GCP-ADP Multiple Select — 30 Questions

Question 1

Which THREE of the following are valid GCS storage classes?

Accepted Answer

Nearline. Option B (Nearline) is a valid GCS storage class designed for data accessed less than once per month, offering lower storage cost with a 30-day minimum storage duration. Option D (Coldline) is also valid, intended for data accessed less than once per quarter, with a 90-day minimum storage duration and retrieval costs. Option E (Standard) is the default GCS storage class for frequently accessed ("hot") data with no minimum storage duration and the highest availability. Options A (Warmline) and C (Hotline) are not real GCS storage classes; they are fabricated names that do not correspond to any Google Cloud Storage offering.

Answer

Warmline

Answer

Hotline

Question 2

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.)

Accepted Answer

Create an external table in BigQuery that points to the Cloud Storage data.. 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.

Answer

Create a BigQuery dataset and use authorized views to restrict access to specific columns.

Answer

Enable uniform bucket-level access on the Cloud Storage bucket.

Answer

Set up a Dataplex lake and zone, and register the Cloud Storage bucket as an asset.

Question 3

You are using Cloud Scheduler to trigger a Cloud Workflows execution every hour. You need to ensure that the scheduler authenticates to the Workflows API securely. Which two of the following are valid methods for authenticating Cloud Scheduler to Workflows? (Choose two.)

Accepted Answer

Use an OAuth token generated for a service account that has the Workflows Invoker role.. Cloud Scheduler can authenticate to Workflows using either OIDC or OAuth tokens generated for a service account. The service account must have the Workflows Invoker role. These methods leverage Google Cloud IAM and avoid managing static secrets, making them secure and recommended. API keys and basic auth are not supported for the Workflows API.

Answer

Use an API key associated with the Workflows API and include it in the request header.

Answer

Use an OAuth 2.0 client ID and secret stored in Cloud Scheduler's configuration.

Answer

Use a static username and password stored in Secret Manager and referenced by Cloud Scheduler.

Question 4

Which THREE of the following are valid Airflow connection types in Cloud Composer?

Accepted Answer

HTTP. Option A (HTTP) is correct because Cloud Composer's Airflow connection types include 'HTTP', which stores host, schema, login, password, and port for calling REST endpoints via the HttpHook. Option D (Google Cloud) is correct because 'Google Cloud' is a built-in connection type used to store a GCP service account key or scopes, enabling operators like BigQueryInsertJobOperator and GCS hooks to authenticate. Option E (Postgres) is correct because 'Postgres' is a standard Airflow connection type that stores host, schema, login, password, and port for the PostgresHook and PostgresOperator. Option B (Kubernetes Pod) is not a connection type; KubernetesPodOperator uses a 'Kubernetes' connection (or in-cluster config), not 'Kubernetes Pod'. Option C (Python) is not a connection type; Python callables are executed via the PythonOperator, and no 'Python' connection exists in Airflow's connection type list.

Answer

Kubernetes Pod

Answer

Python

Question 5

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

Accepted Answer

The YAML rule configuration is malformed. 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.

Answer

The user has a slow internet connection

Answer

The data files were moved to another lake

Question 6

Which THREE of the following are Dataflow windowing types?

Accepted Answer

Fixed. In Google Cloud Dataflow (Apache Beam), the supported windowing types are fixed windows, sliding windows, and session windows, so options A, B, and D are correct. Fixed windows (A) divide a PCollection into non-overlapping, contiguous time intervals of a set duration, such as every 60 seconds. Session windows (B) group elements that arrive close together in time, separated by a gap duration of inactivity, which is useful for user-activity streams. Sliding windows (D) are defined by a window duration and a slide period, allowing windows to overlap, such as a 60-second window that slides every 30 seconds. Binary (C) and Random (E) are not Beam/Dataflow windowing types; no such windowing functions exist in the Dataflow model.

Answer

Binary

Answer

Random

Question 7

Which THREE steps are involved in an effective Dataflow pipeline development?

Accepted Answer

Deployment. The three correct steps are Deployment (A), Testing (B), and Development (D). Development (D) is where you author the pipeline code using the Apache Beam SDK, defining PTransforms, sources, and sinks before anything can be run. Testing (B) is essential to validate pipeline logic and correctness, typically via unit tests with TestPipeline and DoFnTester, and integration tests against representative data before production. Deployment (A) is the step of running the pipeline on a runner such as Dataflow, whether via the gcloud command, the Dataflow template, or a CI/CD job, to execute it at scale. Automated email alerts (C) are an operational monitoring concern, not a core pipeline development step, and manual data entry (E) is not part of building a Dataflow pipeline at all.

Answer

Automated email alerts

Answer

Manual data entry

Question 8

Which TWO of the following strategies help reduce Cloud Composer costs?

Accepted Answer

Delete unused environments. Option C (Delete unused environments) is correct because Cloud Composer charges for the underlying GKE nodes, Cloud SQL database, and web server that run continuously per environment, so removing environments that are no longer needed eliminates those recurring infrastructure costs. Option E (Enable auto-scaling) is correct because Composer's autoscaling dynamically adjusts the number of worker nodes based on the workload's queue demand, allowing the environment to scale down during idle periods and avoid paying for excess capacity. Option A (Keep all historical logs forever) is wrong because retaining logs indefinitely increases Cloud Logging storage costs rather than reducing them. Option B (Use the largest environment size) is wrong because larger environments provision more workers and thus cost more, not less. Option D (Increase scheduler frequency) is wrong because more frequent scheduler runs increase resource consumption and can raise costs instead of lowering them.

Answer

Keep all historical logs forever

Answer

Use the largest environment size

Answer

Increase scheduler frequency

Question 9

Which TWO are valid methods to trigger a Dataflow job?

Accepted Answer

Cloud Console. Option A (Cloud Console) is correct because Google Cloud Dataflow jobs can be created and launched directly from the Cloud Console UI, where you select a template or pipeline and configure parameters before clicking Run. Option C (Cloud Scheduler) is correct because Cloud Scheduler can send an HTTP request to the Dataflow REST API (or trigger a Cloud Function that calls it) to start a Dataflow job on a cron schedule, making it a valid programmatic trigger method. Option B (Direct keyboard input) is not a valid trigger mechanism for Dataflow jobs, as Dataflow has no such input channel. Option D (GCS bucket browsing) is not a trigger method; browsing objects in Cloud Storage does not launch a Dataflow job. Option E (Cloud DNS lookup) is unrelated to Dataflow, as Cloud DNS resolves domain names and cannot start data processing jobs.

Answer

Direct keyboard input

Answer

GCS bucket browsing

Answer

Cloud DNS lookup

Question 10

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

Accepted Answer

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

Answer

Assets can be created without a storage resource

Answer

Assets can only be BigQuery tables

Answer

Assets duplicate the physical data

Question 11

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

Accepted Answer

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

Answer

Use IP-based firewalls for all datasets

Answer

Assign roles to individual files

Question 12

Which THREE of the following are necessary for a production-ready Dataflow pipeline?

Accepted Answer

Error handling. Error handling (B) is necessary because a production Dataflow pipeline must gracefully manage failures such as malformed input records, transient network issues, or failed side inputs, typically via try/except blocks, dead-letter queues, or retry policies, so that a single bad element does not halt the entire streaming or batch job. Logging (C) is required to capture runtime events, warnings, and errors through tools like Cloud Logging (Stackdriver), enabling operators to diagnose issues, audit data flow, and trace pipeline behavior across workers. Monitoring (E) is essential to observe pipeline health and performance metrics such as system lag, throughput, watermark progression, and worker CPU/memory in Cloud Monitoring, allowing alerting and timely intervention before SLA breaches. The unmarked options do not belong: desktop-based processing (A) is irrelevant because Dataflow is a fully managed, cloud-based service that runs on Google Cloud infrastructure, not on a local desktop, and manual refreshes (D) contradict the automation and continuous operation expected of a production pipeline, which should run on schedules or streaming triggers without human intervention.

Answer

Desktop-based processing

Answer

Manual refreshes

Question 13

You are creating a Looker Studio report that must allow users to interactively explore sales data. The report should include controls that let users filter data by product category and date range. Which two controls should you add? (Choose two.)

Accepted Answer

A date range control.. A filter control on the product category dimension and a date range control are the standard Looker Studio controls for interactive filtering by category and time. They apply to all charts on the page, enabling users to dynamically explore sales data. The other options either do not provide filtering or are not interactive.

Answer

A parameter control to adjust the date granularity.

Answer

A chart-level filter on each chart for product category.

Answer

A data control to switch between different data sources.

Question 14

A data team is building a BigQuery ML model to predict customer churn directly from a BigQuery table. They want to evaluate the model's quality and understand which input features influenced its predictions. Which two BigQuery ML capabilities should they use to accomplish these goals? (Choose two.)

Accepted Answer

Use ML.EXPLAIN_PREDICT to return per-row feature attributions alongside each prediction.. ML.EVALUATE measures the churn model's predictive quality with classification metrics on held-out data, while ML.EXPLAIN_PREDICT returns per-row feature attributions that reveal which inputs drove each prediction. Together they cover both evaluation and interpretability. The other functions describe training data, training progress, or generate embeddings, none of which answers how good the model is or why it predicts churn.

Answer

Use ML.TRAINING_INFO to inspect the iteration history of the model's training run.

Answer

Use ML.GENERATE_EMBEDDING to convert customer records into vector embeddings for similarity search.

Answer

Use ML.FEATURE_INFO to list the summary statistics of each feature column used during training.

Question 15

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

Accepted Answer

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

Answer

A local database client like DBeaver

Answer

Text editor for configuration files

Answer

A standard SSH client

Question 16

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

Accepted Answer

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

Answer

Users must provide a password for each asset

Answer

Dataplex ignores existing storage permissions

Answer

Dataplex provides its own authentication system

Question 17

Which THREE of the following are common steps in a typical data pipeline orchestration pattern?

Accepted Answer

Validation. In a typical data pipeline orchestration pattern, data ingestion (C) is a core step because it moves raw data from sources such as databases, APIs, or files into the pipeline for processing. Transformation (D) is also essential, as it cleanses, enriches, aggregates, or otherwise converts ingested data into a usable format for analytics or downstream systems. Validation (A) is a common pipeline step that checks data for schema conformance, completeness, quality, and business rules before or after transformation to prevent bad data from propagating. Network cable installation (B) and hardware provisioning (E) are infrastructure or physical-layer tasks, not orchestration steps within a data pipeline workflow, so they are not correct.

Answer

Network cable installation

Answer

Hardware provisioning

Question 18

A data engineer is building a Looker Studio report connected to a BigQuery table. The report must allow viewers to dynamically change the date range and filter data by region without editing the report. The engineer wants to add interactive controls. Which two components should be added to the report to meet these requirements? (Choose two.)

Accepted Answer

Drop-down list control. To enable viewers to change the date range and filter by region without editing the report, interactive controls are needed. A date range control provides a calendar picker for date filtering, and a drop-down list control allows selection of dimension values like region. Calculated fields, scorecards, and data blending are not interactive controls and do not fulfill the dynamic filtering requirements.

Answer

Calculated field

Answer

Scorecard with conditional formatting

Answer

Data blending

Question 19

You are building a Looker Studio report connected to a BigQuery table that contains billions of rows. The report includes several charts and filters. Users report that the report is slow to load and that changing a filter takes a long time. Which TWO actions should you take to improve performance? (Choose two.)

Accepted Answer

Enable BigQuery BI Engine and reserve memory for the dataset.. For large BigQuery datasets, slow Looker Studio performance is best addressed by reducing query latency and data scanned. BigQuery BI Engine provides in-memory acceleration for interactive queries, while a materialized view pre-aggregates data to minimize processing. Both directly improve chart load and filter responsiveness. Other options either do not address query speed or are impractical for billions of rows.

Answer

Increase the data freshness setting to 24 hours to reduce the number of queries.

Answer

Extract the data into a Looker Studio extract and schedule daily refreshes.

Answer

Add more filters to the report to narrow down the data before charts load.

Question 20

A data practitioner is building a Looker Studio report connected to a BigQuery table that contains millions of rows. The report currently returns an error indicating the query result exceeds the row limit, and stakeholders also complain that the charts take too long to render. Which two actions should the practitioner take to address these issues? (Choose two.)

Accepted Answer

Enable the 'Extract data' option on the data source so a snapshot of the data is cached and reused by the report.. Both symptoms trace back to volume: too many rows for the connector and too much work per page render. Aggregating in BigQuery before the data reaches Looker Studio reduces the row count under the connector limit and lightens each query. Caching that reduced result as an extract removes repeated warehouse round trips, so charts render from local snapshot data. Together they fix the error and the latency without changing the report's meaning.

Answer

Switch the data source to the Google Sheets connector and import the BigQuery results into a spreadsheet first.

Answer

Increase the number of rows returned by editing the BigQuery connector's maximum row setting in the data source configuration.

Answer

Add more chart types to the report page so Looker Studio distributes the query load across multiple visualizations.