variations and errors. It also helps
in maintaining clean data which is essential for accurate reporting and analytics. Using
standardized naming conventions ensures that all users adhere to a consistent format,
making it easier to manage and analyze data across the organization. For more information
on best practices for data management in Salesforce, refer to Salesforce's documentation
on Data Management Best Practices.
Question # 4
What is a societal implication of excluding ethics in AI development?
A. Faster and cheaper development B. More innovation and creativity C. Harm to marginalized communities
Answer: C
Explanation: Excluding ethics in AI development can lead to societal implications such as
harm to marginalized communities. When ethical considerations are not integrated into AI
development, the resulting technologies may perpetuate or amplify biases, leading to unfair
treatment or discrimination against certain groups. This can reinforce existing social
inequalities and prevent these communities from benefiting equally from the advancements
in AI technology. Salesforce is committed to responsible AI development and emphasizes
the importance of ethical considerations in their development practices to prevent such
outcomes. Details on Salesforce’s approach to ethical AI and its importance can be found
at Salesforce Ethical AI.
Question # 5
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company's recommendations?
A. The response time for the product B. The accuracy of the product C. The diversity of the product
Answer: B
Explanation: Incomplete data quality negatively impacts the accuracy of product
recommendations made by Cloud Kicks. If data is missing or incomplete, the AI models
used for product recommendation may not have enough information to accurately predict
customer preferences and behavior. This leads to recommendations that may not align well
with customer needs, reducing customer satisfaction and potentially affecting sales.
Ensuring complete and accurate data is crucial for effective recommendation systems.
Salesforce discusses the impact of data quality on AI outcomes and strategies to enhance
data integrity in their documentation on AI and data management, which can be referenced
at Data Management for AI.
Question # 6
A sales manager wants to use AI to help sales representatives log their calls quicker and
more accurately.
Which functionality provides the best solution?
A. Call Summaries B. Sales Dialer C. Auto-Generated Sales Tasks
Answer: A
Explanation: The best functionality to help sales representatives log their calls quicker and
more accurately is the use of AI-generated Call Summaries. This feature leverages AI to
analyze voice data from sales calls and automatically generate concise summaries and
actionable insights, which are then logged into the CRM system. This not only speeds up
the process of recording call details but also enhances the accuracy of the data captured,
reducing the likelihood of human error and ensuring that important details are not missed.
Salesforce provides AI tools that integrate with telephony solutions to enable these
capabilities, enhancing the efficiency of sales operations. For more information on
Salesforce AI features like Einstein Call Coaching that support this functionality, visit
Salesforce Einstein Call Coaching.
Question # 7
Cloud Kicks prepares a dataset for an AI model and identifies some inconsistencies in the data.
What is the most appropriate action the company should take?
A. Adjust the Al model to account for the data inconsistencies. B. Increase the quantity of data being used for training the model C. Investigate the data inconsistencies and apply data quality techniques.
Answer: C
Explanation: When inconsistencies in data are identified, the most appropriate action is to
investigate these inconsistencies and apply data quality techniques. Adjusting the AI model
to accommodate poor quality data or simply increasing the quantity of data without
addressing the underlying issues does not solve the problem and can lead to less reliable
AI outputs. Proper data cleaning, normalization, and validation are necessary steps to
ensure that the data fed into an AI model is accurate and reliable, thus enhancing the
model’s performance. Salesforce provides guidelines on how to manage and improve data
quality, including practical steps for addressing data inconsistencies, detailed at Improving
Data Quality in Salesforce.
Question # 8
How does poor data quality affect predictive and generative AI models?
A. Creates inaccurate results B. Increases raw data volume C. Decreases storage efficiency
Answer: A
Explanation: Poor data quality significantly impacts the performance of predictive and
generative AI models by leading to inaccurate and unreliable results. Factors such as
incomplete data, incorrect data, or poorly formatted data can mislead AI models during the
learning phase, causing them to make incorrect assumptions, learn inappropriate patterns,
or generalize poorly to new data. This inaccuracy can be detrimental in applications where
precision is critical, such as in predictive analytics for sales forecasting or customer
behavior analysis. Salesforce emphasizes the importance of data quality for AI model
effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best
Practices.
Question # 9
Which AI tool is a web of connections, guided by weights and biases?
A. Neural networks B. Predictive Analytics C. Rules- based systems D. Mark this item for later review,
Answer: A
Explanation: Neural networks are a key AI tool designed as a web of interconnected
nodes, similar to the human brain's structure. Each connection, or synapse, in a neural
network is guided by weights and biases that are adjusted during the learning process.
These weights and biases determine the strength and influence of one node over another,
facilitating complex pattern recognition and decision-making processes. Neural networks
are extensively used in machine learning for tasks like image and speech recognition,
among others. For more on neural networks in the context of Salesforce AI, see the
Question # 10
Cloud Kicks wants to evaluate the quality of its sales data.
Which first step should they take for the data quality assessment?
A. Plan and align territories, B. Run a new report or dashboard. C. Identify business objectives.
Answer: C
Explanation: The first step Cloud Kicks should take for data quality assessment is
to identify business objectives. This is crucial because understanding how the company
uses customer data to support its business objectives will guide the assessment process1.
By identifying the business objectives, Cloud Kicks can determine what customer data is
required to support those objectives and how that data is being used. This foundational
step is essential before moving on to other aspects of data quality assessment, such as
running reports or planning territories. It aligns the data quality initiatives with the
company’s goals and ensures that the assessment is focused on areas that will drive
business value
Question # 11
How does AI assist in lead qualification?
A. Scores leads based on customer data B. Creates personalized SMS campaigns C. Automatically interacts with prospects
Answer: A
Explanation: AI assists in lead qualification primarily by scoring leads based on customer
data. This process, known as lead scoring, uses machine learning algorithms to evaluate
leads against a set of predefined criteria that reflect potential interest and sales readiness.
The scores assigned help sales teams prioritize their efforts toward leads most likely to
convert, thus improving efficiency and success rates in sales activities. Salesforce AI
enhances this process through features like Einstein Lead Scoring, which automatically
calculates scores based on both historical conversion data and behavioral data from
prospects. For further insights, Salesforce provides detailed documentation on lead scoring
with AI at Salesforce Einstein Lead Scoring.
Question # 12
Which best describes the difference between predictive AI and generative Al?
A. Predictive AT uses machine learning to classify or predict outputs from its input datawhereas generative Al does not use machine learning to generate its output. B. Predictive Al uses machine learning to classify or predict outputs from its input datawhereas generative Al uses machine learning to generate new and original output for 4given input C. Predictive Al and generative Al have the same capabilities but differ in the type of inputthey receive; predictive AT receives raw data whereas generative AT receives naturallanguage.
Answer: B
Explanation: Predictive AI and generative AI represent two different applications of
machine learning technologies. Predictive AI focuses on making predictions based on
historical data. It analyzes past data to forecast future outcomes, such as customer churn
or sales trends. On the other hand, generative AI is designed to generate new and original
outputs based on the learned data patterns. This includes tasks like creating new images,
text, or music that resemble the training data but do not duplicate it. Both types of AI use
machine learning, but their objectives and outputs are distinct. For detailed differences and
applications in a Salesforce context, Salesforce's guide on AI technologies is a helpful
resource, accessible at Salesforce AI Technologies.
Question # 13
Which action introduces bias in the training data used for AI algorithms?
A. Using a large dataset that is computationally expensive B. Using a dataset that represents diverse perspectives and populations C. Using a dataset that underrepresents perspectives and populations
Answer: C
Explanation: Introducing bias in training data for AI algorithms occurs when the dataset
used underrepresents certain perspectives and populations. This type of bias can skew AI
predictions, making the system less fair and accurate. For example, if a dataset
predominantly contains information from one demographic group, the AI's performance
may not generalize well to other groups, leading to biased or unfair outcomes. Salesforce
discusses the impact of biased training data and ways to mitigate this in their AI ethics
guidelines, which can be explored further in the Salesforce AI documentation on
Responsible Creation of AI.
Question # 14
What is the significance of explainability of trusted AI systems?
A. Increases the complexity of AI models B. Enhances the security and accuracy of AI models C. Describes how Al models make decisions
Answer: C
Explanation: The significance of the explainability of trusted AI systems is that it describes
how AI models make decisions. Explainability is crucial for building trust and accountability
in AI systems, ensuring that users and stakeholders understand the decision-making
processes and outcomes generated by AI. This is particularly important in scenarios where
AI decisions impact personal or financial status, such as in credit scoring or healthcare
diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI
practices, aiming to make AI systems more transparent and understandable. More details
about Salesforce’s approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.
Question # 15
Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.
Which type of records negatively impact data quality?
A. Structured B. Complete C. Duplicate
Answer: C
Explanation: Duplicate records negatively impact data quality by creating inconsistencies
and confusion in database management, leading to potential errors in customer
relationship management (CRM) systems like Salesforce. Duplicates can skew analytics
results, lead to inefficiencies in customer service, and result in redundant marketing efforts.
Salesforce offers various tools to identify and merge duplicate records, thereby maintaining
high data integrity. More about managing duplicate records in Salesforce and ensuring data
quality can be found in Salesforce’s documentation on duplicate management at
Salesforce Duplicate Management.
Question # 16
Cloud Kicks' latest email campaign is struggling to attract new customers.
How can AI increase the company's customer email engagement?
A. Create personalized emails B. Resend emails to inactive recipients C. Remove invalid email addresses
Answer: A
Explanation: AI can significantly increase customer email engagement by creating
personalized emails. Salesforce Einstein AI enhances email marketing campaigns by
analyzing customer data and past interactions to tailor the content, timing, and
recommendations within emails. This personalization leads to higher engagement rates as
emails resonate more closely with individual preferences and behaviors. Salesforce
Marketing Cloud provides tools to leverage AI for crafting personalized email campaigns,
ensuring that emails are relevant and appealing to recipients. For more insights into how AI
can be used to enhance email marketing, see the Salesforce Marketing Cloud page at
Salesforce Marketing Cloud Email Studio.
Question # 17
A business analyst (BA) is preparing a new use case for Al. They run a report to check for
null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?
A. Duplication B. Usage C. Completeness
Answer: C
Explanation: By checking for null values, a business analyst (BA) is verifying the data
quality component of completeness. Completeness refers to the absence of missing values
or gaps in the data, which is essential for the accuracy and reliability of reports and
analytics used in AI models. Null values can indicate incomplete data, which may adversely
affect the performance of AI applications by leading to incorrect predictions or insights.
Salesforce emphasizes the importance of data completeness for effective data analysis
and provides tools for data quality assessment and improvement. Details on handling data
completeness in Salesforce can be explored at Salesforce Help Data Management.
Question # 18
Cloud Kicks plans to use automated chat as its primary support channel.
Which Einstein feature should they use?
A. Discovery B. Bots C. Next Best Action
Answer: B
Explanation: For Cloud Kicks, using automated chat as the primary support channel, the
recommended Einstein feature is Bots. Einstein Bots are designed to automate customer
interactions on common issues through chat and messaging platforms. They can handle
routine requests, provide quick answers to frequently asked questions, and escalate more
complex issues to human agents. Using Einstein Bots helps improve customer service
efficiency and speed, leading to enhanced customer satisfaction. To learn more about
setting up and optimizing Einstein Bots for a business, you can visit the Salesforce
documentation on Einstein Bots at Salesforce Einstein Bots.
Question # 19
Which type of AI can enhance customer service agents' email responses by analyzing the
written content of previous emails?
A. Natural language processing B. Machine learning C. Deep learning
Answer: A
Explanation: Natural language processing (NLP) is the type of AI that can enhance
customer service agents' email responses by analyzing the written content of previous
emails. NLP technologies interpret and generate human language, allowing AI systems to
understand, respond to, and even anticipate customer needs based on email interactions.
This capability helps in crafting more relevant, accurate, and personalized email responses,
improving customer service quality. Salesforce utilizes NLP in its Einstein AI platform to
augment various customer service functions. More about Salesforce Einstein’s NLP
capabilities can be found on the Salesforce Einstein page at Salesforce Einstein NLP.
Question # 20
A sales manager is looking to enhance the quality of lead data in their CRM system.
Which process will most likely help the team accomplish this goal?
A. Redesign the lead conversion process, B. Review and update missing lead information. C. Prioritize active leads quarterly.
Answer: B
Explanation: To enhance the quality of lead data in their CRM system, the most effective
process is to review and update missing lead information. This process involves identifying
incomplete records and filling in missing details, which can significantly improve the
accuracy and usefulness of lead data. Accurate and complete lead information is crucial for
effective lead scoring, prioritization, and follow-up, enhancing overall sales performance.
Salesforce CRM offers data quality tools and features that assist in regularly reviewing and
maintaining the accuracy of lead data. Information on managing lead data quality in
Salesforce can be found at Salesforce Lead Management.
Question # 21
A developer has a large amount of data, but it is scattered across different systems and is
not standardized.
Which key data quality element should they focus on to ensure the effectiveness of the AI
models?
A. Performance B. Consistency C. Volume
Answer: B
Explanation: When data is scattered and not standardized, the key data quality element a
developer should focus on is consistency. Consistency refers to the uniformity and
standardization of data across different systems, which is crucial for integrating and
analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce
provides tools and practices for ensuring data consistency, such as data integration and
management solutions that help standardize and synchronize data across platforms. For
more information on Salesforce data management, refer to the Salesforce data
management tools at Salesforce Data Management.
Question # 22
What are predictive analytics, machine learning, natural language processing (NLP), and
computer vision?
A. Different types of data models used in Salesforce B. Different types of automation tools used in Salesforce C. Different types of AI that can be applied in Salesforce
Answer: C
Explanation: Predictive analytics, machine learning, natural language processing (NLP),
and computer vision are all types of artificial intelligence technologies that can be applied in
Salesforce to enhance various aspects of business operations and customer interactions.
Predictive analytics uses historical data to make predictions about future events. Machine
learning involves algorithms that can learn from and make decisions based on data. NLP is
concerned with the interactions between computers and humans using natural language,
and computer vision interprets and processes visual information from the world to make
sense of it in the way humans do. Salesforce harnesses these AI technologies, particularly
through its Einstein platform, to provide powerful tools that help businesses automate
tasks, make better decisions, and offer more personalized services. For more on how
Salesforce utilizes these AI technologies, you can explore the Einstein AI services
documentation at Salesforce Einstein.
Question # 23
What does the term "data completeness" refer to in the context of data quality?
A. The degree to which all required data points are present in the dataset B. The process of aggregating multiple datasets from various databases C. The ability to access data from multiple sources in real time
Answer: A
Explanation: Data completeness is a measure of data quality that assesses whether all
required data points are present in a dataset. It checks for missing values or gaps in data
necessary for accurate analysis and decision-making. In the context of Salesforce,
ensuring data completeness is crucial for the effectiveness of CRM operations, reporting,
and AI-driven applications like Salesforce Einstein, which rely on complete data to function
optimally. Salesforce provides various tools and features, such as data validation rules and
batch data import processes, that help maintain data completeness across its platform.
Detailed guidance on managing data quality in Salesforce can be found in the Salesforce
Help documentation on data management at Salesforce Help Data Management.
Question # 24
What is one technique to mitigate bias and ensure fairness in AI applications?
A. Ongoing auditing and monitoring of data that is used in AI applications B. Excluding data features from the Al application to benefit a population C. Using data that contains more examples of minority groups than majority groups
Answer: A
Explanation: A technique to mitigate bias and ensure fairness in AI applications is ongoing
auditing and monitoring of the data used in AI applications. Regular audits help identify and
address any biases that may exist in the data, ensuring that AI models function fairly and
without prejudice. Monitoring involves continuously checking the performance of AI
systems to safeguard against discriminatory outcomes. Salesforce emphasizes the
importance of ethical AI practices, including transparency and fairness, which can be
further explored through Salesforce’s AI ethics guidelines at Salesforce AI Ethics.
Question # 25
What are the potential consequences of an organization suffering from poor data quality?
A. Low employee morale, stock devaluation, and inability to attract top talent B. Revenue loss, poor customer service, and reputational damage C. Technical debt, monolithic system architecture, and slow ETL throughput
Answer: B
Explanation: The potential consequences of an organization suffering from poor data
quality include revenue loss, poor customer service, and reputational damage. Poor data
quality can lead to inaccurate analytics and decision-making, impacting customer
interactions, marketing strategies, and financial forecasting. These issues ultimately affect
customer satisfaction and could lead to financial losses and a damaged brand reputation.
Salesforce highlights the importance of maintaining high data quality for effective CRM and
AI applications, offering various tools and best practices to enhance data integrity. For
guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
Question # 26
What is the main focus of the Accountability principle in Salesforce's Trusted AI Principles?
A. Safeguarding fundamental human rights and protecting sensitive data B. Taking responsibility for one's actions toward customers, partners, and society C. Ensuring transparency In Al-driven recommendations and predictions
Answer: B
Explanation
“The main focus of the Accountability principle in Salesforce’s Trusted AI Principles is taking responsibility for one’s actions toward customers, partners, and society. Accountability means that AI systems should be designed and developed with respect for the impact and consequences of their actions on others.
Accountability also means that AI developers and users should be aware of and adhere to the ethical, legal, and regulatory standards and expectations of their industry and domain.”
Question # 27
Which features of Einstein enhance sales efficiency and effectiveness?
A. Opportunity List View, Lead List View, Account List view B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard C. Opportunity Scoring, Lead Scoring, Account Insights
Answer: C
Explanation
“Opportunity Scoring, Lead Scoring, Account Insights are features of Einstein that enhance sales efficiency and effectiveness. Opportunity Scoring and Lead Scoring use predictive models to assign scores to opportunities and leads based on their likelihood to close or convert. Account Insights use natural language processing (NLP) to provide relevant news and insights about accounts based on their industry, location, or events.”
Question # 28
What is a key benefit of effective interaction between humans and AI systems?
A. Leads to more informed and balanced decision making B. Alerts humans to the presence of biased data C. Reduces the need for human involvement
Answer: A
Explanation
“A key benefit of effective interaction between humans and AI systems is that it leads to more informed and balanced decision making. Effective interaction means that humans and AI systems can communicate and collaborate with each other in a clear, natural, and respectful way. Effective interaction can help leverage the strengths and complement the weaknesses of both humans and AI systems. Effective interaction can also help increase trust, confidence, and satisfaction in using AI systems.”
Question # 29
A business analyst (BA) wants to improve business by enhancing their sales processes and customer..
Which AI application should the BA use to meet their needs?
A. Sales data cleansing and customer support data governance B. Machine learning models and chatbot predictions C. Lead scoring, opportunity forecasting, and case classification
Answer: C
Explanation
“Lead scoring, opportunity forecasting, and case classification are AI applications that can help a business analyst improve their sales processes and customer support. Lead scoring can help prioritize leads based on their likelihood to convert, opportunity forecasting can help predict future sales or revenue based on historical data and trends, and case classification can help categorize and route cases based on their attributes.”
Question # 30
In the context of Salesforce’s Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?
A. Empower users to off all skill level to build AI application with clicks, not code. B. Empower users to contribute to the growing body of knowledge of leading AI research. C. Empower users to solve challenging technical problems using neural networks.
Answer: A
Explanation
“The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery.”
Salesforce Salesforce-AI-Associate Latest Result Cards
- Einstein Analytics Fundamentals (20%)
- Data Model and Management (15%)
- AI Model Building and Management (30%)
- Implementation and Consulting Best Practices (20%)
- Ethics and Privacy (15%)
Prerequisites
None, but experience with Salesforce recommended
Target Audience
Salesforce professionals, data analysts, AI enthusiasts
Exam Registration
Via Salesforce Certification website or testing centers
Cost
Approximately USD 200 (varies by region)
Retake Policy
Allowed after 14 days of first attempt, fees apply
Certification Validity
2 years, with maintenance exams required annually
Preparation Materials
Salesforce Trailhead modules, study guides, practice exams
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The Salesforce AI Associate exam is designed for individuals who want to demonstrate their knowledge and skills in using artificial intelligence (AI) technologies within the Salesforce ecosystem. It is suitable for developers, administrators, and consultants who work with AI features in Salesforce. Salesforce-AI-Associate Exam Details
What are the prerequisites for taking the Salesforce AI Associate exam?
There are no specific prerequisites for the Salesforce AI Associate exam. However, it is recommended to have a fundamental understanding of Salesforce and AI concepts. Preparing for the Salesforce AI Associate Exam
How can I register for the Salesforce AI Associate exam?
The Salesforce AI Associate exam consists of multiple-choice questions and is typically 60 minutes in duration. Salesforce AI Associate Exam Format
What topics are covered in the Salesforce AI Associate exam?
The exam covers a range of topics including AI fundamentals, Salesforce Einstein, predictive modeling, and more. You can find a detailed exam outline on the Salesforce certification website. Salesforce AI Associate Exam Guide
Can you provide some tips for preparing for the exam?
Sure! To prepare for the exam, it's important to study the official Salesforce documentation, complete Trailhead modules, and consider taking practice exams to assess your knowledge. Preparing for Salesforce Exams
Are there any official study materials or resources recommended for exam preparation?
Salesforce provides official study materials, including Trailhead modules, certification guides, and practice exams, which are highly recommended for preparation. Salesforce Trailhead
How much does it cost to take the Salesforce AI Associate exam?
The cost of the Salesforce AI Associate exam may vary. Check the Salesforce certification website for the most up-to-date pricing information. Salesforce Certification Pricing
What are Salesforce-AI-Associate Practice Tests, and why are they important?
Salesforce-AI-Associate Practice Tests are simulated exams that mimic the format and content of the actual Salesforce AI Associate certification exam. They are crucial for candidates to assess their readiness and identify areas of improvement before taking the real exam.
How can I contact your support team if I have questions or encounter issues with the practice tests?
You can contact our support team through the "Contact Us" page on our website or by emailing [email protected]. We are here to assist you.
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