Relativity Analytics Cert - 10.3
Conceptual Indexes Pt 1 Exam
Questions and Answers 100% Pass
Conceptual analytics helps reveal the facts of a case by doing the following: - ✔✔-
Giving users an overview of the document collection through clustering
-Helping users find similar documents with a right-click
-Allowing users to build example sets of key issues
-Running advanced keyword analysis
Latent Semantic Indexing (LSI) - ✔✔A mathematical approach to indexing documents
that Conceptual analytics uses
Conceptual Index - ✔✔Uses Latent Semantic Indexing (LSI) to discover concepts
between documents. This indexing process is based solely on term co-occurrence. The
language, concepts, and relationships are defined entirely by the contents of your
documents and learned by the index
100% Pass Guarantee Olivia West, All Rights Reserved © 2025 1
,Classification Index - ✔✔Uses coded examples to build a Support Vector Machine
(SVM) to predict a document's relevance. This index is used solely by the Active
Learning application
Language Agnostic - ✔✔LSI is this, meaning that you can index any language and it
learns that language
Training Set - ✔✔LSI enables Relativity Analytics to learn the language and, ultimately,
the conceptuality of each document by first processing this set of data
Memory - ✔✔Analytics indexes are always stored in this when being worked with, so
response time is very fast.
Concept Space - ✔✔When you create an Analytics index, Relativity uses the training set
of documents to build a mathematical model called this
Concept rank - ✔✔This is an indication of the conceptual distance between two items. It
may be referred to as a coherence score, rank, or threshold. In each scenario, the number
represents the same thing
Support Vector Machine learning (SVM) - ✔✔This is used solely by Active Learning.
With this algorithm, you don't need to provide the Analytics index with any training
text
100% Pass Guarantee Olivia West, All Rights Reserved © 2025 2
, Hyperplane - ✔✔After this is established, all documents without a coding decision are
pulled into the model and mapped on either side of it based on the model's current
understanding of the difference between relevant and not relevant.
Rank - ✔✔This measures the strength or confidence the model has in a document being
relevant or not relevant. It is measured on a scale from 100 to 0
Multiple Indexes - ✔✔If you want to limit search results to certain document groups or
have more than one language in the document set, this might give you better results
-No documents appear in the saved search
-Search contains fields that would cause the index to error - ✔✔These two things can
cause a warning message appears upon clicking Save to create a new Conceptual index
Searchable set - ✔✔This set should include all of the documents on which you want to
perform any Analytics function. Only documents included in this set are returned when
you run clustering, categorization, or any other Analytics feature
Training set - ✔✔The document set from which the Analytics engine learns word
relationships to create the concept index
Remove documents that errored during population = Yes - ✔✔This option removes
documents that received errors during population so that you don't have to manually
remove them from the population table in the database
100% Pass Guarantee Olivia West, All Rights Reserved © 2025 3
Conceptual Indexes Pt 1 Exam
Questions and Answers 100% Pass
Conceptual analytics helps reveal the facts of a case by doing the following: - ✔✔-
Giving users an overview of the document collection through clustering
-Helping users find similar documents with a right-click
-Allowing users to build example sets of key issues
-Running advanced keyword analysis
Latent Semantic Indexing (LSI) - ✔✔A mathematical approach to indexing documents
that Conceptual analytics uses
Conceptual Index - ✔✔Uses Latent Semantic Indexing (LSI) to discover concepts
between documents. This indexing process is based solely on term co-occurrence. The
language, concepts, and relationships are defined entirely by the contents of your
documents and learned by the index
100% Pass Guarantee Olivia West, All Rights Reserved © 2025 1
,Classification Index - ✔✔Uses coded examples to build a Support Vector Machine
(SVM) to predict a document's relevance. This index is used solely by the Active
Learning application
Language Agnostic - ✔✔LSI is this, meaning that you can index any language and it
learns that language
Training Set - ✔✔LSI enables Relativity Analytics to learn the language and, ultimately,
the conceptuality of each document by first processing this set of data
Memory - ✔✔Analytics indexes are always stored in this when being worked with, so
response time is very fast.
Concept Space - ✔✔When you create an Analytics index, Relativity uses the training set
of documents to build a mathematical model called this
Concept rank - ✔✔This is an indication of the conceptual distance between two items. It
may be referred to as a coherence score, rank, or threshold. In each scenario, the number
represents the same thing
Support Vector Machine learning (SVM) - ✔✔This is used solely by Active Learning.
With this algorithm, you don't need to provide the Analytics index with any training
text
100% Pass Guarantee Olivia West, All Rights Reserved © 2025 2
, Hyperplane - ✔✔After this is established, all documents without a coding decision are
pulled into the model and mapped on either side of it based on the model's current
understanding of the difference between relevant and not relevant.
Rank - ✔✔This measures the strength or confidence the model has in a document being
relevant or not relevant. It is measured on a scale from 100 to 0
Multiple Indexes - ✔✔If you want to limit search results to certain document groups or
have more than one language in the document set, this might give you better results
-No documents appear in the saved search
-Search contains fields that would cause the index to error - ✔✔These two things can
cause a warning message appears upon clicking Save to create a new Conceptual index
Searchable set - ✔✔This set should include all of the documents on which you want to
perform any Analytics function. Only documents included in this set are returned when
you run clustering, categorization, or any other Analytics feature
Training set - ✔✔The document set from which the Analytics engine learns word
relationships to create the concept index
Remove documents that errored during population = Yes - ✔✔This option removes
documents that received errors during population so that you don't have to manually
remove them from the population table in the database
100% Pass Guarantee Olivia West, All Rights Reserved © 2025 3
