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Relativity Analytics Specialist EXAM QUESTIONS & ANSWERS 2024 ( A+ GRADED 100% VERIFIED) $10.99   Add to cart

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Relativity Analytics Specialist EXAM QUESTIONS & ANSWERS 2024 ( A+ GRADED 100% VERIFIED)

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  • Relativity Analytics Specialist
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  • Relativity Analytics Specialist

Relativity Analytics Specialist EXAM QUESTIONS & ANSWERS 2024 ( A+ GRADED 100% VERIFIED)

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  • June 18, 2024
  • 51
  • 2023/2024
  • Exam (elaborations)
  • Questions & answers
  • scenario one mil
  • Relativity Analytics Specialist
  • Relativity Analytics Specialist
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FLYINGHIGHER
FLYINGHIGHER
Relativity
Analytics
Specialist
Exam
Scenario:
One
million
mostly
English
electronic
documents
were
indexed
before
you
were
assigned
to
a
matter.
Another
25
scanned,
handwritten
documents
were
OCRed
in
an
effort
to
make
them
searchable.
Your
client
has
reported
some
problems
with
the
current
conceptual
index.
You
are
assigned
to
address
these
issues.

The
data
source
and
training
data
source
saved
searches
return
the
Extracted
Text
of
all
documents.

No
additional
filters
were
applied
to
the
index.

The
account
name,
WorldCom,
occurs
too
many
times
in
the
resulting
cluster
titles.
*****
What
should
you
do
to
resolve
the
problem
with
the
client
name,
WorldCom?
a.
Add
the
client
name
to
the
Concept
Stop
Word
list.
b.
Run
a
keyword
expansion
on
the
client
name.
c.
Enable
the
email
header
filter
on
the
conceptual
index.
d.
Re-run
the
cluster
set
including
all
documents.
a.
Add
the
client
name
to
the
Concept
Stop
Word
list. FLYINGHIGHER
Scenario:
One
million
mostly
English
electronic
documents
were
indexed
before
you
were
assigned
to
a
matter.
Another
25
scanned,
handwritten
documents
were
OCRed
in
an
effort
to
make
them
searchable.
Your
client
has
reported
some
problems
with
the
current
conceptual
index.
You
are
assigned
to
address
these
issues.

The
data
source
and
training
data
source
saved
searches
return
the
Extracted
Text
of
all
documents.

No
additional
filters
were
applied
to
the
index.

The
account
name,
WorldCom,
occurs
too
many
times
in
the
resulting
cluster
titles.
*****
What
acceptable
actions
can
you
take
to
properly
handle
the
OCRed
documents?
(Select
all
that
apply.)
a.
Exclude
the
OCRed
documents
and
handle
them
manually.
b.
Include
the
OCRed
documents
in
the
data
source
only.
c.
Include
the
OCRed
documents
in
the
data
source
and
training
data
source.
d.
Include
OCRed
documents
in
the
training
data
source
only.
a.
Exclude
the
OCRed
documents
and
handle
them
manually.
b.
Include
the
OCRed
documents
in
the
data
source
only. FLYINGHIGHER
Scenario:
One
million
mostly
English
electronic
documents
were
indexed
before
you
were
assigned
to
a
matter.
Another
25
scanned,
handwritten
documents
were
OCRed
in
an
effort
to
make
them
searchable.
Your
client
has
reported
some
problems
with
the
current
conceptual
index.
You
are
assigned
to
address
these
issues.

The
data
source
and
training
data
source
saved
searches
return
the
Extracted
Text
of
all
documents.

No
additional
filters
were
applied
to
the
index.

The
account
name,
WorldCom,
occurs
too
many
times
in
the
resulting
cluster
titles.
*****
Some
of
the
document
text
had
poor-quality
OCR,
and
has
now
been
overlaid.
How
can
you
update
the
index
to
reflect
this
new
text?
a.
Run
an
incremental
build
of
the
index
b.
Run
a
full
build
of
the
index.
c.
Add
new
repeated
content
filters.
d.
Cluster
the
documents.
b.
Run
a
full
build
of
the
index.
The
conceptual
index
processing
is
based
on
term
co-occurrence.
True/False. FLYINGHIGHER
True
-
the
language,
concepts,
and
relationships
are
defined
by
the
contents
of
your
documents.
Conceptual
Analytics
doesn't
use
word
order.
True/False.
True
-
conceptual
indexes
are
based
term
co-occurrence
A
Classification
index
learns
how
terms
are
related
to
categories
based
on
the
contents
of
your
documents.
True/False.
True
A
Conceptual
Index
supports
which
of
the
following
functions
within
Relativity?
(Select
All
that
Apply)
a.
Keyword
Expansion
b.
Concept
Search
c.
Active
Learning
d.
Categorization
a.
Keyword
Expansion
b.
Concept
Search
d.
Categorization
For
categorization,
the
concept
rank
indicates
documents
the
distance
between
the
example
document
and
the
resulting
document.
True
-
this
is
the
concept
rank
for
categorization
Only
documents
included
in
the
data
source
are
returned
when
you
run
clustering,
categorization,
etc.
True/False.

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