Comprehensive and Detailed Explanation:
The requirements are role-based access, masking specific columns (PII) for one role, and avoiding data duplication.
Option B is the correct answer because it directly addresses all requirements using Google Cloud's modern data governance tools. You use Data Catalog (governed by Dataplex) to create a taxonomy and policy tags (e.
g., a "PII" tag). You apply this tag to the sensitive columns in your BigQuery table. Then, using IAM, you can grant the HR Analyst role access to the table but apply a data masking policy to the "PII" tag for that role.
When an analyst queries the table, BigQuery dynamically masks the tagged columns for them at query time.
The HR Admin role is granted full access without the masking policy. This provides fine-grained security without creating any copies of the data.
Option A is a valid but older approach. An authorized view could work, but it requires manually maintaining the view's SQL definition. Policy tags are more scalable and manageable, especially as the number of tables and policies grows.
Options C and D are incorrect because they both involve creating a new, separate dataset with masked data.
This explicitly violates the requirement to "reduce data duplication."
Reference (Google Cloud Documentation Concepts):
This is the primary use case for column-level security in BigQuery. The official documentation shows how to
"Restrict access with BigQuery column-level security" by creating a taxonomy and policy tags in Data Catalog. You then grant IAM permissions on these policy tags (roles/datacatalog.categoryFineGrainedReader) to different groups of users. For users who should see masked data, you do not grant them this role on the sensitive tags, and a data masking rule (e.g., nullification, hashing) can be applied. This entire governance framework can be managed centrally via Dataplex.

Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles

The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node.
You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node.
Reference: https://cloud.google.com/ml-engine/docs/training-overview#job_configuration_parameters

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
Reference: https://cloud.google.com/dataproc/docs/concepts
/iam#iam_roles_and_cloud_dataproc_operations_summary