GCP-ADP Data Management — Timed 50 Questions

Question 1

You are configuring a Dataplex Data Quality task. You need to ensure the task runs on a schedule and produces a report in BigQuery. Which resource is required to execute this task?

Accepted Answer

Dataproc Serverless. A Spark-based environment or a serverless execution environment managed by Dataplex is used to run these tasks.

Answer

Dataflow template

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BigQuery Data Transfer Service

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Cloud Scheduler

Question 2

A team requires access to a specific Dataplex Zone. You want to grant them the ability to view metadata and query the data without granting them full owner permissions. Which role should you assign at the Lake level?

Accepted Answer

roles/dataplex.dataViewer. roles/dataplex.dataViewer is the Dataplex role designed to grant read access to data within a lake/zone, including the ability to view metadata and query the underlying data, without granting administrative or owner permissions. It is the least-privilege role that satisfies the requirement of viewing metadata and querying data at the lake level.

Answer

roles/bigquery.dataViewer

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roles/dataplex.viewer

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roles/dataplex.admin

Question 3

You are migrating data into a Dataplex Lake. You need to ensure that the data is partitioned by date to improve query performance. Where do you define this partitioning?

Accepted Answer

At the Asset level. In Dataplex, partitioning is defined at the Asset level because an asset represents the actual data resource (such as a BigQuery table or Cloud Storage bucket) that is being cataloged and governed. Partitioning is a property of the underlying data storage, so it is configured where the asset is defined, not at the lake or zone level. This allows different assets within the same zone to have different partitioning schemes.

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At the Zone level

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At the Lake level

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In the IAM policy

Question 4

You are using Dataplex to manage data across multiple projects. You need to organize your BigQuery datasets and Cloud Storage buckets into a single logical entity for governance. Which Dataplex resource should you create?

Accepted Answer

Lake. The correct resource is a Lake. In Dataplex, a lake is a logical container that groups together zones and assets, providing a single logical entity for governance across multiple projects. It allows you to organize and manage data from various sources like BigQuery datasets and Cloud Storage buckets.

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Asset

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Taxonomy

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Data Zone

Question 5

You need to restrict data access to specific geographic regions for compliance. Which Dataplex feature supports this?

Accepted Answer

Regionalized Lake and Zone definition. Dataplex Lakes and Zones are created within a specific Google Cloud region, and the regionalized Lake/Zone definition enforces that data assets reside in that region, supporting data residency compliance. By defining lakes and zones in the required geography, you constrain where data is stored and processed.

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Location-based IAM policies

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Data residency tags

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VPC Service Controls

Question 6

You have multiple departments using the same Dataplex Lake. You need to ensure that each department can only see data belonging to their own projects. What should you configure?

Accepted Answer

Separate Zones within the Lake. Dataplex Zones are sub-divisions within a Lake that let you group assets by department, data classification, or processing stage (raw vs. curated). By creating separate Zones for each department and attaching the appropriate assets, you can apply IAM policies at the Zone level so each department only sees and accesses its own data. This provides the isolation required without creating separate Lakes.

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Data tags on the assets

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IAM roles on the bucket only

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Separate Lakes for each department

Question 7

You need to ensure that a Dataplex user cannot view the contents of files in a GCS bucket registered as an asset. What is the minimal configuration?

Accepted Answer

Remove the Storage Object Viewer role from the user. The minimal configuration is to remove the Storage Object Viewer role from the user. Dataplex relies on the underlying storage permissions to control access to data. If a user does not have Storage Object Viewer on the bucket, they cannot view the file contents, even if they have Dataplex permissions.

Answer

Remove the user from the Dataplex Lake Viewer role

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Create a Dataplex deny-policy

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Delete the asset from Dataplex

Question 8

When setting up a Dataplex Lake, you chose the 'Multi-region' location type. What is the implication of this choice?

Accepted Answer

It improves data availability and durability. Choosing a multi-region location for a Dataplex Lake replicates metadata and data across multiple geographic regions, which improves availability (survives a single-region outage) and durability (redundant copies reduce data loss risk). This is the same trade-off as multi-region BigQuery datasets or multi-region Cloud Storage buckets: higher resilience at the cost of slightly higher latency and cost.

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It restricts access to users in a single region

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It prevents the use of BigQuery datasets

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It reduces the cost of storing data

Question 9

You need to share a BigQuery dataset across multiple organizations. What is the recommended approach using Dataplex?

Accepted Answer

Use Dataplex to aggregate the datasets in one Lake. Dataplex Lakes can attach assets from multiple projects and even multiple organizations, allowing a single logical Lake to aggregate datasets that live in different projects. This provides centralized governance, discovery, and metadata management without copying or exposing data publicly. It is the recommended pattern for cross-organization data sharing because it preserves ownership and security boundaries while enabling unified access.

Answer

Grant public access to the dataset

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Copy all data to a single project

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Export the data to CSV files

Question 10

Which Dataplex component allows you to query data across different storage systems using a single SQL interface?

Accepted Answer

BigQuery. BigQuery is the correct answer because it provides a serverless, highly scalable data warehouse that can query data across multiple storage systems using standard SQL. Through features like BigQuery Omni and external tables, it can directly query data in Cloud Storage, Amazon S3, Azure Blob Storage, and other sources without data movement. Dataplex itself integrates with BigQuery to provide a unified analytics interface, but the actual query engine that enables cross-storage SQL is BigQuery. Thus, BigQuery is the Dataplex component that allows querying data across different storage systems with a single SQL interface.

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Dataproc

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Data Catalog

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Cloud Storage

Question 11

You are troubleshooting lineage issues. Which THREE of the following factors could prevent lineage from being captured? (Choose three)

Accepted Answer

The service account lacks 'lineage.events.create' permission. Option A is correct because lineage capture depends on the identity performing the operation holding the 'lineage.events.create' permission; without it, the lineage event cannot be written and lineage will not be recorded. Option C is correct because lineage is emitted through specific API endpoints, so if the pipeline calls the wrong or legacy endpoints, no lineage events are generated. Option D is correct because only supported services integrate with the lineage system; jobs running on an unsupported service will not produce lineage events. Option B is incorrect because lake administrator privileges are not required for lineage capture, only the appropriate lineage permission. Option E is incorrect because a multi-region bucket affects data location and access, not whether lineage events are emitted.

Answer

The user is not an administrator of the lake

Answer

The data files are in a multi-region bucket

Question 12

Which TWO of the following are true about the relationship between Data Catalog and Dataplex? (Choose two)

Accepted Answer

Dataplex uses Data Catalog to store metadata. Option A is correct because Dataplex relies on Data Catalog as its underlying metadata repository: when Dataplex discovers, profiles, or manages assets across lakes, zones, and assets, the resulting technical and business metadata is stored in and served through Data Catalog. Option B is correct because Data Catalog has been folded into Dataplex as a sub-feature, so the Data Catalog capabilities (search, tagging, metadata management) are now delivered as part of the Dataplex product rather than as a standalone service. Option C is incorrect because they are not billed as two separate products; Data Catalog functionality is included within Dataplex, so there is no separate Data Catalog charge. Option D is incorrect because they do not maintain separate IAM systems; access is governed through unified Google Cloud IAM roles and permissions that apply across Dataplex and its Data Catalog metadata. Option E is incorrect because they are not completely separate products — Data Catalog is integrated into and subsumed by Dataplex.

Answer

You must pay for them separately

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They have separate IAM systems

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They are completely separate products

Question 13

You are using Dataplex Data Quality tasks to validate a BigQuery table that receives daily incremental loads. The table is partitioned by ingestion date and contains a column named customer_id that must never be null. You need the task to run every night, scan only the most recently loaded partition, and write the results to a BigQuery table so that your operations team can query failures. What should you do?

Accepted Answer

Create a Dataplex Data Quality task, define a rule that checks customer_id for null values, configure a schedule, set the task's scope to the target BigQuery partition, and specify a BigQuery destination table for the results.. A Dataplex Data Quality task can enforce a non-null rule on customer_id, run on a schedule, limit its scan to a specific BigQuery partition, and export results to a BigQuery table. That directly satisfies the nightly cadence, the incremental partition scope, and the queryable result destination. The other approaches either add unnecessary services, use a non-queryable destination, or scan more data than required.

Answer

Create a Dataplex Data Quality task with a null rule on customer_id and configure it to write results to a Cloud Storage bucket as JSON files, then load those files into BigQuery with a scheduled query.

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Create a Dataplex Data Quality task with a null rule on customer_id, then use a Cloud Scheduler job to invoke the Dataplex API nightly and configure the task to publish results to a Pub/Sub topic.

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Create a Dataplex Data Quality task with a null rule on customer_id and set the task to run on a schedule, but leave the scope at the table level so that all partitions are scanned each night to ensure completeness.

Question 14

A data engineer needs to copy a 500 GB dataset from an on-premises data center to Cloud Storage. The data center has a reliable 1 Gbps internet connection, and the transfer must complete within two days. Which Google Cloud service should they use?

Accepted Answer

Storage Transfer Service. Storage Transfer Service is the managed online transfer solution for moving large datasets from on-premises or other cloud sources into Cloud Storage. It supports scheduling, retries, and incremental transfers, which suits a 500 GB move over a reliable 1 Gbps link within two days. Physical appliances and network-only options do not fit the requirements.

Answer

Transfer Appliance

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Cloud Interconnect with a Partner Interconnect attachment

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Cloud VPN with gsutil parallel composite uploads

Question 15

You are a data practitioner at a retail company. Your team has a BigQuery dataset that contains customer transaction records spanning 5 years. Analysts frequently run queries filtered by transaction_date, but most queries only look at the last 30 days. Query costs are high because BigQuery scans the entire table for each query. You want to reduce cost and improve performance with minimal changes to existing queries. What should you do?

Accepted Answer

Partition the table by transaction_date using day partitioning.. Partitioning the table by transaction_date allows BigQuery to scan only the partitions that match the query filter. Since most queries target the last 30 days, daily partitioning will prune the older partitions, significantly reducing bytes processed and cost. Existing queries that filter on transaction_date will automatically benefit without modification, making this the most efficient and least disruptive solution.

Answer

Create a clustered table on the transaction_date column.

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Create a view that filters the last 30 days and have analysts query the view.

Answer

Export the last 30 days of data to a new table and have analysts query that table.

Question 16

A financial services company stores transaction records in BigQuery. Compliance requires that individual rows be deleted upon customer request, and the table is partitioned by transaction date. Which approach satisfies the deletion requirement while minimizing cost?

Accepted Answer

Run a DELETE statement filtered by customer ID on the partitioned table. A DELETE statement filtered by customer ID on the partitioned table removes the specific rows required by compliance. Because the table is partitioned by transaction date, filtering on the partition column can reduce the data scanned and lower cost. Other options either rewrite the whole table, delete entire partitions, or merely restrict access without deleting data.

Answer

Set a partition expiration on the table to remove old partitions

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Recreate the table with a CREATE TABLE AS SELECT that excludes the customer rows

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Use a column-level security policy to hide the customer records

Question 17

A financial services company stores transaction records in BigQuery. A compliance officer must be able to see only the account_id and transaction_date columns for rows where region equals 'EMEA', while analysts on the same team must continue to see all columns and rows. You want to enforce this at query time without creating a separate copy of the data. What should you do?

Accepted Answer

Create a view that selects only the allowed columns and filters region to EMEA, then grant the compliance officer access to the view instead of the base table.. An authorized view is the right mechanism when one user needs a restricted projection and filter while others keep full access. The view runs with the owner's permissions, so the compliance officer reads only the allowed columns and rows without any direct grant on the base table, and no data copy is created.

Answer

Export the EMEA rows to a separate BigQuery dataset and grant the compliance officer access to that dataset only.

Answer

Use column-level security to grant access only to account_id and transaction_date, and use row-level security to filter region to EMEA.

Answer

Grant the compliance officer the BigQuery Data Viewer role on the dataset and rely on their query habits to select only the allowed columns.

Question 18

You are managing a BigQuery dataset that contains a table with a column of type STRING that stores JSON-formatted data. Analysts frequently need to extract specific fields from this JSON column in their queries. You want to provide them with a way to query these fields efficiently without repeatedly parsing the JSON. What should you do?

Accepted Answer

Add generated columns to the table that extract the JSON fields using JSON_VALUE, and then query those columns.. Generated columns let you define columns that are automatically computed from other columns using functions like JSON_VALUE. They are stored and maintained by BigQuery, so queries can access the extracted values without parsing the JSON string each time. This provides an efficient, native way to expose JSON fields as first-class columns.

Answer

Use a materialized view that precomputes the extracted JSON fields and refreshes automatically.

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Create a separate table that stores the parsed JSON fields and use a scheduled query to keep it in sync.

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Create a view that uses the JSON_EXTRACT_SCALAR function to parse the JSON column and expose the fields as separate columns.

Question 19

A company stores customer transaction data in BigQuery. The security team requires that analysts can query the data but cannot see individual credit card numbers. Analysts should still be able to run aggregations and joins. What should the data engineer implement?

Accepted Answer

Column-level security using policy tags in Data Catalog. Policy tags in Data Catalog provide column-level access control in BigQuery. By assigning a policy tag to the credit card column and granting only specific groups the fine-grained reader role, analysts can query the table and perform aggregations while the sensitive column remains inaccessible or masked. This directly enforces the security requirement.

Answer

BigQuery row-level security with a filter on transaction amount

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Cloud DLP inspection of the table with a scheduled scan

Answer

Authorized views that exclude the credit card column

Question 20

Your analytics team queries a BigQuery table that grows by about 40 GB per day. Most queries filter on a single `event_date` column, but the table is not partitioned, so each query scans the full table and costs are climbing. You want the lowest-effort change that reduces bytes scanned for these date-filtered queries without rewriting the ingestion pipeline. What should you do?

Accepted Answer

Create a new table partitioned by the `event_date` column and load new data into it, then point the team's queries at the new table.. Partitioning the table on the column that filters appear in most often allows BigQuery to skip partitions that cannot match the filter, which cuts bytes scanned and therefore cost. Because the team already filters on `event_date`, a table partitioned on that column gives immediate benefit, and loading new data into it avoids a disruptive full rewrite of historical ingestion.

Answer

Set a custom quota on the dataset that limits the number of bytes each user can query per day.

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Enable the BigQuery BI Engine reservation on the dataset and let the team continue querying the original table.

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Create a materialized view that selects all columns from the table, and have the team query the materialized view instead.