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| Section | Objectives |
|---|---|
| Data Management | - Object and metadata management
|
| System Administration | - Instance administration
|
| Workspace Administration | - Workspace creation and configuration
|
| Review and Production | - Production management
|
| Search and Analytics | - Search functionality
|
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NEW QUESTION # 31
What operation does not work when trying to analyze documents that are password protected?
Answer: A
Explanation:
The correct answer is B. Save as PDF. Relativity's imaging documentation states that the Password Bank is used to decrypt certain password-protected files during processing and imaging workflows. It also explicitly states that Save as PDF does not integrate with the Password Bank and that if you select Save as PDF when imaging a password-protected native document, an error occurs .
This directly differentiates Save as PDF from the other options. Processing supports password decryption through the Password Bank. Mass Image and Imaging Sets also work with the Password Bank, allowing protected files to be imaged if the correct password is available in the bank. Because Save as PDF does not use that mechanism, it fails where the other imaging-related workflows can succeed.
From a case administration perspective, this is a practical operational detail. Password-protected content often needs special handling, and administrators need to know which tools integrate with Relativity's decryption workflow. The one that does not work for this purpose is Save as PDF .
NEW QUESTION # 32
With email thread visualization ETV, under what conditions does the red exclamation point indicate coding mismatch?
Answer: D
Explanation:
The correct answer is C. The email and other parent emails in the thread are coded differently. Relativity' s Email thread visualization documentation explains the meaning of the visual warning icons used in the thread display. The red exclamation point is associated with a coding mismatch condition in the thread. In practice, this means related parent emails in the thread have been coded inconsistently, which is exactly the kind of review inconsistency ETV is designed to expose.
This is different from missing attachments, duplicate spare comparison, or a generic "Needs Review" state.
ETV is focused on surfacing thread relationships, inclusiveness, missing emails, and coding consistency across the email conversation. From a Structured Analytics standpoint, the red exclamation point is a QC indicator that reviewers should inspect the thread because coding decisions across parent emails do not align.
Therefore, the correct answer is C .
NEW QUESTION # 33
When should you run OCR on a production?
Answer: B
NEW QUESTION # 34
A client sends you a data set they want processed that is comprised entirely of large Excel files. What is the best Excel Text Extraction Method to get the data to review as quickly as possible?
Answer: C
Explanation:
The correct answer is A. Relativity. Relativity's Processing throughput guidance states that internal performance testing has shown the Relativity text extraction method to be at least two times faster than the native method on Excel, Word, and PowerPoint file types, and also less error-prone than native extraction.
Since the question asks for the best method to get a large Excel-only data set to review as quickly as possible, the documented speed advantage makes Relativity the best answer.
The native-based methods are slower according to that same documentation, even though they may have other use cases. The processing profile documentation also shows that native/failover combinations are available configurations, but the performance guidance specifically favors the Relativity extraction method for speed on Excel files. "Extract and place inline" is not the documented Excel text extraction method choice for this question. Therefore, the fastest and most review-efficient choice for large Excel files is Relativity.
NEW QUESTION # 35
Your document collection contains a subset of invoices that are being identified as textual near duplicates yet are all from different days and are of differing amounts. What setting can you toggle to see if the results can be improved?
Answer: A
Explanation:
The correct answer is A. Ignore Numbers. Relativity's near duplicate documentation explains that when Ignore Numbers is set to true, the similarity calculation considers only tokens beginning with letters, which means numbers are excluded from the similarity percentage calculation. That is directly relevant for invoice populations, because many invoices share nearly identical wording while differing mainly by dates, invoice numbers, quantities, or amounts. In a set like the one described, toggling Ignore Numbers is the setting most likely to change how those documents are grouped and help you assess whether the current near duplicate results can be improved.
The other options do not fit this use case. MD5Hash Verifier is related to hash-based duplicate validation rather than textual near duplicate tuning. Relativity Compare is a review tool for examining differences after grouping, not a setting that changes how the grouping is calculated. Auto-recognize Dates is not the structured analytics tuning option documented for this scenario. Therefore, for invoice-heavy data where numerical differences are driving questionable textual near duplicate results, the correct setting to toggle is Ignore Numbers.
NEW QUESTION # 36
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