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CCNA Data Management Questions

66 questions · Data Management topic · All types, answers revealed

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1
Multi-Selectmedium

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

Select 2 answers
A.IAM roles can be applied at the Lake, Zone, or Asset level
B.Users must provide a password for each asset
C.Dataplex ignores existing storage permissions
D.Dataplex provides its own authentication system
E.Dataplex integrates with VPC Service Controls
AnswersA, E

Dataplex enforces access through IAM policy bindings at three granularities: lake, zone and asset, letting permissions be scoped precisely to the resource hierarchy rather than the whole project. This satisfies the stem's requirement for true security statements, since inherited roles cascade downward from lake to zone to asset.

Why this answer

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.

Exam trap

GCP-ADP often tests the integration of Dataplex with IAM and VPC Service Controls, and candidates may mistakenly think Dataplex has its own authentication or ignores storage permissions.

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2
MCQhard

You need to control access to specific columns in a BigQuery table using Dataplex-integrated policy tags. What must you create before you can apply a policy tag to a column?

A.Data Zone
B.IAM Condition
C.BigQuery Dataset permission
D.Policy Tag Taxonomy
AnswerD

A policy tag taxonomy is the prerequisite container that must exist before any policy tag can be created or applied. Dataplex requires tags to be organised within a taxonomy, so the column-level access control cannot be enforced until that hierarchy is defined in the same region as the BigQuery data.

Why this answer

Policy tags in BigQuery are organized within a Policy Tag Taxonomy, which is a hierarchical container created in Data Catalog (now Dataplex Universal Catalog). Before any policy tag can be applied to a column, you must first create a taxonomy to hold the tags, then create the individual policy tags inside it, and finally grant the Fine-Grained Reader role to users. The taxonomy is the mandatory parent resource — you cannot create a standalone policy tag without it.

Exam trap

GCP-ADP often tests the hierarchy of policy tag creation — candidates confuse the taxonomy (the container) with the policy tag itself and pick an unrelated IAM or dataset construct.

How to eliminate wrong answers

Option A is wrong because a Data Zone is a Dataplex construct used to organize data assets within a lake for discovery and management, not a prerequisite for column-level security. Option B is wrong because IAM Conditions are used to add attribute-based constraints (like time or resource name) to IAM bindings — they are not the container for policy tags. Option C is wrong because BigQuery Dataset permissions control access at the dataset/table level, not column-level; policy tags operate independently of dataset ACLs.

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3
MCQmedium

A fintech firm streams transaction events into a Pub/Sub topic. The analytics team needs near-real-time aggregates in BigQuery and must also retain the raw events for seven years in Cloud Storage for compliance. They want a managed service with minimal code. Which pipeline should you recommend?

A.Use a Cloud Scheduler job to pull from Pub/Sub every minute and load batches into both BigQuery and Cloud Storage.
B.Configure a Pub/Sub subscription with a BigQuery subscription, and enable message retention on the topic for seven years.
C.Run a Cloud Run service that reads from Pub/Sub, writes aggregates to BigQuery, and archives to Cloud Storage.
D.Use Dataflow with a streaming pipeline that writes to BigQuery and also writes raw events to Cloud Storage.
AnswerD

Dataflow is a fully managed, autoscaling service for stream and batch processing. A single streaming pipeline can read from Pub/Sub, compute windowed aggregates into BigQuery, and simultaneously write raw events to Cloud Storage for long-term retention, all with Apache Beam code and managed runners. This satisfies near-real-time aggregation, seven-year retention, and minimal operational overhead in one pipeline.

Why this answer

Dataflow is the managed stream-processing service that can read Pub/Sub, compute windowed aggregates into BigQuery, and write the raw stream to Cloud Storage in the same pipeline. That covers near-real-time analytics and multi-year retention with minimal custom operational code. The alternatives either cannot retain messages long enough, require hand-built processing, or rely on a scheduler that does not process streams.

Exam trap

The trap here is assuming Pub/Sub message retention can satisfy long-term compliance archiving, when retention is measured in days and archiving to Cloud Storage is required.

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4
MCQeasy

A retail company stores point-of-sale events in Pub/Sub and wants them available for SQL analysis in BigQuery with minimal operational overhead and near-real-time latency. The team has no servers to manage and wants the simplest supported path. Which approach should they use?

A.Schedule a Cloud Scheduler job that runs a Dataflow batch pipeline every fifteen minutes to read from Pub/Sub and write to BigQuery.
B.Use the BigQuery subscription type on the Pub/Sub topic to write messages directly into a BigQuery table.
C.Export Pub/Sub messages to a Cloud Storage bucket and load them into BigQuery with a scheduled load job.
D.Write a Cloud Run service that subscribes to the topic and calls the BigQuery Storage Write API for each message.
AnswerB

A BigQuery subscription is a native Pub/Sub subscription type that streams messages straight into a BigQuery table without any code to write or servers to manage. It supports dead-letter topics and handles batching and retries automatically. This is the simplest supported path for getting Pub/Sub events into BigQuery with near-real-time latency.

Why this answer

Pub/Sub provides a native BigQuery subscription type that streams messages directly into a table, eliminating the need to run or maintain any subscriber infrastructure. It is the lowest-overhead way to achieve near-real-time SQL analysis, and it handles delivery concerns such as retries and dead-lettering, which custom pipelines would have to implement manually.

Exam trap

The trap here is reaching for Dataflow or custom code out of habit, when Pub/Sub already offers a managed BigQuery subscription that needs no compute to operate.

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5
MCQeasy

A retail company ingests point-of-sale events into BigQuery. Analysts frequently filter by store_id and by transaction date, and the table grows by several hundred gigabytes per month. You want to reduce the bytes scanned by these filtered queries with the least effort. What should you do?

A.Create a materialized view that pre-aggregates sales by store and day.
B.Set a maximum bytes billed limit on the dataset to force smaller scans.
C.Enable the BigQuery BI Engine reservation for the dataset.
D.Partition the table by transaction date and cluster it by store_id.
AnswerD

Partitioning by transaction date lets BigQuery prune whole partitions when queries filter on the date, and clustering by store_id sorts data within each partition so filters on store_id scan fewer blocks. Together they cut bytes scanned for the exact access pattern described, with minimal ongoing effort. This is the standard, low-maintenance optimization for large time-series tables with a common secondary filter column.

Why this answer

Filtering by date and store on a large, growing event table is the classic case for partitioning on the date column and clustering on the secondary filter column. Partition pruning removes irrelevant date ranges, and clustering limits blocks scanned within each partition, lowering cost and improving speed. The remaining choices either accelerate only narrow query types, add a caching layer without changing storage, or merely block expensive queries.

Exam trap

The trap here is reaching for a caching or guardrail feature to cut cost when the durable fix is physically organizing the table with partitioning and clustering.

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6
MCQmedium

Your organization stores raw event data in a Cloud Storage bucket that receives new files throughout the day. Analysts want to query this data with SQL in BigQuery without loading it into native BigQuery storage first. The data is in Parquet format and partitioned by date in the folder structure. What should you create?

A.An external table in BigQuery that points to the Cloud Storage bucket and uses the Parquet format and hive-partitioned layout.
B.A Cloud SQL for PostgreSQL instance with a foreign data wrapper pointing at the Cloud Storage bucket.
C.A BigQuery table created with a load job that reads the Parquet files from Cloud Storage.
D.A Dataproc cluster running Spark that reads the Parquet files and exposes them through a Hive metastore.
AnswerA

BigQuery external tables can query data directly in Cloud Storage without loading it into BigQuery storage. For Parquet files laid out in a hive-partitioned folder structure, the external table definition can use the hive partitioning option so partition pruning works. This lets analysts run SQL against the raw files immediately, matching the requirement.

Why this answer

A BigQuery external table lets analysts query Parquet files in Cloud Storage directly with SQL, and hive partitioning enables partition pruning on the date folders. This avoids loading data into BigQuery storage and keeps new files immediately queryable. Load jobs copy data, Dataproc is heavier and not SQL-in-BigQuery, and Cloud SQL does not natively read Parquet from Cloud Storage.

Exam trap

The trap here is assuming that querying Cloud Storage data in BigQuery always requires a load job, when external tables are the supported way to query data in place.

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7
Multi-Selectmedium

You are designing a data lake in Cloud Storage and need to ensure that data is organized for efficient querying by BigQuery and other analytics services. You plan to use a Hive-style partitioned directory structure. Which two practices should you follow? (Choose two.)

Select 2 answers
A.Use a consistent key=value naming convention for partition directories, such as dt=2024-01-01.
B.Store all files in a single directory and rely on file name prefixes for filtering.
C.Use a columnar file format such as Parquet or ORC for the data files.
D.Store each partition as a single large file to minimize the number of files.
E.Ensure that the partition key is the last element in the directory path, such as year=2024/month=01/day=01.
AnswersA, C

A consistent key=value naming convention, like dt=2024-01-01, allows BigQuery and other tools to automatically infer partitioning when you create an external table. This improves query performance by enabling partition pruning. It also makes the data layout self-describing and easier to manage across tools.

Why this answer

Using a consistent key=value directory naming convention and columnar file formats like Parquet or ORC are best practices for a Hive-partitioned data lake. The naming convention enables automatic partition inference and pruning, while columnar formats reduce I/O and improve query efficiency. Together, they optimize both cost and performance for analytics services like BigQuery.

Exam trap

The trap here is assuming that any file organization or file format works equally well, when only consistent key=value partitioning and columnar formats deliver the performance and cost benefits expected in a data lake.

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8
MCQmedium

Your company stores sensitive customer records in a Cloud Storage bucket. A compliance officer requires that every object in the bucket be automatically classified and that any object containing credit card numbers be flagged for review. You want a managed Google Cloud service that samples and inspects the data without writing custom code. What should you use?

A.Cloud Audit Logs with a log-based metric that counts objects uploaded to the bucket.
B.Cloud Data Loss Prevention (DLP) with a discovery scan configured on the Cloud Storage bucket.
C.Cloud Storage Object Lifecycle Management rules that move objects to Coldline storage after 30 days.
D.VPC Service Controls with an access policy that restricts the bucket to approved projects.
AnswerB

Sensitive Data Protection, also known as Cloud DLP, provides managed discovery scans that sample and inspect data in Cloud Storage. You can configure infoType detectors such as CREDIT_CARD_NUMBER and generate findings that flag matching objects for review. This meets the requirement without custom code and is the Google Cloud service designed for automated sensitive-data classification.

Why this answer

Cloud DLP discovery scans are the managed way to sample and inspect data in Cloud Storage and produce findings when infoTypes such as credit card numbers are detected. This provides automatic classification and flagging without custom code. Audit logs, VPC Service Controls, and lifecycle rules operate on metadata, access, or storage class rather than content, so they cannot detect sensitive data inside objects.

Exam trap

The trap here is confusing metadata-level controls, such as audit logs or lifecycle rules, with content inspection, which only a data classification service like Cloud DLP performs.

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9
MCQmedium

You need to track lineage for a BigQuery table populated by a Dataflow pipeline. How does Dataplex help?

A.Lineage is not supported in Dataplex
B.You must use a third-party tool
C.Dataplex captures it automatically for supported services
D.You must manually enter the lineage in the Data Catalog UI
AnswerC

Dataplex automatically captures lineage for supported services, including BigQuery and Dataflow, without manual instrumentation. This satisfies the stem's requirement to track lineage for a BigQuery table populated by a Dataflow pipeline, recording source-to-target relationships across those services.

Why this answer

Dataplex automatically captures lineage for supported services, including BigQuery and Dataflow. When a Dataflow pipeline writes to a BigQuery table, Dataplex records the lineage information, showing the source and target. This automatic capture eliminates manual effort.

Exam trap

GCP-ADP often tests whether candidates know that Dataplex provides automatic lineage for supported services, rather than requiring manual entry or third-party tools.

How to eliminate wrong answers

Option A is wrong because lineage is supported in Dataplex for various services. Option B is wrong because Dataplex provides built-in lineage, so a third-party tool is not required. Option D is wrong because lineage is captured automatically, not manually entered.

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10
MCQmedium

You want to enforce that all data in a specific Dataplex Zone must be encrypted with a Customer-Managed Encryption Key (CMEK). How do you enforce this?

A.There is no way to enforce CMEK in Dataplex
B.Use a Dataflow job to re-encrypt
C.Configure CMEK at the Lake/Zone creation level
D.Apply it via an IAM policy
AnswerC

CMEK applied at Lake or Zone creation propagates that key to every asset within the zone, so encryption is enforced by the container itself rather than per-asset. This satisfies the stem's requirement that all data in the Dataplex Zone use a Customer-Managed Encryption Key.

Why this answer

In Dataplex, CMEK enforcement is configured at the Lake and Zone creation level. When you create a lake or zone, you can specify a CMEK (a Cloud KMS key) that will be used to encrypt all data assets within that lake or zone. This ensures that any data stored in the zone is encrypted with the customer-managed key, satisfying compliance requirements.

Exam trap

The trap is thinking CMEK can be enforced via IAM or applied after the fact — candidates overlook that CMEK is a creation-time property of the lake/zone resource.

How to eliminate wrong answers

Option A is wrong because Dataplex does support CMEK at the lake/zone level. Option B is wrong because re-encrypting with a Dataflow job is a manual, post-hoc workaround, not an enforcement mechanism. Option D is wrong because IAM policies control access, not encryption key selection — CMEK is a resource-level configuration, not an IAM binding.

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11
MCQeasy

Which role is required to manage Dataplex assets within a Zone?

A.roles/storage.admin
B.roles/bigquery.admin
C.roles/dataplex.admin
D.roles/viewer
AnswerC

Granting roles/dataplex.admin provides full control over Dataplex assets, including creating, updating and deleting zones, assets and lakes. This satisfies the stem's requirement to manage assets within a Zone, since the role encompasses all Dataplex resource operations rather than only metadata viewing or data-level access.

Why this answer

Managing Dataplex assets within a Zone requires the roles/dataplex.admin role, which grants full control over lakes, zones, assets, and their metadata. This role includes permissions to create, update, and delete assets and to manage IAM policies on Dataplex resources.

Exam trap

GCP-ADP often tests role-to-resource mapping — the trap is choosing a source-system admin role (storage.admin, bigquery.admin) when the question asks about Dataplex-native asset management.

How to eliminate wrong answers

Option A is wrong because roles/storage.admin only governs Cloud Storage buckets and objects, not Dataplex asset metadata or zone configuration. Option B is wrong because roles/bigquery.admin manages BigQuery datasets, tables, and jobs but does not confer Dataplex asset management rights. Option D is wrong because roles/viewer is read-only and cannot create, modify, or delete Dataplex assets.

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12
MCQeasy

A healthcare analytics team stores patient encounter records in Cloud Storage as CSV files and needs to run SQL over them in BigQuery without loading or duplicating the data. The files must remain the system of record, and the team wants to avoid storage charges for a second copy. Which BigQuery feature should they use?

A.Load the CSV files into a native BigQuery table with a scheduled load job every hour.
B.Create a BigLake table over the CSV files and query it with the BigQuery Storage API.
C.Mount the Cloud Storage bucket as a BigQuery dataset using a federated dataset connection.
D.Create an external table over the Cloud Storage bucket and query the CSV files directly.
AnswerD

External tables let BigQuery query data that lives in Cloud Storage without loading it, so the CSV files stay the system of record and no second copy is billed. This matches the requirement to avoid duplication and keeps the data in its original location while still allowing SQL access from BigQuery.

Why this answer

An external table points BigQuery at the CSV objects in Cloud Storage and queries them in place, satisfying the requirement to keep the files as the system of record and avoid a duplicate billed copy. Loading duplicates data, while BigLake and non-existent dataset mounting add complexity or do not address the stated need.

Exam trap

The trap here is reaching for BigLake because it sounds more modern, when the scenario specifies no governance or multi-engine requirement that would justify it over a standard external table.

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13
Multi-Selectmedium

You are designing a data governance strategy for a BigQuery environment. You need to automatically apply policy tags to columns that contain sensitive data so that only authorized users can query those columns. Which two actions should you take? (Choose two.)

Select 2 answers
A.Configure a Cloud DLP job to redact sensitive values from the table on a nightly schedule.
B.Enable VPC Service Controls on the project that hosts the BigQuery dataset.
C.Set the default table expiration on the dataset to 30 days to limit exposure of sensitive data.
D.Grant the Fine-Grained Reader role on the policy tag to the group that should be able to query the sensitive columns.
E.Create a taxonomy and policy tags in Data Catalog, then attach the policy tags to the sensitive columns in the BigQuery table schema.
AnswersD, E

The Fine-Grained Reader role on a policy tag grants the ability to read data in columns associated with that tag. Users who lack this role on the tag cannot query the protected columns even if they have access to the table. This is the mechanism that restricts access to sensitive columns, so it is required.

Why this answer

Column-level security in BigQuery is implemented with policy tags from a Data Catalog taxonomy. You attach the policy tags to the sensitive columns and then grant the Fine-Grained Reader role on each tag to the groups that should see the data. Together these actions automatically restrict access at the column level.

The other choices address network perimeters, data redaction, or retention rather than column-level authorization.

Exam trap

The trap here is assuming that broader security controls such as VPC Service Controls or DLP redaction provide column-level access control, when only policy tags combined with Fine-Grained Reader grants do that.

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14
MCQhard

You want to perform data quality checks on a BigQuery table that is updated daily. How should you schedule the Dataplex task?

A.Configure a CRON schedule on the task
B.Manually trigger via the UI
C.Wait for discovery to trigger it
D.Use Cloud Run to trigger the task
AnswerA

Dataplex data quality tasks support CRON scheduling, letting the scan run automatically each day after the table's daily update. This satisfies the recurring, unattended execution the scenario demands, without external orchestrators such as Cloud Composer or manual triggering.

Why this answer

Dataplex tasks can be scheduled using a CRON expression to run automatically at specified intervals. Since the BigQuery table is updated daily, a CRON schedule ensures the data quality checks run after each update without manual intervention. This is the standard method for recurring data quality checks in Dataplex.

Exam trap

GCP-ADP often tests the misconception that discovery or other services can trigger tasks, but the correct answer is always the native scheduling mechanism (CRON) for Dataplex tasks.

How to eliminate wrong answers

Option B is wrong because manual triggering via the UI is not automated and would require human action daily, which is impractical. Option C is wrong because discovery does not trigger data quality tasks; discovery is for metadata harvesting, not for executing tasks. Option D is wrong because using Cloud Run to trigger the task adds unnecessary complexity and is not the native scheduling mechanism for Dataplex tasks.

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15
MCQeasy

Which of the following is NOT a benefit of using Dataplex?

A.High-frequency OLTP transaction support
B.Automated data discovery
C.Improved data compliance
D.Unified data management
AnswerA

Dataplex is a metadata and governance layer for analytics — discovery, lineage, quality and lake zones. It does not serve high-frequency OLTP workloads, which need a transactional database with row-level locking and low-latency commits. Hence it is not a Dataplex benefit.

Why this answer

Dataplex is a data governance and metadata management service designed for analytics workloads — it provides automated discovery, cataloging, data quality, and unified management across data lakes. It is not an OLTP (online transaction processing) engine; it does not support high-frequency transactional workloads like row-level inserts/updates at scale. Therefore, high-frequency OLTP transaction support is NOT a benefit of Dataplex.

Exam trap

GCP-ADP often tests whether candidates confuse Dataplex's governance/analytics focus with transactional database capabilities, so any option mentioning OLTP or high-frequency transactions is a red flag.

How to eliminate wrong answers

Option B is wrong because automated data discovery is a core Dataplex capability — the Discovery process scans data and populates the Dataplex Catalog with schemas and metadata. Option C is wrong because improved data compliance is a key benefit, achieved through data lineage, tagging, and policy enforcement across zones and lakes. Option D is wrong because unified data management across silos (Cloud Storage, BigQuery, etc.) is precisely what Dataplex provides via lakes, zones, and assets.

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16
Multi-Selecteasy

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

Select 2 answers
A.Google Cloud CLI (gcloud)
B.A local database client like DBeaver
C.Text editor for configuration files
D.Google Cloud Console
E.A standard SSH client
AnswersA, D

The gcloud CLI exposes Dataplex through the `gcloud dataplex` command group, letting you create, manage and query lakes, zones, assets and tasks from a terminal. This satisfies the stem's requirement for a tool that interacts with Dataplex, and it suits scripted or automated administration.

Why this answer

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.

Exam trap

GCP-ADP often tests the misconception that Dataplex is a database queryable by standard SQL clients, when it is an API/CLI/Console-managed metadata and governance service.

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17
MCQmedium

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?

A.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.
B.Create an external table over the orders-raw bucket and query the CSV files directly from BigQuery.
C.Create a Dataproc cluster with a scheduled PySpark job that reads the bucket and writes to BigQuery.
D.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.
AnswerA

BigQuery Data Transfer Service supports Cloud Storage as a data source and can load files from a bucket on a recurring schedule. This satisfies the minimal-operations requirement because Google manages the transfer runs, and the transfer automatically picks up new objects matching the source URI pattern on each scheduled run without any custom code.

Why this answer

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.

Exam trap

The trap here is assuming that querying files in place with an external table counts as loading data into BigQuery.

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18
Multi-Selecthard

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

Select 3 answers
A.Adding and removing data assets
B.Manually editing individual data files
C.Rebuilding the physical database indexes
D.Configuring data quality task schedules
E.Managing IAM permissions for lake users
AnswersA, D, E

Adding and removing data assets is a core Dataplex Lake administrative task, since a lake's assets define which Cloud Storage buckets and BigQuery datasets it governs. Without asset management, the lake cannot enforce metadata, discovery or security policies across its zones, directly satisfying the stem's requirement for common lake administration duties.

Why this answer

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.

Exam trap

GCP-ADP often tests the misconception that Dataplex administers the physical data (files, indexes) rather than the metadata and governance layer, causing candidates to select storage- or database-engine tasks.

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19
Multi-Selecthard

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)

Select 3 answers
A.Cloud Audit Logs to monitor access
B.Cloud Data Loss Prevention (DLP) API
C.Cloud Build for data encryption
D.Cloud Scheduler for data deletion
E.Policy Tags for fine-grained access
AnswersA, B, E

Cloud Audit Logs record who accessed or modified data and when, providing the immutable access trail needed to demonstrate PII handling compliance. Combined with Dataplex's governance and classification, this satisfies the stem's requirement to monitor and evidence access for compliance purposes.

Why this answer

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.

Exam trap

GCP-ADP often tests which GCP services map to compliance pillars — the trap is picking generic infrastructure services (Cloud Build, Cloud Scheduler) that sound operational but have no data-governance role.

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20
Multi-Selectmedium

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

Select 2 answers
A.A unique name for the Lake
B.A default encryption key
C.A geographic region
D.An external URL for the data
E.A list of all users in the organization
AnswersA, C

Every Dataplex Lake requires a unique name as its identifying attribute within the project and region. This satisfies the mandatory configuration the stem asks for, since the name is used to reference the lake in IAM policies, assets and zones.

Why this answer

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.

Exam trap

GCP-ADP often tests whether candidates assume encryption keys or data source URLs are required at lake creation, when in fact only name and region are mandatory — data attachment happens later at the zone/asset level.

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21
MCQhard

A compliance officer requires that a Cloud Storage bucket holding customer records retain every object version for seven years, and that no user, including project owners, be able to delete or overwrite those versions during that period. You need a configuration that enforces this even against accidental or malicious deletion attempts. What should you configure?

A.Grant the compliance team the Storage Object Viewer role and remove Storage Object Admin from all other users.
B.Apply a bucket lock on a retention policy that sets the retention period to seven years.
C.Enable Object Versioning on the bucket and set a lifecycle rule to delete noncurrent versions after 2555 days.
D.Create a Cloud Storage retention policy with a seven-year period but leave it unlocked.
AnswerB

A retention policy prevents objects from being deleted or overwritten until the retention period elapses, and locking the policy makes it permanent: it cannot be removed or shortened, and the retention period cannot be reduced. This enforces immutability for seven years even against project owners, satisfying the compliance requirement. Locking is irreversible, which is exactly what makes the guarantee strong.

Why this answer

A locked retention policy is the only Cloud Storage mechanism that guarantees objects cannot be deleted or overwritten for a fixed period regardless of IAM permissions, because locking removes the ability to shorten or delete the policy itself. Object Versioning and role restrictions provide recoverability or reduced exposure but remain reversible, so they cannot satisfy an enforced seven-year immutability mandate.

Exam trap

The trap here is treating Object Versioning as equivalent to immutability, when noncurrent versions remain deletable by anyone holding delete permissions.

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22
MCQmedium

You have a Dataplex Lake containing data from two different regions. How does this affect data access?

A.Users cannot access data across regions
B.Users must have access to both regional storage resources
C.Data access is automatically merged
D.The Lake automatically replicates data
AnswerB

A Dataplex Lake spanning two regions references storage buckets in each region. Access requires IAM permissions on both underlying regional buckets, so users need rights to both regional storage resources to query all lake data.

Why this answer

A Dataplex Lake can reference assets (Cloud Storage buckets or BigQuery datasets) that reside in different regions, but Dataplex does not move or replicate the data. Access to each asset is governed by the underlying regional storage resource, so users must have permissions and network access to both regions to query data across them. Thus, users must have access to both regional storage resources.

Exam trap

GCP-ADP often tests the misconception that a Dataplex Lake unifies or replicates data across regions, when in fact it only unifies metadata and governance — physical access still depends on the regional resources.

How to eliminate wrong answers

Option A is wrong because users can access data across regions — there is no hard block — provided they have the necessary IAM permissions and the services support cross-region access. Option C is wrong because data access is not automatically merged; Dataplex provides a unified metadata and governance layer, but the physical data remains in its original region and queries must reach each region. Option D is wrong because Dataplex does not automatically replicate data between regions; replication would be a separate Cloud Storage or BigQuery feature configured by the user.

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23
MCQmedium

You have a Data Catalog taxonomy that you want to share across multiple projects. How do you achieve this?

A.Move the taxonomy to the Organization node
B.Duplicate the taxonomy in each project
C.Define it in a central project and share access
D.Use Cloud Resource Manager to link them
AnswerC

Data Catalog taxonomies are project-scoped resources, so defining the taxonomy once in a central project and granting other projects' users or service accounts IAM access to it shares it without duplication. This satisfies the requirement to reuse one taxonomy across multiple projects.

Why this answer

Data Catalog taxonomies are project-scoped resources, so to share one across multiple projects you define it once in a central (host) project and grant other projects' users or service accounts the appropriate Data Catalog IAM roles (e.g., Data Catalog Viewer/Editor) on that taxonomy. This avoids duplication and keeps policy definitions consistent. Cross-project sharing is achieved through IAM bindings, not by relocating the resource.

Exam trap

GCP-ADP often tests the misconception that Data Catalog taxonomies can be attached to the Organization node like IAM policies or org-level custom roles, when in fact they are strictly project-scoped and shared only via IAM.

How to eliminate wrong answers

Option A is wrong because taxonomies cannot be attached to the Organization node — Data Catalog policy tags and taxonomies are project-level resources, and there is no organization-level taxonomy container. Option B is wrong because duplicating the taxonomy in each project creates drift and defeats the purpose of centralized governance; it also multiplies maintenance. Option D is wrong because Cloud Resource Manager manages projects, folders, and org hierarchy — it does not link or share Data Catalog taxonomies between projects.

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24
MCQmedium

What is the role of the 'Dataplex Data Steward'?

A.To write SQL queries for the business
B.To manage metadata, quality rules, and compliance
C.To develop ETL pipelines
D.To manage the underlying infrastructure
AnswerB

Dataplex Data Steward owns metadata curation, data quality rule definition, and compliance policy enforcement across lake zones and assets. This satisfies the stem's focus on governance duties, since stewards govern business metadata and quality thresholds rather than building pipelines or administering IAM, which fall to data engineers and platform admins.

Why this answer

A Dataplex Data Steward is responsible for managing metadata, defining and enforcing data quality rules, and ensuring compliance with governance policies across data assets. This role uses Dataplex features like Business Glossary, Data Quality tasks, and Data Catalog to curate and govern data. It is a governance-focused role, not an engineering or analytics role.

Exam trap

GCP-ADP often tests the confusion between governance roles (Data Steward) and engineering/analyst roles (ETL developer, SQL analyst), tempting candidates to pick a hands-on technical option instead of the governance-focused one.

How to eliminate wrong answers

Option A is wrong because writing SQL queries for the business is an analyst or data engineer responsibility, not the Data Steward's governance-focused role. Option C is wrong because developing ETL pipelines is a data engineering task; the Steward governs metadata and quality, not pipeline code. Option D is wrong because managing underlying infrastructure is a cloud/platform admin responsibility, not part of the Data Steward's metadata and compliance duties.

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25
MCQeasy

Which console menu would you navigate to in order to access Dataplex?

A.Compute Engine
B.Operations
C.Analytics
D.Storage
AnswerC

Dataplex is surfaced in the Google Cloud console under the Analytics section, alongside BigQuery and other data analytics services. Navigating to Analytics is therefore the menu path that exposes the Dataplex governance and metadata capabilities the question asks you to locate.

Why this answer

Dataplex is accessed from the Analytics section of the Google Cloud Console, alongside BigQuery, Dataflow, Dataproc, and other data analytics services. This is where Google groups its data governance and analytics offerings.

Exam trap

GCP-ADP often tests console navigation trivia, and candidates who associate Dataplex with Storage (because it governs data lakes) pick the wrong menu — the correct grouping is Analytics.

How to eliminate wrong answers

Option A is wrong because Compute Engine is for virtual machines and does not host Dataplex. Option B is wrong because Operations (formerly Stackdriver) is for monitoring, logging, and observability. Option D is wrong because Storage is for Cloud Storage buckets and related storage services, not data governance.

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26
MCQhard

You have a BigQuery table that is partitioned by day on a DATE column. You need to ensure that data older than 90 days is automatically deleted to reduce storage costs, while keeping the table structure and recent data intact. What should you do?

A.Enable table expiration on the table for 90 days.
B.Set a partition expiration on the table to 90 days.
C.Create a scheduled query that deletes rows older than 90 days daily.
D.Use a lifecycle rule on the dataset to delete tables older than 90 days.
AnswerB

Setting a partition expiration on a partitioned table automatically deletes partitions older than the specified number of days. This reduces storage costs without affecting the table structure or recent data. It is a built-in feature of BigQuery and requires no manual intervention.

Why this answer

Setting partition expiration on a partitioned table automatically removes partitions older than the specified retention period. This is the simplest and most cost-effective way to delete old data while preserving the table and recent partitions. It requires no manual intervention and integrates with BigQuery's storage management.

Exam trap

The trap here is confusing partition expiration with table expiration or dataset lifecycle rules, which delete entire tables rather than just old partitions.

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27
MCQmedium

You need to move an asset from one Dataplex Zone to another. What is the correct procedure?

A.Update the IAM policy to include the new zone
B.Delete the asset from the first zone and recreate it in the second
C.Use the 'move' command in the CLI
D.Update the asset metadata to point to the new zone
AnswerB

Dataplex assets are bound to a single zone at creation; no move operation exists. Deleting the asset and recreating it in the target zone is the only supported procedure, satisfying the stem's cross-zone relocation requirement.

Why this answer

In Dataplex, assets are immutable with respect to their zone assignment. To move an asset from one zone to another, you must delete it from the source zone and recreate it in the target zone. There is no direct move operation.

This procedure ensures that metadata and permissions are correctly re-established in the new zone.

Exam trap

GCP-ADP often tests the assumption that assets can be moved or updated like other resources, when in fact they must be deleted and recreated.

How to eliminate wrong answers

Option A is wrong because updating IAM policies does not change the asset's zone; it only controls access. Option C is wrong because there is no 'move' command in the Dataplex CLI for assets; such an operation does not exist. Option D is wrong because asset metadata includes the zone as an immutable property; you cannot update it to point to a new zone.

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28
MCQmedium

Your organization stores sensitive customer data in a Cloud Storage bucket. You need to ensure that data is automatically classified and tagged as 'PII' when it is written, and that the tags are available for audit and access control decisions. You want to minimize manual effort and use Google Cloud native tooling. What should you do?

A.Create a Cloud Function that triggers on object finalize events, calls the DLP API to inspect the object, and writes custom tags to a separate Firestore database.
B.Use Dataplex Data Quality tasks to scan the bucket and automatically apply PII tags based on predefined rules.
C.Enable Sensitive Data Protection (DLP) discovery on the bucket and configure a discovery scan with a schedule; then publish the results to Data Catalog.
D.Configure a VPC Service Controls perimeter around the bucket and enable Access Transparency logs to classify the data.
AnswerC

Sensitive Data Protection discovery profiles data in Cloud Storage, identifies infoTypes such as PII, and can publish findings to Data Catalog as tags. This automates classification and makes tags available for audit and IAM conditions. It is the native, low-effort solution for continuous auto-classification of data at rest in Cloud Storage.

Why this answer

Sensitive Data Protection discovery is the native Google Cloud service for automatically profiling and classifying data in Cloud Storage. It can detect PII infoTypes, run on a schedule, and publish findings as tags in Data Catalog. This satisfies the need for automatic classification with minimal manual effort and integrates with audit and access control.

Exam trap

The trap here is assuming that Data Quality tasks or custom code can automatically detect and tag PII, when only Sensitive Data Protection discovery provides built-in infoType detection and Data Catalog integration.

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29
MCQeasy

What does 'Data Lineage' show you in Dataplex?

A.The path the data took through the system
B.The number of users accessing the data
C.The cost of storing data
D.The database schema version
AnswerA

Data Lineage records how data flows and transforms across Dataplex assets, mapping source-to-target relationships through processing steps. This satisfies the stem by showing the path data took through the system, including upstream sources and downstream consumers.

Why this answer

Data Lineage in Dataplex shows the path data takes through the system — from source to destination, including transformations and dependencies. It provides a visual and programmatic view of how data flows across datasets, tables, and processes, enabling impact analysis and root-cause debugging.

Exam trap

GCP-ADP often tests the distinction between lineage (data flow path) and other metadata like schema, cost, or access — candidates confuse 'lineage' with 'audit' or 'catalog' concepts.

How to eliminate wrong answers

Option B is wrong because user access counts are an audit/usage metric, not lineage; lineage tracks data movement, not consumption. Option C is wrong because storage cost is a billing concern, not lineage. Option D is wrong because schema version is a metadata attribute, not the flow of data through the system.

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30
MCQeasy

You need to grant a data analyst read-only access to a specific BigQuery dataset. The analyst should be able to query tables but must not be able to modify data or view other datasets in the project. Which IAM role should you grant on the dataset?

A.roles/bigquery.dataEditor
B.roles/bigquery.user
C.roles/bigquery.dataViewer
D.roles/bigquery.metadataViewer
AnswerC

The BigQuery Data Viewer role (roles/bigquery.dataViewer) grants read-only access to data and metadata within a dataset. It allows the analyst to query tables and view schemas but not modify data or access other datasets. This role is the least privilege needed for read-only query access.

Why this answer

Granting roles/bigquery.dataViewer on the dataset provides read-only access to the data and metadata, allowing the analyst to query tables without modification rights. It is the least privilege role that meets the requirement and can be scoped to a specific dataset, preventing access to other datasets in the project.

Exam trap

The trap here is confusing roles that grant query job execution (like roles/bigquery.user) with roles that grant data access, leading to either insufficient permissions or over-provisioning.

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31
Multi-Selecthard

You are designing a BigQuery table for a fleet of IoT devices that emit one reading per second. The table will hold years of data, and most queries filter by device identifier and a time range. You want to reduce the bytes scanned by these queries and control long-term storage cost. (Choose two.)

Select 2 answers
A.Enable a customer-managed encryption key on the table to reduce storage cost.
B.Create a materialized view that aggregates readings per device per hour and query only the view.
C.Set the table's default expiration to 30 days so older data is deleted automatically.
D.Cluster the table on the device identifier column.
E.Partition the table by the event timestamp column using time-unit partitioning.
AnswersD, E

Clustering sorts data within each partition by the clustered column, so filters on device identifier can skip blocks that do not match. Combined with timestamp partitioning, this matches the common query pattern of device plus time range and further reduces bytes scanned.

Why this answer

Partitioning by the event timestamp and clustering by device identifier align the physical layout of the table with the dominant query pattern of filtering by device and time range. Together they let BigQuery prune partitions and skip blocks, cutting bytes scanned and cost, while still retaining years of data.

Exam trap

The trap here is treating storage-cost controls like table expiration or encryption as substitutes for query scan reduction.

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32
Multi-Selecthard

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

Select 3 answers
A.Define a clear zone strategy
B.Audit existing IAM permissions for the source data
C.Disable all data quality checks
D.Manually copy every file using scp
E.Enable discovery to inventory existing data
AnswersA, B, E

A zone strategy maps raw, curated and analytics zones before migration, so assets land in the correct Dataplex zone with appropriate security and discovery settings. This satisfies the stem's requirement for a migration consideration that prevents later restructuring.

Why this answer

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.

Exam trap

GCP-ADP often tests the misconception that migration means copying files or skipping quality checks, when the exam expects governance-first steps like zone strategy, IAM audit, and discovery.

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33
MCQhard

You are configuring a Dataplex asset that points to a GCS bucket. You want to ensure that only files in a specific folder are included in the asset. How do you specify this?

A.Define a SQL view on the folder
B.Use an IAM condition on the bucket
C.Specify a path filter in the asset definition
D.Move the files to a separate bucket
AnswerC

Dataplex assets support a path filter expression, such as a wildcard or prefix, that restricts discovery to matching objects under the bucket. This satisfies the requirement to include only files in a specific folder within the asset.

Why this answer

In Dataplex, when defining a zone or asset that points to a Cloud Storage bucket, you can specify a path filter (a prefix or folder path) in the asset definition to limit discovery and inclusion to only files under that folder. This is the native mechanism for scoping a Dataplex asset to a subset of a bucket.

Exam trap

GCP-ADP often tests the misconception that IAM conditions or separate buckets are required to scope Dataplex assets, when the built-in path filter is the correct, lightweight answer.

How to eliminate wrong answers

Option A is wrong because a SQL view operates on structured data in BigQuery, not on GCS object paths, and cannot filter files for a Dataplex asset. Option B is wrong because an IAM condition controls access permissions, not the set of files included in a Dataplex asset's discovery. Option D is wrong because moving files to a separate bucket is an operational workaround, not the configuration method for specifying a folder within an asset.

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34
MCQhard

You have an automated pipeline that deletes files in a GCS bucket every hour. Dataplex shows the asset as 'Out of sync'. Why?

A.The lake is locked for maintenance
B.The user does not have permission to delete files
C.Discovery needs to rescan to update metadata
D.The file deletion is violating the Dataplex policy
AnswerC

Dataplex metadata is a cached snapshot, not a live view. Hourly deletions change the bucket's contents without triggering discovery, so the asset's stored schema and file list drift from reality until the next rescan runs and refreshes them.

Why this answer

Dataplex Discovery periodically crawls GCS buckets to extract metadata and update the Dataplex Catalog. When files are deleted outside of Dataplex (e.g., by an automated pipeline), the cataloged metadata becomes stale, causing the asset to be marked 'Out of sync'. The correct resolution is to trigger a new discovery scan, which re-crawls the bucket and synchronizes the metadata with the current state.

Thus, option C is correct.

Exam trap

GCP-ADP often tests the misconception that 'Out of sync' indicates a permission or policy issue, when it actually signals metadata staleness requiring a discovery rescan.

How to eliminate wrong answers

Option A is wrong because lake maintenance locks are not a standard Dataplex feature and would not cause an 'Out of sync' status; maintenance typically results in a different error or unavailability. Option B is wrong because permission issues would prevent the deletion itself, not cause the asset to be out of sync; the pipeline is successfully deleting files, so permissions are not the problem. Option D is wrong because Dataplex policies govern data access and lifecycle, not file deletion operations; deleting files does not violate a policy but rather changes the underlying data, leading to metadata drift.

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35
MCQhard

You need to audit who accessed sensitive data in a Dataplex Lake. Which service should you monitor to see access logs for these data assets?

A.Dataplex Task Logs
B.Cloud Audit Logs
C.BigQuery Query Logs
D.Data Catalog history
AnswerB

Cloud Audit Logs records Data Access audit entries for Dataplex lake assets, capturing who read or wrote sensitive data. Enabling Data Access logs on the relevant services satisfies the audit requirement, since Admin Activity logs alone omit data reads.

Why this answer

Cloud Audit Logs, specifically Data Access audit logs, record all access to Dataplex Lake data assets, including who accessed what and when. Dataplex integrates with Cloud Audit Logs to provide a unified audit trail for data access across its assets. This is the correct service to monitor for auditing sensitive data access.

Exam trap

GCP-ADP often tests the distinction between service-specific logs (e.g., BigQuery Query Logs) and centralized audit logs (Cloud Audit Logs) for access auditing, causing candidates to overlook Cloud Audit Logs as the primary source.

How to eliminate wrong answers

Option A is wrong because Dataplex Task Logs capture logs from Dataplex tasks (e.g., discovery, quality), not user access to data assets. Option C is wrong because BigQuery Query Logs only cover queries executed in BigQuery, not access to Dataplex Lake assets across other storage systems. Option D is wrong because Data Catalog history tracks metadata changes, not data access events.

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36
MCQeasy

A data engineer needs to load a 500 GB CSV file from a Cloud Storage bucket into a BigQuery table once per day. The CSV has a header row and uses comma delimiters. The engineer wants the simplest managed approach that requires no code and handles schema autodetection. What should the engineer use?

A.BigQuery Data Transfer Service with a Cloud Storage transfer configuration
B.Cloud Dataproc with a PySpark job that reads the CSV from Cloud Storage and writes to BigQuery
C.bq load with the --autodetect flag executed from a cron job on a Compute Engine VM
D.Cloud Dataflow with a custom Apache Beam pipeline using TextIO and BigQueryIO
AnswerA

BigQuery Data Transfer Service supports Cloud Storage as a source and can load CSV files on a recurring schedule. It provides schema autodetection, header row handling, and requires no code, matching the requirement for a simple managed daily load of a 500 GB CSV file.

Why this answer

BigQuery Data Transfer Service is the managed, no-code option for scheduled loads from Cloud Storage into BigQuery. It supports CSV with headers, autodetects schema, and runs on a schedule without requiring a VM, cluster, or custom pipeline code. The other options all require managing infrastructure or writing code.

Exam trap

The trap here is assuming that any tool capable of loading CSV data satisfies a no-code managed requirement, when self-managed VMs, clusters, or custom pipelines introduce operational overhead the scenario explicitly excludes.

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37
MCQmedium

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?

A.Cloud Storage FUSE with a BigQuery remote function
B.BigQuery Data Transfer Service
C.Datastream with a BigQuery destination
D.BigQuery external tables over Cloud Storage
AnswerD

External tables let BigQuery query data that stays in Cloud Storage, so the team avoids duplicating storage costs while using standard SQL. This directly matches the requirement to query without loading into BigQuery native storage. They must still consider query performance and format support, but the core capability is exactly this.

Why this answer

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.

Exam trap

The trap here is assuming that any BigQuery data ingestion service can query Cloud Storage in place, when only external tables provide that in-place query capability.

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38
MCQmedium

You need to load a large CSV file from Cloud Storage into BigQuery. The file contains a header row, and you want to automatically detect the schema. You also need to ensure that if any row has a type mismatch, the load job fails and reports the error. Which load configuration should you use?

A.Set skipLeadingRows to 1 and autodetect to true, and set maxBadRecords to 0.
B.Set skipLeadingRows to 0 and autodetect to true, and set maxBadRecords to 0.
C.Set skipLeadingRows to 1 and autodetect to true, and set maxBadRecords to 10.
D.Set skipLeadingRows to 1 and autodetect to false, and set maxBadRecords to 0.
AnswerA

Setting skipLeadingRows to 1 skips the header row, autodetect enables schema inference, and maxBadRecords to 0 ensures that any bad record causes the job to fail. This configuration meets all requirements: header handling, schema detection, and strict error reporting.

Why this answer

The correct configuration skips the header row, enables schema autodetection, and sets maxBadRecords to 0 to fail on any bad record. This ensures the load job processes the CSV correctly, infers the schema, and enforces strict error handling as required.

Exam trap

The trap here is assuming that maxBadRecords can be left at a default or set to a positive number while still failing on any bad record, when only setting it to 0 enforces immediate failure.

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39
MCQmedium

A healthcare organization needs to analyze sensitive patient data in BigQuery while ensuring that analysts can see aggregated results but cannot view individual patient identifiers. Which Google Cloud feature should they implement?

A.Row-level security with filter predicates
B.BigQuery authorized views
C.Column-level security with policy tags
D.VPC Service Controls perimeter around BigQuery
AnswerC

Column-level security with policy tags in BigQuery lets you classify columns containing identifiers and restrict access so analysts can query other columns without seeing sensitive values. This directly supports viewing aggregated results while blocking individual identifiers. It is the native BigQuery mechanism for fine-grained column access control.

Why this answer

Column-level security with policy tags allows classifying sensitive columns and granting access only to authorized users. Analysts without the policy tag role can query other columns and compute aggregates but cannot see patient identifiers. Other options either restrict rows, use less scalable view patterns, or address network boundaries rather than column visibility.

Exam trap

The trap here is confusing row-level security with column-level security, when only the latter hides individual identifier columns.

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40
MCQhard

You are configuring a Dataplex Data Quality task. You want to validate that a 'user_id' column has no NULL values. Where do you define this rule?

A.In the Cloud Storage metadata
B.In a YAML configuration file
C.In the BigQuery table schema
D.In the Dataplex Asset definition
AnswerB

Dataplex Data Quality rules are declared in a YAML spec, where each rule targets a column and sets a dimension plus a nonNull expectation. Defining the rule in that YAML configuration file is what enforces the no-NULL constraint on user_id.

Why this answer

Dataplex Data Quality tasks are defined using a YAML specification that describes the rules, including non-null constraints on columns. The YAML file is uploaded to a Cloud Storage bucket and referenced when creating the Data Quality task. This approach allows for flexible, version-controlled rule definitions that are decoupled from the underlying data schema.

Exam trap

GCP-ADP often tests the misconception that data quality rules are defined in the data source schema (e.g., BigQuery schema) or in the asset metadata, when in fact Dataplex uses a separate YAML specification.

How to eliminate wrong answers

Option A is wrong because Cloud Storage metadata (e.g., object metadata) is used for storing arbitrary key-value pairs about objects, not for defining data quality validation rules. Option C is wrong because BigQuery table schema defines column names and data types, but does not enforce or define data quality rules such as non-null checks; NOT NULL constraints exist but are not used by Dataplex Data Quality tasks. Option D is wrong because the Dataplex Asset definition describes the resource (e.g., a BigQuery table or Cloud Storage bucket) and its discovery status, but does not contain data quality rule specifications.

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41
MCQeasy

You manage a BigQuery dataset that contains a table with 500 TB of historical order data. Analysts frequently run queries that filter on the `order_date` column. Query costs are rising because every query scans the entire table. You want to reduce the bytes scanned and cost without changing the table name or existing SQL. What should you do?

A.Set a custom query quota on the project so analysts cannot run queries that scan more than 1 TB.
B.Enable the BigQuery BI Engine reservation on the dataset to cache query results and lower cost.
C.Partition the table by the `order_date` column so queries filtering on that column scan only relevant partitions.
D.Create a materialized view that pre-aggregates orders by date and instruct analysts to query the view instead.
AnswerC

Partitioning the table by `order_date` makes BigQuery prune partitions that do not match the filter predicate, so a query for a specific date range reads only those partitions rather than the full 500 TB. This directly reduces bytes scanned and cost while keeping the table name and existing SQL unchanged, satisfying every stated requirement.

Why this answer

Partitioning a table by a column that queries commonly filter on lets BigQuery prune partitions and read only the data needed, which reduces bytes scanned and therefore cost. Because partitioning does not change the table name or require SQL changes, it satisfies all the constraints. The other choices either require SQL rewrites, do not affect scan volume, or block queries instead of optimizing them.

Exam trap

The trap here is assuming that any performance feature, such as a materialized view or BI Engine, will automatically reduce bytes scanned on the base table when only partition pruning actually does that.

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42
Multi-Selecteasy

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

Select 2 answers
A.A private on-premises data center
B.The root organization node
C.The user's local laptop
D.US (Multi-region)
E.US-central1
AnswersD, E

Dataplex lakes accept multi-region locations such as US, satisfying the stem's requirement for a valid placement. Multi-region scopes data across the United States, unlike single-region or zonal options, and Dataplex supports this alongside regional locations. This makes US (Multi-region) a legitimate lake location.

Why this answer

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.

Exam trap

The trap is confusing logical/resource hierarchy locations (organization node) or on-premises environments with valid GCP geographic locations where managed services like Dataplex lakes can actually be provisioned.

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43
Multi-Selecthard

You are designing a data lake in Cloud Storage and need to ensure that analysts can query the data using BigQuery without moving or copying the data. You also need to enforce fine-grained access control at the column level for sensitive fields. Which two actions should you take? (Choose two.)

Select 2 answers
A.Create a BigQuery dataset and use authorized views to restrict access to specific columns.
B.Create an external table in BigQuery that points to the Cloud Storage data.
C.Enable uniform bucket-level access on the Cloud Storage bucket.
D.Set up a Dataplex lake and zone, and register the Cloud Storage bucket as an asset.
E.Use BigQuery policy tags to define column-level access control on the external table.
AnswersB, E

External tables in BigQuery allow you to query data directly from Cloud Storage without loading it into BigQuery. This satisfies the requirement to query without moving data. You can define the schema and then use policy tags on columns to enforce fine-grained access control, making this a necessary step for the solution.

Why this answer

To query Cloud Storage data in BigQuery without moving it, you create an external table. To enforce column-level access control, you apply policy tags to the columns of that external table. Policy tags are the native BigQuery feature for column-level security and work with external tables, meeting both requirements.

Exam trap

The trap here is assuming that Dataplex or bucket-level permissions can enforce column-level access control for BigQuery queries, but only BigQuery policy tags provide that capability.

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44
MCQeasy

Which service allows you to view the schema and metadata of a BigQuery table that has been registered in Dataplex?

A.Data Catalog
B.Cloud Storage Console
C.Cloud Build
D.Cloud IAM console
AnswerA

Data Catalog surfaces technical metadata and schema for BigQuery tables registered in Dataplex, satisfying the requirement to inspect table structure. Dataplex automatically publishes entries to Data Catalog, which exposes column names, types and descriptions through search and the API, letting you view schema without querying the table directly.

Why this answer

Data Catalog is the metadata management service in Google Cloud that indexes and exposes schema, column names, descriptions, tags, and other metadata for BigQuery tables (and other assets) registered in Dataplex. Once a BigQuery table is registered as a Dataplex asset, its technical metadata is surfaced through Data Catalog, which is the intended UI/API for schema and metadata discovery.

Exam trap

The trap is confusing Dataplex (the governance/discovery layer that registers assets) with Data Catalog (the metadata UI/API that displays schemas) — candidates pick Dataplex itself or a storage console instead of the catalog service.

How to eliminate wrong answers

Option B is wrong because the Cloud Storage Console only shows bucket and object metadata (names, sizes, storage classes), not BigQuery table schemas. Option C is wrong because Cloud Build is a CI/CD service for building and deploying containers/artifacts and has no role in metadata cataloging. Option D is wrong because the Cloud IAM console manages identities, roles, and permissions — it does not display data schemas or table metadata.

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45
MCQeasy

What is the purpose of a 'Data Zone' in Dataplex?

A.To execute Spark jobs
B.To store query results
C.To act as a physical storage location
D.To provide a logical grouping for governance and policies
AnswerD

A Data Zone groups assets within a lake so policies, IAM and metadata rules apply consistently across them. This satisfies the stem's governance purpose, providing the logical boundary where Dataplex enforces access and policy controls.

Why this answer

A Data Zone in Dataplex is a logical construct that groups data assets (such as Cloud Storage buckets, BigQuery datasets, and BigLake tables) so that a single set of governance policies, discovery, and access controls can be applied across them. It does not provision or represent physical storage; it is an organizational boundary that maps to underlying resources. This lets data stewards manage metadata, quality, and security consistently without moving or duplicating data.

Exam trap

The trap here is confusing Dataplex's logical governance constructs (Lakes, Zones, Assets) with physical compute or storage services, leading candidates to pick 'physical storage location' or 'execute Spark jobs'.

How to eliminate wrong answers

Option A is wrong because Spark job execution is handled by Dataproc or Dataplex's own task/processing features, not by a Data Zone, which is purely a governance grouping. Option B is wrong because query results are stored by the query engine (e.g., BigQuery) in its own result tables, not in a Data Zone. Option C is wrong because a Data Zone is a logical abstraction over existing assets, not a physical storage location — the actual bytes remain in Cloud Storage, BigQuery, or BigLake.

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46
MCQeasy

You have sensitive data in Cloud Storage that needs to be deleted after 30 days to comply with regulatory requirements. What is the most efficient way to manage this?

A.Use Cloud Storage lifecycle management policies
B.Run a manual BigQuery script
C.Write a Cloud Function to check dates and delete files
D.Create a Dataplex lifecycle task
AnswerA

Cloud Storage lifecycle management policies automatically delete objects once they reach the specified age, satisfying the 30-day regulatory retention limit without custom code or manual intervention. Rules apply at bucket level across matching objects, making this the most efficient, serverless mechanism for enforcing time-based deletion at scale.

Why this answer

Cloud Storage Object Lifecycle Management lets you define rules directly on a bucket (e.g., Delete objects 30 days after creation) that GCS enforces automatically at no additional cost. This is the native, server-side, most efficient mechanism for time-based deletion and requires no code, scheduler, or external service.

Exam trap

The trap here is reaching for a compute-based solution (Cloud Function, script) when GCS provides a declarative, zero-maintenance lifecycle feature — candidates over-engineer because they forget lifecycle rules exist.

How to eliminate wrong answers

Option B is wrong because BigQuery scripts operate on BigQuery datasets/tables, not on Cloud Storage objects, and cannot delete GCS objects. Option C is wrong because a Cloud Function would require writing, deploying, scheduling, and maintaining code plus a trigger (e.g., Cloud Scheduler), which is operationally heavier and more error-prone than a declarative lifecycle rule. Option D is wrong because Dataplex lifecycle tasks govern Dataplex lake/zone/asset metadata and data discovery, not raw GCS object retention; Dataplex does not delete GCS objects on a schedule.

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47
MCQmedium

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?

A.Partition the table by a DATE or TIMESTAMP column using daily partitioning.
B.Create a separate table for each month and use a UNION ALL view to combine them.
C.Create a materialized view that selects the last 30 days of data and instruct analysts to query the view.
D.Enable the BigQuery BI Engine reservation on the dataset.
AnswerA

Partitioning the table by a date or timestamp column allows BigQuery to prune partitions when queries filter on that column, scanning only the relevant partitions. This reduces bytes processed and improves performance for recent-data queries. It requires no change to how analysts write queries as long as they filter on the partitioning column.

Why this answer

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.

Exam trap

The trap here is assuming that a materialized view or BI Engine automatically reduces bytes scanned for ad-hoc time-filtered queries, when only partitioning prunes data at the storage level.

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48
MCQeasy

Which service should you use to search for and discover datasets across your entire Google Cloud organization?

A.BigQuery Explorer
B.Cloud Storage Browser
C.Cloud Logging
D.Data Catalog
AnswerD

Data Catalog provides a searchable, organisation-wide inventory of datasets and tables, letting users discover and tag assets across all projects. It indexes metadata from BigQuery and other sources, satisfying the requirement to search the entire Google Cloud organisation.

Why this answer

Data Catalog is Google Cloud's fully managed, scalable metadata management service that enables search and discovery of datasets across the organization. It provides a unified inventory of data assets with tagging and search capabilities, making it the correct choice for discovering datasets organization-wide.

Exam trap

GCP-ADP often tests the difference between service-specific browsers (BigQuery Explorer, Cloud Storage Browser) and the cross-organizational discovery tool (Data Catalog) — candidates pick the service they know best rather than the one designed for enterprise-wide search.

How to eliminate wrong answers

Option A is wrong because BigQuery Explorer only searches within BigQuery datasets, not across the entire organization or other services. Option B is wrong because Cloud Storage Browser lists buckets and objects but does not provide metadata search or discovery across datasets. Option C is wrong because Cloud Logging is for log ingestion and analysis, not dataset discovery.

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49
MCQmedium

What happens if you delete a Dataplex Lake?

A.The data is moved to a 'trash' bucket
B.The lake is deleted, but the underlying assets remain
C.Access to the assets is granted to all users
D.All underlying data assets are deleted
AnswerB

Deleting a Dataplex lake removes only the lake resource and its metadata configuration; the underlying Cloud Storage or BigQuery assets themselves are untouched. This satisfies the stem's scenario, since the assets remain accessible directly after the lake is gone.

Why this answer

Deleting a Dataplex Lake removes the lake resource itself but does not delete the underlying data assets (such as Cloud Storage buckets or BigQuery datasets) that were attached to it. The assets remain intact and can be re-attached to another lake or managed separately. This design prevents accidental data loss and allows for flexible re-organization of data governance structures.

Exam trap

GCP-ADP often tests the distinction between metadata and data; candidates assume deleting a lake deletes the data, but Dataplex only manages metadata, leaving assets intact.

How to eliminate wrong answers

Option A is wrong because Dataplex does not move data to a trash bucket; deletion of a lake does not affect the storage location of assets. Option C is wrong because deleting a lake does not grant access to all users; IAM policies remain unchanged. Option D is wrong because underlying data assets are not deleted; only the lake metadata and management layer are removed.

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50
Multi-Selectmedium

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

Select 2 answers
A.Discovery must be manually triggered for every scan
B.Discovery works only for BigQuery tables
C.Discovery deletes duplicate files
D.Discovery scans and infers schema from the data
E.Discovery generates metadata for the data assets
AnswersD, E

Discovery jobs sample data in the zone's assets and infer schema, deriving column names, data types and structure from the actual files or tables. This inferred schema populates the Dataplex metadata catalogue, enabling search and governance without manual schema definition.

Why this answer

Option D is correct because Dataplex discovery automatically scans data in supported storage systems (such as Cloud Storage buckets and BigQuery datasets) and infers the schema by sampling and profiling the underlying files or tables. Option E is correct because the discovery process produces and publishes metadata — including schema, data profiles, and other statistics — into the Dataplex Data Catalog for the discovered assets. Option A is incorrect because discovery can run on a schedule (and can also be triggered on-demand), so it does not require manual triggering for every scan.

Option B is incorrect because discovery supports multiple data sources, including Cloud Storage objects and BigQuery, not only BigQuery tables. Option C is incorrect because discovery is a metadata and schema inference service; it does not delete or deduplicate files.

Exam trap

GCP-ADP often tests the misconception that Dataplex Discovery is a data-transformation or deduplication tool, when it is strictly a metadata and schema-inference service.

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51
Multi-Selecthard

You have noticed that your data quality tasks are failing. Which THREE of the following are potential causes for failure? (Choose three)

Select 3 answers
A.The YAML rule configuration is malformed
B.The service account lacks permissions on the data
C.The user has a slow internet connection
D.The Dataproc environment is not available
E.The data files were moved to another lake
AnswersA, B, D

Data quality tasks parse the YAML rule specification before execution; malformed syntax, invalid field names or wrong indentation causes the task to fail at configuration load, before any data is scanned. This directly explains the observed task failures.

Why this answer

Option A is correct because data quality tasks in tools like Cloud Dataplex/DQ rely on YAML rule specifications, and malformed YAML (bad indentation, invalid keys, syntax errors) will cause the task to fail during parsing before execution. Option B is correct because the service account executing the data quality job must have the required IAM permissions (e.g., BigQuery Data Viewer, Storage Object Viewer) on the underlying data; missing permissions cause authorization errors and task failure. Option D is correct because data quality tasks often run on a Dataproc cluster or serverless Dataproc environment, so if that environment is unavailable (deleted, misconfigured, or out of quota), the job cannot execute and fails.

Option C is not a valid cause because a slow internet connection on the user's side does not affect server-side data quality task execution. Option E is not a valid cause because moving data files to another lake would typically result in a 'not found' or empty-scan condition rather than being a general failure cause, and it is not one of the marked correct answers.

Exam trap

GCP-ADP often tests the misconception that client-side factors (like internet speed) affect server-side Dataplex job execution, when failures are driven by config, IAM, or Dataproc availability.

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52
MCQmedium

You have a BigQuery table that is partitioned by ingestion time. You need to reduce storage costs by automatically deleting data older than 90 days. You want to ensure that the deletion happens without manual intervention and that the partitions are completely removed. What should you do?

A.Set a partition expiration on the table for 90 days.
B.Configure a Cloud Scheduler job that calls the BigQuery API to drop partitions older than 90 days.
C.Create a scheduled query that runs daily to delete rows older than 90 days using a DELETE statement.
D.Use a lifecycle rule on the BigQuery dataset to delete tables older than 90 days.
AnswerA

Partition expiration in BigQuery automatically deletes partitions after a specified number of days. For ingestion-time partitioned tables, you can set the partition expiration at the table level, and BigQuery will remove partitions older than the specified duration. This is a native, automated way to manage storage costs and ensures partitions are completely deleted.

Why this answer

Partition expiration is a table setting that automatically deletes partitions after a specified number of days. For ingestion-time partitioned tables, this is the simplest and most efficient way to remove old data and reduce storage costs. It requires no manual intervention and ensures partitions are dropped entirely.

Exam trap

The trap here is thinking that a scheduled DELETE or custom script is needed, but partition expiration is a built-in feature that automatically manages partition deletion.

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53
MCQhard

You need to ensure that PII (Personally Identifiable Information) in BigQuery is automatically detected and tagged across your organization. Which Dataplex feature should you enable on your Lake?

A.Automatic data discovery
B.Data quality task
C.Lifecycle management policy
D.IAM policy inheritance
AnswerA

Automatic data discovery profiles and scans data in the lake, using its built-in classifiers to detect sensitive information such as PII and attach corresponding tags automatically. This satisfies the stem's requirement for organisation-wide automatic detection and tagging without manual intervention.

Why this answer

Automatic data discovery in Dataplex scans data in a Lake and automatically identifies and tags sensitive information such as PII using built-in or custom data classifiers. It profiles data assets and applies metadata tags, which is exactly what's needed to detect and tag PII across an organization.

Exam trap

GCP-ADP often tests the difference between data discovery/classification (Dataplex discovery, DLP) and data quality (Dataplex data quality tasks) — candidates pick 'data quality' because it sounds like it involves inspecting data, but it evaluates rules, not sensitivity.

How to eliminate wrong answers

Option B is wrong because a data quality task evaluates data against defined quality rules (e.g., completeness, uniqueness) — it does not detect or tag PII. Option C is wrong because lifecycle management policies govern storage tiering and deletion of data, not data classification. Option D is wrong because IAM policy inheritance controls access permissions, not data discovery or tagging.

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54
MCQmedium

You are the data engineer for a retail company on Google Cloud. Analysts query a BigQuery table that contains 400 TB of point-of-sale records and almost always filter by transaction_date. Query costs have grown sharply because every query scans the full table. You need the most cost-effective change that reduces bytes scanned for date-filtered queries without altering the table schema. What should you do?

A.Enable the BigQuery BI Engine reservation and let the in-memory cache absorb the repeated date-range scans.
B.Partition the table by the transaction_date column, since partition pruning skips partitions that do not match the filter predicate.
C.Add a clustering key on transaction_date and rely on cluster pruning to eliminate all non-matching data blocks.
D.Create a materialized view that pre-aggregates daily sales totals, then point all analyst queries at the view.
AnswerB

Partitioning the table on transaction_date lets BigQuery prune partitions when a query filters on that column, so only the relevant date ranges are read instead of the full 400 TB. It requires no schema change to existing columns and is the standard low-cost lever for reducing bytes scanned on time-filtered analytical workloads.

Why this answer

Partitioning the table on the transaction_date column is correct because BigQuery uses partition pruning to read only the partitions that satisfy the date predicate, directly lowering bytes scanned and cost. Clustering, materialized views, and BI Engine can help in narrower cases, but none changes the physical partitioning of the huge base table the way date partitioning does.

Exam trap

The trap here is assuming clustering on a date column delivers the same pruning benefit as partitioning, when partitioning is the stronger and more predictable reduction for date-range filters.

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55
MCQhard

You are a data engineer at a healthcare company. You need to create a Dataflow pipeline that reads from a Pub/Sub subscription and writes to BigQuery. The pipeline must handle late-arriving data and ensure exactly-once processing. You also need to minimize cost. Which of the following should you use?

A.Use Dataflow with the Pub/Sub to BigQuery template and set the --discardUnknownFields option.
B.Use BigQuery's streaming ingestion with a Pub/Sub subscription directly.
C.Use Dataflow with Apache Beam's PubSubIO and BigQueryIO, enabling windowing and triggers for late data, and use BigQueryIO.Write with exactly-once semantics.
D.Use Cloud Data Fusion with a Realtime pipeline that writes to BigQuery.
AnswerC

Apache Beam's PubSubIO and BigQueryIO support windowing and triggers to handle late-arriving data. BigQueryIO.Write can be configured with exactly-once semantics using the withMethod and withWriteDisposition settings. This approach ensures data is processed correctly even if it arrives late, and exactly-once processing is achieved. It is the standard way to build such pipelines in Dataflow.

Why this answer

Dataflow with Apache Beam provides the necessary constructs for exactly-once processing and late data handling. PubSubIO and BigQueryIO support windowing and triggers, and BigQueryIO.Write can be configured for exactly-once semantics. Other options either lack these features or are not cost-effective for this use case.

Therefore, using Dataflow with these configurations is the correct approach.

Exam trap

The trap here is assuming that BigQuery streaming ingestion or templates alone can guarantee exactly-once processing and late data handling without Apache Beam's windowing and triggers.

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56
MCQeasy

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?

A.Export the campaign table to Cloud Storage, then run the join in Cloud Shell using a local SQL engine.
B.Create a BigQuery external table over the CSV file in Cloud Storage and join it with the campaign table in the query.
C.Import the CSV into BigQuery as a native table and set up a scheduled query to re-import it weekly.
D.Use the bq load command to append the CSV into a partitioned table each week.
AnswerB

An external table points at the CSV object in Cloud Storage, so each query reads the current file contents. Joining that external table with a native campaign table is fully supported in BigQuery SQL, and it avoids any weekly load job, which matches the requirement to always reflect the latest partner data.

Why this answer

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.

Exam trap

The trap here is assuming that any join with a Cloud Storage file requires first loading that file into BigQuery.

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57
MCQeasy

What is the primary function of the 'Dataplex Discovery' process?

A.To manage VPC firewall rules
B.To run SQL queries
C.To migrate data to the cloud
D.To automatically catalog and profile data
AnswerD

Dataplex Discovery automatically scans registered data lakes and zones, generating metadata and profiling statistics for each dataset. This satisfies the stem's requirement for the primary function: it catalogues assets into the Knowledge Catalog and profiles their structure and content, enabling search, governance and understanding without manual metadata entry.

Why this answer

Dataplex Discovery is the automated process that scans data assets (Cloud Storage buckets, BigQuery datasets) to infer schemas, extract metadata, and profile data (e.g., statistics, data quality). It populates the Dataplex Catalog so users can search, understand, and govern data without manual cataloging. Its primary function is therefore to automatically catalog and profile data.

Exam trap

GCP-ADP often tests whether candidates confuse Dataplex Discovery with data movement or query execution, when its actual role is metadata extraction, cataloging, and profiling.

How to eliminate wrong answers

Option A is wrong because VPC firewall rules are managed by VPC firewall, not Dataplex; Dataplex has no role in network security configuration. Option B is wrong because running SQL queries is the function of BigQuery (or other query engines), not Dataplex Discovery; Dataplex may surface metadata but does not execute queries. Option C is wrong because migrating data to the cloud is handled by services like Storage Transfer Service or Database Migration Service; Dataplex Discovery does not move data, it only inspects it in place.

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58
MCQhard

You have data scattered across multiple projects and need to provide a unified data governance view. What is the recommended hierarchy?

A.A single GCS bucket for all data
B.Multiple Lakes, one per project, and manage them independently
C.One Lake, with assets added from multiple projects
D.One Lake per project, then use a project-linking tool
AnswerC

Dataplex lakes span projects: a single lake acts as the governance boundary, and you attach assets that reference BigQuery datasets or Cloud Storage buckets residing in other projects. This gives one unified view without duplicating or moving the underlying data.

Why this answer

Dataplex is designed so that a single Lake can contain Zones and Assets that reference resources from multiple projects, giving one unified governance and discovery view across the organization. This is the recommended hierarchy because it centralizes policy, metadata, and lineage while still allowing data to physically reside in different projects.

Exam trap

The trap is assuming governance must mirror project boundaries (one lake per project) — the exam tests whether you know Dataplex lakes can aggregate assets across projects into a single governance view.

How to eliminate wrong answers

Option A is wrong because consolidating all data into one GCS bucket ignores project-level isolation, IAM boundaries, and regional/data-residency requirements, and does not provide governance metadata. Option B is wrong because multiple independent lakes per project defeats the purpose of a unified view and forces duplicated governance effort. Option D is wrong because there is no 'project-linking tool' in Dataplex; lakes are the linking construct, and one-lake-per-project plus a fictional tool is not a supported architecture.

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59
MCQeasy

You are a data analyst at a marketing firm. You need to query a large dataset stored in BigQuery, but you want to minimize query costs. The dataset contains a column named event_time of type TIMESTAMP. Most of your queries filter on event_time for a specific day. What should you do to reduce the amount of data scanned by your queries?

A.Add a clustered column on event_time to the table.
B.Create a partitioned table on the event_time column.
C.Use the LIMIT clause to restrict the number of rows returned.
D.Use a wildcard table query to select only the relevant shards.
AnswerB

Partitioning the table on event_time allows BigQuery to scan only the partitions that contain the relevant data. When you filter on a specific day, BigQuery prunes all other partitions, significantly reducing the bytes processed and thus the query cost. This is the most effective way to minimize costs for queries that filter on a time column. It requires recreating or altering the table to be partitioned.

Why this answer

Partitioning the table on event_time enables BigQuery to prune partitions that do not match the filter. When querying for a specific day, only that day's partition is scanned, reducing the amount of data processed and lowering cost. Other methods like LIMIT, wildcard tables, or clustering do not achieve the same cost reduction for time-filtered queries.

Exam trap

The trap here is thinking that LIMIT reduces cost, when it only limits the result set, not the data scanned.

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60
Multi-Selecthard

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)

Select 3 answers
A.Grant Dataplex-specific roles to users
B.Apply IAM roles to the underlying storage resources
C.Use Policy Tags for column-level security
D.Use IP-based firewalls for all datasets
E.Assign roles to individual files
AnswersA, B, C

Dataplex defines its own IAM roles, such as Dataplex Viewer and Dataplex Editor, that grant access to lake, zone and asset metadata independently of the underlying storage. Assigning these roles to users satisfies the requirement to control access within the lake itself.

Why this answer

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.

Exam trap

GCP-ADP often tests whether candidates understand that Dataplex does not replace native storage IAM — the trap is assuming Dataplex roles alone grant data access, or that network controls like firewalls apply to lake data.

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61
Multi-Selectmedium

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)

Select 2 answers
A.The user needs BigQuery permissions on the dataset
B.The user needs Dataplex asset-management permissions
C.The dataset must be public
D.The dataset must exist in the same project as the Lake
E.The dataset must contain at least one table
AnswersA, B

Adding a BigQuery dataset as a lake asset requires the user to hold BigQuery permissions on that dataset, such as bigquery.datasets.get. This satisfies the stem's prerequisite, since Dataplex must be able to read the dataset's metadata before registering it.

Why this answer

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.

Exam trap

GCP-ADP often tests that Dataplex asset attachment requires dual permissions (Dataplex + BigQuery) and supports cross-project datasets, so candidates who assume the dataset must be public or co-located pick the wrong options.

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62
Multi-Selectmedium

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)

Select 2 answers
A.Assign different IAM policies to different zones
B.Use zones to store query results
C.Create separate zones for each user
D.Group data by processing state (e.g., raw, curated)
E.Use zones to increase data storage speed
AnswersA, D

Dataplex zones are logical partitions of a lake, and IAM policies can be attached at zone level, so each zone grants distinct access. This satisfies the stem's requirement for a valid way zones improve data organisation.

Why this answer

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).

Exam trap

GCP-ADP often tests whether candidates understand that Dataplex Zones are governance constructs — the trap is selecting options that treat zones as storage, performance, or per-user isolation mechanisms.

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63
MCQmedium

You are loading a daily CSV file from Cloud Storage into a BigQuery table. The file contains a header row, uses comma delimiters, and has some rows with extra trailing columns. You want the load to tolerate these malformed rows by skipping them and recording how many were skipped, without failing the whole job. Which load configuration should you use?

A.Set `skipLeadingRows` to 1 and set `allowJaggedRows` to false.
B.Set `skipLeadingRows` to 1 and set `ignoreUnknownValues` to true.
C.Set `skipLeadingRows` to 1 and set `maxBadRecords` to a positive value.
D.Set `skipLeadingRows` to 1 and enable `allowQuotedNewlines`.
AnswerC

`skipLeadingRows` skips the header row, and `maxBadRecords` lets the load succeed as long as the number of bad rows does not exceed the threshold. BigQuery records the count of skipped records, which you can inspect. A row with extra columns counts as a bad record, so this configuration tolerates malformed rows instead of failing the entire job.

Why this answer

`maxBadRecords` is the setting that lets a load job complete while skipping rows that cannot be parsed, and BigQuery reports how many were skipped. Pairing it with `skipLeadingRows` handles the header so the header is not counted as a bad row, giving a load that tolerates the malformed data and still succeeds.

Exam trap

The trap here is confusing `ignoreUnknownValues`, which applies to extra fields in formats like JSON, with tolerance for CSV rows that have the wrong number of columns.

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64
MCQeasy

A user complains they cannot see a Dataplex Lake in the console. You verify they have the 'Viewer' role on the project. What is missing?

A.They need to refresh their browser
B.They need the 'roles/dataplex.viewer' role
C.They need the 'Owner' role
D.They need to be added to the project's whitelist
AnswerB

Project-level Viewer grants broad read access but does not include Dataplex-specific permissions. The roles/dataplex.viewer role supplies dataplex.lakes.get and related metadata reads required to render the Lake in the console, so it must be granted separately.

Why this answer

Dataplex uses its own IAM roles, and project-level Viewer (roles/viewer) does not grant permission to view Dataplex lakes. The user needs the Dataplex Viewer role (roles/dataplex.viewer) — or a broader role that includes dataplex.lakes.get/list — to see the lake in the console.

Exam trap

The trap is assuming basic project roles (Viewer/Editor/Owner) cover all services — GCP-ADP tests whether you know Dataplex requires its own service-specific IAM roles like roles/dataplex.viewer.

How to eliminate wrong answers

Option A is wrong because a browser refresh does not change IAM permissions; if the role is missing, the lake will remain invisible regardless of refresh. Option C is wrong because Owner is far broader than necessary and is not the specific missing permission — the principle of least privilege calls for roles/dataplex.viewer, and Owner would work but is not the correct/least-privilege answer. Option D is wrong because there is no project 'whitelist' concept in GCP IAM; access is granted via IAM bindings, not whitelists.

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65
Multi-Selectmedium

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

Select 2 answers
A.Assets can be created without a storage resource
B.Assets inherit the location of the zone
C.An asset must belong to exactly one zone
D.Assets can only be BigQuery tables
E.Assets duplicate the physical data
AnswersB, C

A Dataplex asset is created within a zone, and its location is not set independently; it is derived from the parent zone's location. This inheritance satisfies the constraint that assets cannot span regions outside their zone.

Why this answer

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.

Exam trap

GCP-ADP often tests the hierarchical relationships and constraints of Dataplex components, such as whether an asset can exist without a zone or if it can belong to multiple zones, leading candidates to incorrectly assume flexibility.

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66
MCQmedium

Your analytics team stores clickstream events in Cloud Storage as newline-delimited JSON files. They want to query this data with BigQuery using SQL without loading it into native BigQuery storage, and they want the table definition to refresh automatically when new files land in the bucket. What should you configure?

A.Create a BigQuery external table over the Cloud Storage bucket and enable automatic metadata refresh.
B.Create a Dataplex asset over the bucket and query it with the Dataplex SQL endpoint.
C.Mount the bucket with Cloud Storage FUSE on a Dataproc cluster and query it with Spark SQL.
D.Load the JSON files into a native BigQuery table using a recurring Cloud Composer job.
AnswerA

A BigQuery external table lets you query data that lives in Cloud Storage directly with SQL, so no ingestion into BigQuery native storage is needed. Enabling automatic metadata refresh makes BigQuery detect new files and update the table's partition and file metadata on its own, which is exactly the behavior requested. The external table definition stays decoupled from the physical data, and the refresh interval is governed by the max_staleness setting.

Why this answer

The scenario calls for querying Cloud Storage files with BigQuery SQL and having the table definition track new files automatically. An external table over Cloud Storage satisfies in-place querying, and automatic metadata refresh keeps the table aware of newly added objects without manual intervention. The other approaches either copy data into native storage, rely on a non-existent SQL endpoint, or move the workload to a different engine and dialect.

Exam trap

The trap here is assuming data must be loaded into native BigQuery storage before it can be queried with SQL, when external tables query Cloud Storage directly.

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