Relativity Analytics Cert - 10.3 Conceptual
Indexes Pt 1 UPDATED ACTUAL Exam
Questions and CORRECT Answers
Conceptual analytics helps reveal the facts of a case by doing the following: - CORRECT
ANSWER - -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) - CORRECT ANSWER - A mathematical approach to
indexing documents that Conceptual analytics uses
Conceptual Index - CORRECT ANSWER - 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
Classification Index - CORRECT ANSWER - 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 - CORRECT ANSWER - LSI is this, meaning that you can index any
language and it learns that language
Training Set - CORRECT ANSWER - LSI enables Relativity Analytics to learn the
language and, ultimately, the conceptuality of each document by first processing this set of data
Memory - CORRECT ANSWER - Analytics indexes are always stored in this when being
worked with, so response time is very fast.
, Concept Space - CORRECT ANSWER - When you create an Analytics index, Relativity
uses the training set of documents to build a mathematical model called this
Concept rank - CORRECT ANSWER - 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) - CORRECT ANSWER - This is used solely by
Active Learning. With this algorithm, you don't need to provide the Analytics index with any
training text
Hyperplane - CORRECT ANSWER - 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 - CORRECT ANSWER - 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 - CORRECT ANSWER - 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 - CORRECT ANSWER - These
two things can cause a warning message appears upon clicking Save to create a new Conceptual
index
Searchable set - CORRECT ANSWER - 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 - CORRECT ANSWER - The document set from which the Analytics engine
learns word relationships to create the concept index
Indexes Pt 1 UPDATED ACTUAL Exam
Questions and CORRECT Answers
Conceptual analytics helps reveal the facts of a case by doing the following: - CORRECT
ANSWER - -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) - CORRECT ANSWER - A mathematical approach to
indexing documents that Conceptual analytics uses
Conceptual Index - CORRECT ANSWER - 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
Classification Index - CORRECT ANSWER - 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 - CORRECT ANSWER - LSI is this, meaning that you can index any
language and it learns that language
Training Set - CORRECT ANSWER - LSI enables Relativity Analytics to learn the
language and, ultimately, the conceptuality of each document by first processing this set of data
Memory - CORRECT ANSWER - Analytics indexes are always stored in this when being
worked with, so response time is very fast.
, Concept Space - CORRECT ANSWER - When you create an Analytics index, Relativity
uses the training set of documents to build a mathematical model called this
Concept rank - CORRECT ANSWER - 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) - CORRECT ANSWER - This is used solely by
Active Learning. With this algorithm, you don't need to provide the Analytics index with any
training text
Hyperplane - CORRECT ANSWER - 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 - CORRECT ANSWER - 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 - CORRECT ANSWER - 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 - CORRECT ANSWER - These
two things can cause a warning message appears upon clicking Save to create a new Conceptual
index
Searchable set - CORRECT ANSWER - 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 - CORRECT ANSWER - The document set from which the Analytics engine
learns word relationships to create the concept index
