GCP-ADP Data Management Practice Test 5 — 15 Questions

Question 1

You need to organize your data governance strategy. Which TWO of the following are valid ways to use Dataplex Zones to improve your data organization? (Choose two)

Accepted Answer

Assign different IAM policies to different zones. Option A is correct because Dataplex zones are logical groupings of data assets within a lake, and IAM policies can be attached at the zone level, allowing you to grant different access permissions to different zones (e.g., restricting raw data while opening curated data to analysts). Option D is correct because zones are designed to organize data by its lifecycle or processing state, so creating raw, curated, and consumption zones is a canonical Dataplex pattern for data governance. Option B is not valid because query results are stored in BigQuery datasets or temporary tables, not in Dataplex zones, which are metadata/organization constructs rather than storage destinations. Option C is not valid because zones group data assets by governance or lifecycle characteristics, not by individual users; per-user access is handled through IAM, not by creating a zone per user. Option E is not valid because zones do not affect storage performance or speed; they are organizational and governance constructs, and storage speed is determined by the underlying service (e.g., Cloud Storage or BigQuery).

Answer

Use zones to store query results

Answer

Create separate zones for each user

Answer

Use zones to increase data storage speed

Question 2

A team is migrating to Dataplex. Which TWO of the following are prerequisites for adding a BigQuery dataset as an asset to a Lake? (Choose two)

Accepted Answer

The user needs BigQuery permissions on the dataset. Option A is correct because registering a BigQuery dataset as a Dataplex asset requires the user to hold BigQuery permissions on that dataset (for example, roles such as BigQuery Data Viewer/Owner or bigquery.datasets.get and bigquery.tables.get) so Dataplex can read the dataset's metadata and contents. Option B is correct because the user must also hold Dataplex asset-management permissions (such as dataplex.assets.create/update/delete, typically via roles/dataplex.admin or roles/dataplex.editor) to attach the dataset as an asset within the Lake. Option C is wrong because the dataset does not need to be public; access is governed by IAM permissions, and public exposure is neither required nor recommended. Option D is wrong because a BigQuery dataset can reside in a different project than the Lake, provided cross-project IAM permissions and the appropriate resource references are configured. Option E is wrong because an empty dataset is still a valid asset; Dataplex does not require the dataset to contain at least one table.

Answer

The dataset must be public

Answer

The dataset must exist in the same project as the Lake

Answer

The dataset must contain at least one table

Question 3

You are implementing a security strategy for your data lake. Which THREE of the following are supported ways to control access to data within a Dataplex Lake? (Choose three)

Accepted Answer

Grant Dataplex-specific roles to users. Option A is correct because Dataplex Lake access is governed through IAM by granting Dataplex-specific roles (such as roles/dataplex.viewer, roles/dataplex.editor, or roles/dataplex.admin) to users, groups, or service accounts, which controls who can view or manage the lake and its zones/assets. Option B is correct because Dataplex assets point to underlying storage such as Cloud Storage buckets or BigQuery datasets, and IAM roles applied directly to those underlying resources (for example roles/storage.objectViewer or roles/bigquery.dataViewer) are honored and provide an additional supported layer of data access control. Option C is correct because Dataplex integrates with BigQuery and Data Catalog Policy Tags (via the Dataplex/Data Catalog taxonomy and policy tag framework) to enforce column-level security, restricting access to specific columns of sensitive data. Option D is not correct because IP-based firewalls are a network-level control (e.g., VPC firewall rules) and are not a supported mechanism for controlling access to data within a Dataplex Lake. Option E is not correct because IAM roles are not assigned to individual files; permissions are granted at the resource hierarchy level (project, bucket, dataset, table, or Dataplex entity), not per file.

Answer

Use IP-based firewalls for all datasets

Answer

Assign roles to individual files

Question 4

When setting up a new Dataplex Lake, which TWO of the following configurations are required? (Choose two)

Accepted Answer

A unique name for the Lake. Option A is correct because every Dataplex Lake must be given a unique name within its project and location; this name is the resource identifier used in the lake's fully qualified path (projects/PROJECT/locations/LOCATION/lakes/LAKE) and is required at creation time. Option C is correct because a Dataplex Lake is a regional resource, so you must specify a geographic region (e.g., us-central1) when creating it; the region determines where the lake's metadata and associated assets are stored and cannot be omitted. Option B is not required because Dataplex uses Google-managed encryption by default, and a customer-managed encryption key (CMEK) is an optional configuration rather than a mandatory one. Option D is not required because a lake is a logical container for zones and assets; it does not need an external URL, as data is referenced through assets within zones rather than a lake-level URL. Option E is not required because access is governed by IAM roles and policies, not by supplying a list of all organization users during lake creation.

Answer

A default encryption key

Answer

An external URL for the data

Answer

A list of all users in the organization

Question 5

You need to ensure data compliance for PII. Which THREE of the following tools or features should you use in combination with Dataplex? (Choose three)

Accepted Answer

Cloud Audit Logs to monitor access. Cloud Audit Logs (A) is correct because it records Admin Activity, Data Access, and System Event logs for Dataplex and underlying services, providing the audit trail needed to demonstrate who accessed PII and when for compliance reporting. Cloud DLP API (B) is correct because it discovers, classifies, and de-identifies PII (e.g., via infoType detectors and masking, tokenization, or format-preserving encryption) across data scanned through Dataplex, which is essential for identifying and protecting sensitive data. Policy Tags (E) is correct because Dataplex integrates with BigQuery column-level security via Policy Tags in Data Catalog, enabling fine-grained, tag-based access control so only authorized principals can read PII columns. Cloud Build (C) is not a data encryption service—encryption is handled by Cloud KMS and default Google-managed encryption—so it does not address PII compliance. Cloud Scheduler (D) is only a cron-style job trigger and does not itself perform data deletion or enforce retention policies, so it is not the right tool for compliance here.

Answer

Cloud Build for data encryption

Answer

Cloud Scheduler for data deletion

Question 6

Which TWO of the following are true about Dataplex assets? (Choose two)

Accepted Answer

Assets inherit the location of the zone. Option B is correct because a Dataplex asset is always created inside a zone, and it automatically inherits that zone's location (region), so you cannot place an asset in a different location from its parent zone. Option C is correct because an asset is a child resource of exactly one zone in the Dataplex hierarchy (lake > zone > asset); it cannot be attached to multiple zones simultaneously. Option A is wrong because an asset must reference an underlying storage resource, such as a Cloud Storage bucket, BigQuery dataset, or BigLake table, so it cannot exist without one. Option D is wrong because assets can represent Cloud Storage buckets/objects, BigQuery datasets/tables, and BigLake tables, not only BigQuery tables. Option E is wrong because Dataplex assets are metadata representations that point to the existing physical data; they do not copy or duplicate the underlying data.

Answer

Assets can be created without a storage resource

Answer

Assets can only be BigQuery tables

Answer

Assets duplicate the physical data

Question 7

Which THREE of the following are common administrative tasks when managing a Dataplex Lake? (Choose three)

Accepted Answer

Adding and removing data assets. Option A (Adding and removing data assets) is correct because a Dataplex Lake organizes zones and assets, and administrators routinely attach or detach assets (such as Cloud Storage buckets or BigQuery datasets) to make data discoverable and governed. Option D (Configuring data quality task schedules) is correct because Dataplex supports Data Quality tasks that can be scheduled to run on a recurring basis, and managing those schedules is a standard administrative duty. Option E (Managing IAM permissions for lake users) is correct because Dataplex uses Cloud IAM roles and policies (for example, roles/dataplex.admin, roles/dataplex.viewer) to control who can view, edit, or manage lakes, zones, and assets. Option B (Manually editing individual data files) is not a Dataplex administrative task, since Dataplex is a metadata and governance layer and does not edit underlying file contents. Option C (Rebuilding the physical database indexes) is not applicable, because Dataplex is a fully managed service and does not expose physical index maintenance to administrators.

Answer

Manually editing individual data files

Answer

Rebuilding the physical database indexes

Question 8

Which TWO of the following are valid locations for a Dataplex Lake? (Choose two)

Accepted Answer

US (Multi-region). Options D and E are correct because a Dataplex Lake is a regional or multi-regional Google Cloud resource, so it must be created in a supported Google Cloud location such as the US multi-region (D) or a specific region like us-central1 (E). Dataplex lakes are scoped to a Google Cloud region or multi-region, which determines where the lake's metadata and associated assets are stored and managed. Option A is incorrect because an on-premises data center is not a Google Cloud location and cannot host a Dataplex Lake, even though on-premises data can be attached via Dataplex assets. Option B is incorrect because the root organization node is an IAM/resource hierarchy scope, not a geographic location for a lake. Option C is incorrect because a user's local laptop is not a Google Cloud region or multi-region and cannot serve as a Dataplex Lake location.

Answer

A private on-premises data center

Answer

The root organization node

Answer

The user's local laptop

Question 9

Which TWO of the following are true regarding Dataplex security? (Choose two)

Accepted Answer

IAM roles can be applied at the Lake, Zone, or Asset level. Option A is correct because Dataplex enforces fine-grained access control through IAM policies that can be attached at the Lake, Zone, or Asset level, allowing administrators to grant roles such as roles/dataplex.viewer or roles/dataplex.editor scoped to specific resources. Option E is correct because Dataplex supports VPC Service Controls perimeter integration, letting organizations restrict access to Dataplex resources and mitigate data exfiltration by adding dataplex.googleapis.com to service perimeters. Option B is not correct because Dataplex relies on Google Cloud IAM and federated identity rather than per-asset passwords. Option C is not correct because Dataplex respects and layers on top of existing Cloud Storage IAM permissions rather than ignoring them. Option D is not correct because Dataplex uses Google Cloud's existing authentication (Cloud Identity/Google Workspace, OAuth 2.0) instead of providing a separate authentication system.

Answer

Users must provide a password for each asset

Answer

Dataplex ignores existing storage permissions

Answer

Dataplex provides its own authentication system

Question 10

You need to migrate legacy data to a Dataplex Lake. Which THREE of the following should you consider for a successful migration? (Choose three)

Accepted Answer

Define a clear zone strategy. Option A is correct because a Dataplex Lake is organized into zones (raw, curated, and analytics) that map to data maturity and access patterns, so defining a clear zone strategy ensures legacy data lands in the appropriate zone with the right governance and lifecycle policies. Option B is correct because auditing existing IAM permissions on the source data is essential to map legacy access controls to Dataplex and Cloud IAM roles, preventing over-privileged or broken access after migration. Option E is correct because enabling Dataplex discovery automatically scans and inventories existing data assets (e.g., in Cloud Storage or BigQuery), giving you a catalog of what must be migrated and how it is structured. Option C is wrong because disabling data quality checks removes validation that migrated data is complete and accurate, which is a migration risk rather than a best practice. Option D is wrong because manually copying every file with scp is not a scalable or supported approach for migrating legacy data into a Dataplex Lake; managed transfer services or Dataplex/Cloud Storage tooling should be used instead.

Answer

Disable all data quality checks

Answer

Manually copy every file using scp

Question 11

Which TWO of the following tools allow you to interact with Dataplex? (Choose two)

Accepted Answer

Google Cloud CLI (gcloud). Option A, the Google Cloud CLI (gcloud), is correct because Dataplex provides gcloud commands (for example, gcloud dataplex lakes, zones, assets, and tasks) that let you create, manage, and query Dataplex resources from a terminal. Option D, the Google Cloud Console, is correct because Dataplex offers a graphical web UI in the Console for browsing lakes, zones, assets, and managing metadata, discovery, and data quality tasks. Option B, a local database client like DBeaver, is not correct because DBeaver connects to databases via JDBC/ODBC and does not natively manage Dataplex lakes, zones, or assets. Option C, a text editor for configuration files, is not correct because editing files alone does not provide an interface to Dataplex services; you would still need gcloud, the Console, or the API to apply them. Option E, a standard SSH client, is not correct because SSH provides remote shell access to hosts, not an interface to the Dataplex service.

Answer

A local database client like DBeaver

Answer

Text editor for configuration files

Answer

A standard SSH client

Question 12

A retail analytics team stores raw clickstream events in a Cloud Storage bucket. They want to query this data with BigQuery using standard SQL without loading it into BigQuery native storage, and they want to minimize cost by avoiding duplicate storage. Which Google Cloud feature should they use?

Accepted Answer

BigQuery external tables over Cloud Storage. BigQuery external tables allow querying data stored in Cloud Storage or other external sources without ingesting it into BigQuery managed storage. This satisfies both the standard SQL requirement and the cost goal of avoiding duplicate storage. Other options either copy data into BigQuery or serve entirely different purposes such as database replication or filesystem mounting.

Answer

Cloud Storage FUSE with a BigQuery remote function

Answer

BigQuery Data Transfer Service

Answer

Datastream with a BigQuery destination

Question 13

You are the data engineer for an online retailer. A Cloud Storage bucket named orders-raw contains daily CSV exports from an on-premises order management system. Each file is named with a date stamp, and new files arrive once per day. You need to load this data into BigQuery with minimal ongoing operational effort, and the load must automatically pick up each new file as it arrives. What should you do?

Accepted Answer

Create a BigQuery Data Transfer Service transfer for Cloud Storage, set the data source to the orders-raw bucket, and schedule it to run daily.. A managed BigQuery Data Transfer Service transfer for Cloud Storage is the lowest-effort way to load recurring files from a bucket into BigQuery. It supports Cloud Storage as a source, runs on a schedule you define, and automatically ingests new files that match the source path, so no custom code or cluster management is needed.

Answer

Create an external table over the orders-raw bucket and query the CSV files directly from BigQuery.

Answer

Create a Dataproc cluster with a scheduled PySpark job that reads the bucket and writes to BigQuery.

Answer

Write a Cloud Run job that reads each CSV from the bucket and calls the BigQuery load API, then trigger it from a Cloud Scheduler job every night.

Question 14

A marketing analyst needs to run a one-time SQL query that joins a BigQuery table containing campaign performance data with a small CSV file stored in a Cloud Storage bucket. The CSV is updated weekly by an external partner. The analyst wants the query to always reflect the latest CSV without loading it into BigQuery each week. What should the analyst do?

Accepted Answer

Create a BigQuery external table over the CSV file in Cloud Storage and join it with the campaign table in the query.. A BigQuery external table over the Cloud Storage CSV lets queries read the file directly, so the join always sees the latest partner data without any load or refresh job. This is the standard pattern when small reference files change frequently and you want to avoid maintaining a native copy.

Answer

Export the campaign table to Cloud Storage, then run the join in Cloud Shell using a local SQL engine.

Answer

Import the CSV into BigQuery as a native table and set up a scheduled query to re-import it weekly.

Answer

Use the bq load command to append the CSV into a partitioned table each week.

Question 15

You manage a BigQuery dataset that contains a table with millions of rows. Analysts frequently query only the most recent 30 days of data, but full-table scans are expensive. You need to reduce query costs and improve performance for these time-based queries with minimal changes to the table structure. What should you do?

Accepted Answer

Partition the table by a DATE or TIMESTAMP column using daily partitioning.. Partitioning a BigQuery table by a date or timestamp column enables partition pruning, so queries that filter on that column scan only the relevant partitions. This directly reduces the bytes processed and improves performance for time-based queries. Other options either do not reduce scanned data for arbitrary filters or add unnecessary complexity without addressing the root cause.

Answer

Create a separate table for each month and use a UNION ALL view to combine them.

Answer

Create a materialized view that selects the last 30 days of data and instruct analysts to query the view.

Answer

Enable the BigQuery BI Engine reservation on the dataset.