AI Associate Certification Prep Exam
Questions With Correct Answers
Natural language understanding (NLU)
refers to systems that handle communication between people and machines.
Natural language processing (NLP)
is distinct from NLU and describes a machine's ability to understand what humans mean when they
speak as they naturally would to another human.
Named entity recognition (NER)
labels sequences of words and picks out the important things like names, dates, and times. NER
involves 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. Just like
our human mind connects the dots to give us insights, deep learning uses algorithms to sift through
data, draw conclusions, and enhance performance.
Einstein Bots
automatically resolve top customer issues, collect qualified customer information, and seamlessly
hand off the customers to agents, meaning increased case deflection in the contact center and
reduced handle times for agents.
Einstein Agent
drives agent productivity across the contact center. Through intelligent case routing, automatic
triaging, and case field prediction, Einstein Agent significantly accelerates issue resolution and
enhances efficiency.
Einstein Discovery
helps managers take action with predictive service KPIs. By serving up real-time analysis of drivers
that impact KPIs, like churn or CSAT and suggested recommendations and explanations, managers are
empowered to make more strategic decisions for their business.
Einstein Vision for Field Service
automates image classification to resolve issues faster on-site. Just by taking a picture of the object,
Einstein Vision can instantly identify the part, ensuring accuracy for the technician and boosting first-
time fix rates.
Einstein Language
brings the power of deep learning to developers. They can use pretrained models to classify text by
the sentiment as either positive, neutral, or negative, and then be able to classify the underlying
intent in a body of text. Put it all together, and you have the ability to process language across
unstructured data in any app.
Bots
A bot is an application that automates standard tasks.
, machine learning (ML)
which is the process of using large amounts of data to train a model to make predictions, instead of
handcrafting an algorithm.
Change management
A set of processes designed to help organizations and individuals successfully implement new
initiatives, like launching new AI solutions. The goal of change management is to ensure the success of
new initiatives by creating a comprehensive plan to launch and track the impact of new technologies.
Data Lake Object
In Data Cloud, data comes in through a data stream and resides in a data lake object (DLO). A DLO is
the storage container for data ingested into Data Cloud
Data Transform
A data transform lets you access data in one or more DLOs and transform it to create your own set of
data.
Streaming Data Transform
A data transform that runs continually
Batch Data Transform
runs on a scheduled basis. Batch data transforms offer more functionality than streaming data
transforms, which are based on a SQL statement. Batch data transforms offer a rich visual editor.
Use a batch data transform when you need to do complex data transformations or when you need
the data updated on a scheduled basis. In a batch data transform, you can join, aggregate, and
append data. You can also use formulas and filters.
Data Cloud
A data platform that combines the power of the Salesforce Platform with the scalability of an
infrastructure that allows for processing data in near-real-time. It can also handle tremendous scale—
and we mean tremendous. Data Cloud can process trillions of records, petabytes of data, and
thousands of requests per second per customer.
Data Model Object, also called DMOs (Groups of Data)
An object in the data model created by ingested data streams and insights. DMOs can be standard or
can be custom, based on your business needs. A DMO stores data such as leads, product info,
customer info, and so on.
Subject Area (A Business Goal)
The Customer 360 Data Model groups subject areas or data models according to business goals. For
marketers, that goal is to market or promote your product or service to your customers. Data model
subject areas might include a grouping of unique identifiers called party, or could be engagement
data, sales orders, or product information.
Attributes (Data About Your Contacts)
Questions With Correct Answers
Natural language understanding (NLU)
refers to systems that handle communication between people and machines.
Natural language processing (NLP)
is distinct from NLU and describes a machine's ability to understand what humans mean when they
speak as they naturally would to another human.
Named entity recognition (NER)
labels sequences of words and picks out the important things like names, dates, and times. NER
involves 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. Just like
our human mind connects the dots to give us insights, deep learning uses algorithms to sift through
data, draw conclusions, and enhance performance.
Einstein Bots
automatically resolve top customer issues, collect qualified customer information, and seamlessly
hand off the customers to agents, meaning increased case deflection in the contact center and
reduced handle times for agents.
Einstein Agent
drives agent productivity across the contact center. Through intelligent case routing, automatic
triaging, and case field prediction, Einstein Agent significantly accelerates issue resolution and
enhances efficiency.
Einstein Discovery
helps managers take action with predictive service KPIs. By serving up real-time analysis of drivers
that impact KPIs, like churn or CSAT and suggested recommendations and explanations, managers are
empowered to make more strategic decisions for their business.
Einstein Vision for Field Service
automates image classification to resolve issues faster on-site. Just by taking a picture of the object,
Einstein Vision can instantly identify the part, ensuring accuracy for the technician and boosting first-
time fix rates.
Einstein Language
brings the power of deep learning to developers. They can use pretrained models to classify text by
the sentiment as either positive, neutral, or negative, and then be able to classify the underlying
intent in a body of text. Put it all together, and you have the ability to process language across
unstructured data in any app.
Bots
A bot is an application that automates standard tasks.
, machine learning (ML)
which is the process of using large amounts of data to train a model to make predictions, instead of
handcrafting an algorithm.
Change management
A set of processes designed to help organizations and individuals successfully implement new
initiatives, like launching new AI solutions. The goal of change management is to ensure the success of
new initiatives by creating a comprehensive plan to launch and track the impact of new technologies.
Data Lake Object
In Data Cloud, data comes in through a data stream and resides in a data lake object (DLO). A DLO is
the storage container for data ingested into Data Cloud
Data Transform
A data transform lets you access data in one or more DLOs and transform it to create your own set of
data.
Streaming Data Transform
A data transform that runs continually
Batch Data Transform
runs on a scheduled basis. Batch data transforms offer more functionality than streaming data
transforms, which are based on a SQL statement. Batch data transforms offer a rich visual editor.
Use a batch data transform when you need to do complex data transformations or when you need
the data updated on a scheduled basis. In a batch data transform, you can join, aggregate, and
append data. You can also use formulas and filters.
Data Cloud
A data platform that combines the power of the Salesforce Platform with the scalability of an
infrastructure that allows for processing data in near-real-time. It can also handle tremendous scale—
and we mean tremendous. Data Cloud can process trillions of records, petabytes of data, and
thousands of requests per second per customer.
Data Model Object, also called DMOs (Groups of Data)
An object in the data model created by ingested data streams and insights. DMOs can be standard or
can be custom, based on your business needs. A DMO stores data such as leads, product info,
customer info, and so on.
Subject Area (A Business Goal)
The Customer 360 Data Model groups subject areas or data models according to business goals. For
marketers, that goal is to market or promote your product or service to your customers. Data model
subject areas might include a grouping of unique identifiers called party, or could be engagement
data, sales orders, or product information.
Attributes (Data About Your Contacts)
