Free RCA Practice Test Questions and Answers (2026) | Cert Empire Practice Questions

In Relativity, a "Choice" is the object used to store the specific, predefined values for single-choice and multiple-choice fields. For example, a "Responsiveness" field would have choices like "Responsive" and "Not Responsive." These choices are created and managed as separate objects within a workspace. When a user codes a document, they are selecting one of these choice objects, and a link between the document and the selected choice is stored in the database. Layouts display these choices, but the choices themselves are the underlying objects.

B. Layouts are the web forms or user interfaces where coding fields and their available choices are displayed for user interaction.

C. Views are customizable lists that determine which fields (columns) are shown for a set of documents, not the storage for coding values.

D. Indexes (e.g., dtSearch, Analytics) are data structures that make document content and metadata searchable; they do not store the primary coding values.

Relativity Documentation, "Fields, choices, and layouts," Version: RelativityOne. This page states, "Choices are the predetermined values that are applied to single and multiple choice list fields."

Relativity Documentation, "Relativity objects," Version: RelativityOne. The documentation lists "Choice" as a distinct workspace object, defining it as a value for a single or multiple choice field.

The Word Index in the transcript viewer lists all non-noise words from a transcript and their frequency. When a user selects a word from this index, Relativity displays all occurrences of that word, including the page number, line number, and the speaker who said the word. Therefore, the Speaker field's data is a key component contained within and presented by the Word Index functionality to provide context for each term.

A. Associated facts are part of the transcript's Digest functionality, where users can create summaries or link key testimony to case facts, separate from the Word Index.

B. Exhibit numbers are managed and viewed in the Exhibits tab of the transcript viewer, not within the Word Index.

D. Count is a calculated frequency of a word's appearance in the transcript; it is a metric about the word, not a data field from the transcript itself like 'Speaker'.

1. Relativity Documentation. "Viewing transcripts". Relativity 10.3 Documentation. Accessed May 2024. (This section describes the viewer interface, including the Word Index tab, and explicitly mentions that it displays the speaker for each word occurrence.)

A dtSearch incremental build is an efficient method for updating an existing dtSearch index. Instead of re-indexing all documents in the searchable set (a "full build"), an incremental build only processes and adds documents that are new to the set since the last index build was completed. This process is significantly faster and uses fewer server resources, making it ideal for workspaces where new documents are frequently added and need to be made searchable quickly.

A. Rebuilding the entire index from scratch is known as a "Full Build," which is a different, more time-consuming operation.

C. Deleting the index is a separate administrative action that completely removes the index and its associated files.

D. Exporting an index is not a standard build operation; it refers to moving or copying index files, not updating them with new documents.

1. Relativity. (n.d.). Managing dtSearch indexes. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/dtSearch/ManagingdtSearchindexes.htm (Section: "Incremental Build - Click to build the index for only those documents that have been added to the search set since the last build.")

2. Relativity. (n.d.). dtSearch. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/dtSearch/dtSearch.htm (The overview explains the purpose of dtSearch indexes and the need to update them as new documents are loaded.)

DeNIST is a standard feature in Relativity Processing that filters out files based on the National Institute of Standards and Technology (NIST) list. The NIST list contains hash values for millions of known, common system and application files (e.g., .dll, .exe files from operating systems like Windows). By enabling the DeNIST option in a processing profile, Relativity compares the hash values of files in a data set against the NIST list and automatically removes exact matches from the discoverable document set, reducing irrelevant data for review.

A. OCR (Optical Character Recognition) is a feature that extracts text from images or non-textual PDFs, making them searchable; it does not remove files.

C. Clustering is an analytics feature that groups conceptually similar documents together; it does not filter files based on a predefined list.

D. Branding is a production feature used to apply Bates numbers and other endorsements to images; it is unrelated to file filtering during processing.

1. Relativity. (2024). Processing profiles. Relativity Documentation. Retrieved from the Relativity Community portal. The documentation for processing profile fields includes "DeNIST," which is described as an option to "remove NIST files from the processing set."

2. Relativity. (2024). Processing. Relativity Documentation. Retrieved from the Relativity Community portal. The overview of processing capabilities highlights DeNISTing as a key step in culling data by "filtering out ignorable files" to reduce data volumes before review.

A Search Terms Report is a tool within Relativity used to determine how many documents in a data set contain specific keywords or phrases. It provides a summary and detailed breakdown of hits for each term, including the number of documents containing the term and the total frequency of the term across the document set. This report is crucial for assessing the scope of potentially relevant documents based on keyword searches before executing a broader review.

A. User permissions are managed in the Workspace Details and Admin tabs, not through a Search Terms Report.

C. Server logs contain system-level information about the performance and errors of the Relativity environment, not keyword search results.

D. Backup status is a system administration detail concerning data protection, unrelated to document content searching.

1. Relativity. (n.d.). Search terms reports. Relativity Server 2023 Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Searchtermsreports/Searchtermsreports.htm. (See "Search terms reports" section, paragraph 1).

2. Relativity. (n.d.). Running a search terms report. Relativity Server 2023 Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Searchtermsreports/Runningasearchtermsreport.htm. (See "Results" section).

Boolean logic is a system of symbolic logic used in searching databases and search engines. The core operators are AND, OR, and NOT. The AND operator is used to narrow search results by requiring that all specified terms appear in the documents returned. For example, a search for "contract AND confidential" will only return documents containing both words. Both keyword search and dtSearch in Relativity support Boolean operators.

A. ADD: This is not a standard Boolean operator; it is sometimes used in mathematical contexts but not for logical search queries.

C. JOIN: This is a command used in SQL and other database languages to combine rows from two or more tables.

D. MERGE: This is a database operation for combining datasets; it is not a Boolean search operator.

- Relativity. (n.d.). Keyword search. RelativityOne Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Searching/Keywordsearch.htm. (This page details the syntax for keyword searches, explicitly listing AND, OR, and NOT as supported Boolean operators).

- Relativity. (n.d.). Searching with dtSearch. RelativityOne Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/dtSearch/SearchingwithdtSearch.htm. (This page confirms that dtSearch also supports the standard Boolean operators AND, OR, and NOT).

An Imaging Set in Relativity is a saved configuration that defines both the scope of documents to be imaged and the specific settings for how the images should be created. The scope is determined by a data source, typically a saved search. The settings are defined in an imaging profile, which specifies options like image format (e.g., TIFF, JPEG), resolution, stamping (e.g., Bates numbers), and other imaging parameters.

A. User permissions are managed through groups, users, and workspace security settings, not through an Imaging Set.

C. Search results are often the input or data source for an Imaging Set, but the set itself also includes all the configuration settings for the job.

D. Export formats are defined in different areas, such as during a production or a simple file export, not within an Imaging Set, which is focused on creating images.

- Relativity Documentation, "Imaging," section on "Imaging sets." The documentation states, "An imaging set is a saved group of documents that you define for an imaging job... you must also select an imaging profile to provide the settings for the job."

- Relativity Documentation, "Imaging," section on "Imaging profiles." This page details the various settings (e.g., format, resolution, stamping) that are configured in a profile, which is then applied to an Imaging Set.

In Relativity, a dynamic object is a custom object created by a user or administrator to store and organize data specific to a case's needs. Unlike system-defined objects like 'Document', dynamic objects are user-defined to track information that doesn't fit into standard fields, such as custodian details, interview notes, or key case issues. They are "dynamic" because they can be created, modified, and linked to other objects (including documents) to build relational databases within a workspace, tailoring the environment to a specific matter's requirements.

A. System-defined objects are built into the Relativity platform by default and are not user-created.

C. A temporary file is a transient file created by an operating system or application, not a persistent, structured object in Relativity.

D. A backup file is a copy of data used for restoration purposes and is unrelated to the application's object model.

1. Relativity. (n.d.). Objects. Relativity Documentation. Retrieved from the official Relativity documentation portal. In the "Objects" section, the documentation explicitly states, "Relativity refers to user-created objects as dynamic objects."

2. Relativity. (n.d.). Creating and editing objects. Relativity Documentation. Retrieved from the official Relativity documentation portal. This page details the process and purpose, stating, "You can create custom objects, called dynamic objects, to store information unique to a case."

An OPT file, or Opticon file, is a type of load file used in e-discovery to associate images with their corresponding records. It acts as a cross-reference file, mapping a unique document identifier (like a Bates number) to the file path of its associated image file (e.g., TIFF or JPG). It also specifies where documents begin and end (document breaks) and can contain volume information.

A. Text extraction is typically handled by DAT files (which contain metadata and extracted text) or separate text files referenced in a load file, not the OPT file itself.

C. Security settings are managed within the Relativity platform through users, groups, and permissions, and are not part of an OPT load file.

D. Index configuration refers to the settings for a dtSearch or Analytics index, which are configured within Relativity and are unrelated to an OPT file's function.

1. Relativity Documentation. "Load file formats for RDC". Relativity Server 2023 Documentation. Accessed May 2024. (This page details the specifications for various load file types, explicitly defining the Opticon (.opt) file as an "image cross-reference file" and outlining its required fields.)

Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. In Relativity, OCR is used to extract text from image files (like TIFFs or image-only PDFs) that do not have discoverable text. This process makes the content of these images searchable within the workspace, which is crucial for review and analysis. OCR can be run on a set of documents, and the resulting text is stored for indexing.

A. Encrypting files is a security measure to protect data from unauthorized access, which is a separate function from OCR.

C. Deleting duplicates is handled by the deduplication process during data processing, not by OCR.

D. Creating layouts is a user interface customization feature for organizing fields and objects for document review.

1. Relativity. (n.d.). OCR. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/OCR/OCR.htm (See the "OCR" section for a functional description).

2. Relativity. (n.d.). OCR profiles. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/OCR/OCRprofiles.htm (This page describes how to configure OCR settings, reinforcing its purpose).

Fuzzy searching is a feature of dtSearch that finds documents containing terms similar in spelling to the specified term. It is useful for identifying misspellings, typographical errors, or variations in character recognition (OCR) within a document set. The degree of similarity is controlled by the fuzziness level, which determines the number of character differences allowed between the search term and the matched term. For example, a search for "apple~1" could return documents containing "apply" or "axple".

A. Exact matches only: This describes a standard keyword search, which is the opposite of fuzzy searching's purpose of finding variations.

C. Delete duplicates: This function is handled by deduplication processes, which identify and remove exact or near-duplicate files, not by a search methodology.

D. Sort documents: Sorting is an organizational function based on metadata fields like date or author, and is unrelated to finding conceptually similar terms.

- Relativity - Searching. (n.d.). dtSearch operators. Relativity Documentation. Retrieved from the RelativityOne documentation portal. The section on the tilde (~) operator describes its use for fuzzy searching to find words that "differ only by a few characters."

- Relativity - Searching. (n.d.). Using dtSearch syntax options. Relativity Documentation. Retrieved from the RelativityOne documentation portal. This page details various search syntax, including fuzzy searching, contrasting it with exact match methods.

The mass edit operation in Relativity is a feature that allows a user with the appropriate permissions to modify the field values for multiple documents or objects at the same time. Instead of editing documents one by one, a user can select a group of documents from a view and apply the same change—such as updating a coding field, assigning a reviewer, or changing a responsiveness designation—to the entire selection in a single action. This significantly improves the efficiency and consistency of the document review and coding process.

B. Deleting users is an administrative task performed in the Users tab, not a document-level mass operation.

C. Creating backups is a system-level administrative function managed through the Relativity infrastructure, not a document review feature.

D. Running OCR is a separate mass operation used to extract text from images, distinct from editing field data.

1. Relativity. (n.d.). Mass operations. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Massoperations/Massoperations.htm (The "Edit" operation is listed and described as a primary mass operation for modifying multiple documents at once).

2. Relativity. (n.d.). Running a mass operation. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Massoperations/Runningamassoperation.htm (This page provides the steps for performing mass operations, including Edit).

Deduplication in the context of e-discovery processing is the process of comparing electronic files to identify and remove duplicate copies, retaining only a single instance. This is a critical data volume reduction technique. Relativity's processing engine calculates a hash value for each document and uses it to identify duplicates at a global or custodial level. By removing redundant documents, deduplication reduces storage costs, minimizes the number of documents that need to be reviewed, and increases the efficiency of the overall e-discovery workflow.

A. Deleting all files is data destruction, whereas deduplication is a selective process to remove only redundant files.

C. Encrypting data is a security measure to protect information, not a method for reducing data volume by removing duplicates.

D. Sorting documents is a way to organize a document list for review and is unrelated to removing duplicate files.

1. Relativity. (n.d.). Processing. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Processing/Processing.htm (The overview describes deduplication as a key feature of the processing workflow).

2. Relativity. (n.d.). Processing profiles. Relativity Documentation. Retrieved from https://help.relativity.com/RelativityOne/Content/Relativity/Processing/Processingprofiles.htm (See the "Deduplication method" field description for a technical explanation of how it works).

Clustering, a feature of Relativity Analytics, is an unsupervised machine learning process that organizes documents by conceptual similarity. It analyzes the text in each document to identify key concepts and then groups documents that share similar concepts. This process helps reviewers quickly understand the main themes within a large, unfamiliar document set without prior input, such as search terms or example documents. The resulting clusters can be used to prioritize review, identify irrelevant data, or perform quality control checks.

A. Sorting by size: This is a simple filtering or sorting operation based on a metadata field (file size) and does not involve conceptual analysis.

C. Encrypting data: Encryption is a security measure used to protect data from unauthorized access and is unrelated to document analysis or organization.

D. Deleting files: Deleting files is a data removal action. While the insights from clustering might inform which documents to delete, clustering itself does not perform deletion.

- Relativity - Analytics. (n.d.). Clustering. Relativity Documentation. Retrieved from the RelativityOne documentation portal. The documentation states, "Clustering is a tool that organizes a document universe into conceptually similar groups, which is useful for quickly assessing your case data."

- Relativity - Analytics. (n.d.). Analytics. Relativity Documentation. Retrieved from the RelativityOne documentation portal. The overview section describes how Analytics features, including clustering, use conceptual analysis to group documents.

To assess the specific textual differences within a near duplicate group, Relativity Compare is the appropriate tool. After running a textual near duplicate analysis, users can view the group members in the related items pane. From there, they can select two documents and use the compare function to see a side-by-side view with redlines highlighting the exact variations in text. This allows for a precise assessment of how the documents differ, which is the core of the question.

B. Image QC is a tool for reviewing the quality of document images (e.g., for clarity or missing pages), not for comparing extracted text.

C. Document View Overlay is not a standard Relativity tool. The term "overlay" refers to updating existing records with new data, not comparing text.

D. The Similar Documents View displays the list of documents in a near duplicate group but does not, by itself, show the differences between them.

1. Relativity Documentation. (n.d.). Running a structured analytics set. Retrieved from Relativity Community. Section: "Textual near duplicate identification results". (This section explicitly mentions using the compare function to see the differences between near duplicates).

2. Relativity Documentation. (n.d.). Viewers. Retrieved from Relativity Community. Section: "Relativity Compare".