Google Associate Data Practitioner Practice Exams
Last updated on Oct 22,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 22,2026
Question #100
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?
- A . Create a dual-region bucket, and upload the files to this bucket.
- B . Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
- C . Create a multi-region bucket, and upload the files to this bucket.
- D . Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed In-Depth
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.
Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.
Option D: Two single-region buckets with Storage Transfer Service automate replication but aren’t synchronous (batch-based), reducing HA compared to dual-region’s real-time sync.
Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google’s HA recommendations. Extract from Google Documentation: From "Cloud Storage Bucket Locations"
(https://cloud.google.com/storage/docs/locations): "Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations."
Reference: Google Cloud Documentation – "Cloud Storage Dual-Region"
(https://cloud.google.com/storage/docs/locations#dual-region).
A
Explanation:
Comprehensive and Detailed In-Depth
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.
Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.
Option D: Two single-region buckets with Storage Transfer Service automate replication but aren’t synchronous (batch-based), reducing HA compared to dual-region’s real-time sync.
Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google’s HA recommendations. Extract from Google Documentation: From "Cloud Storage Bucket Locations"
(https://cloud.google.com/storage/docs/locations): "Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations."
Reference: Google Cloud Documentation – "Cloud Storage Dual-Region"
(https://cloud.google.com/storage/docs/locations#dual-region).
Question #100
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?
- A . Create a dual-region bucket, and upload the files to this bucket.
- B . Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
- C . Create a multi-region bucket, and upload the files to this bucket.
- D . Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed In-Depth
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.
Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.
Option D: Two single-region buckets with Storage Transfer Service automate replication but aren’t synchronous (batch-based), reducing HA compared to dual-region’s real-time sync.
Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google’s HA recommendations. Extract from Google Documentation: From "Cloud Storage Bucket Locations"
(https://cloud.google.com/storage/docs/locations): "Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations."
Reference: Google Cloud Documentation – "Cloud Storage Dual-Region"
(https://cloud.google.com/storage/docs/locations#dual-region).
A
Explanation:
Comprehensive and Detailed In-Depth
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.
Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.
Option D: Two single-region buckets with Storage Transfer Service automate replication but aren’t synchronous (batch-based), reducing HA compared to dual-region’s real-time sync.
Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google’s HA recommendations. Extract from Google Documentation: From "Cloud Storage Bucket Locations"
(https://cloud.google.com/storage/docs/locations): "Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations."
Reference: Google Cloud Documentation – "Cloud Storage Dual-Region"
(https://cloud.google.com/storage/docs/locations#dual-region).
Question #103
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations.
What should you do?
- A . Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
- B . Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
- C . Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.
- D . Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
Correct Answer: D
D
Explanation:
Comprehensive and Detailed in Depth
Why D is correct:Synchronous replication ensures that data is written to both the primary and secondary instances at the same time, minimizing data loss (RPO).
Regional availability (HA) within different zones provides redundancy within the same region, minimizing latency.
Why other options are incorrect:A: Asynchronous replication has a potential for data loss.
B: Multiregion backups are for disaster recovery, not minimizing latency.
C: Hourly backups do not provide the lowest possible RPO.
Reference: Cloud SQL high availability: https://cloud.google.com/sql/docs/postgres/high-availability
Cloud SQL backups: https://cloud.google.com/sql/docs/postgres/backup-restore
D
Explanation:
Comprehensive and Detailed in Depth
Why D is correct:Synchronous replication ensures that data is written to both the primary and secondary instances at the same time, minimizing data loss (RPO).
Regional availability (HA) within different zones provides redundancy within the same region, minimizing latency.
Why other options are incorrect:A: Asynchronous replication has a potential for data loss.
B: Multiregion backups are for disaster recovery, not minimizing latency.
C: Hourly backups do not provide the lowest possible RPO.
Reference: Cloud SQL high availability: https://cloud.google.com/sql/docs/postgres/high-availability
Cloud SQL backups: https://cloud.google.com/sql/docs/postgres/backup-restore
Question #104
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery.
Which data manipulation methodology should you choose?
- A . EL
- B . ELT
- C . ETL
- D . ETLT
Correct Answer: C
C
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
C
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
Question #105
Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis.
What should you recommend?
- A . Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.
- B . Create a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.
- C . Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.
- D . Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your
team. Analyze the data and generate visualizations in Gooqle Sheets.
Correct Answer: A
A
Explanation:
Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.
A
Explanation:
Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.
Question #106
Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly.
What should you do?
- A . Store the data in Cloud Storaqe usinq Nearline storaqe.
- B . Store the data in Cloud Storaqe usinq Coldline storaqe.
- C . Store the data in Cloud Storage using Standard storage.
- D . Store the data in Cloud Storage using Archive storage.
Correct Answer: A
A
Explanation:
Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.
A
Explanation:
Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.
Question #107
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach.
What should you do?
- A . Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.
- B . Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
- C . Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
- D . Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
Correct Answer: C
C
Explanation:
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud’s ecosystem for efficient data summarization and storage.
C
Explanation:
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud’s ecosystem for efficient data summarization and storage.
Question #108
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label.
Which BigQuery ML query should you use?
- A . CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype=’logisric_reg’) AS SELECT * from cusromer_data;
- B . CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=’ logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
- C . CREATE OR REPLACE MODEL churn_prediction_model options (model type=’logistic_reg’) AS select churned as label FROM customer_data;
- D . CREATE OR REPLACE MODEL churn_prediction_model options(model_type=’logistic_reg*) as select ’ except(churned) FROM customer data;
Correct Answer: B
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:BigQuery ML requires the target label to be explicitly named label.
EXCEPT (churned) selects all columns except the churned column, which becomes the features.
churned AS label renames the churned column to label, which is required for BigQuery ML.
logistic_reg is the correct model_type option.
Why other options are incorrect:A: Does not rename the target column to label. Also has a typo in the model type.
C: Only selects the target label, not the features.
D: Has a syntax error with the single quote before except.
Reference: BigQuery ML Logistic Regression: https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-logistic-regression
BigQuery ML Syntax: https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:BigQuery ML requires the target label to be explicitly named label.
EXCEPT (churned) selects all columns except the churned column, which becomes the features.
churned AS label renames the churned column to label, which is required for BigQuery ML.
logistic_reg is the correct model_type option.
Why other options are incorrect:A: Does not rename the target column to label. Also has a typo in the model type.
C: Only selects the target label, not the features.
D: Has a syntax error with the single quote before except.
Reference: BigQuery ML Logistic Regression: https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-logistic-regression
BigQuery ML Syntax: https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create
Question #109
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface.
What should you do?
- A . Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
- B . Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
- C . Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
- D . Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
Correct Answer: A
A
Explanation:
Pushing event information to a Pub/Sub topic and then creating a Dataflow job using the Dataflow job builder is the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations is A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here’s why:
Pub/Sub and Dataflow:
Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
Dataflow is built for real time streaming and applying transformations.
Let’s break down why the other options are less suitable:
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a
BigQuery scheduled job that executes once each day to apply transformations:
This is a batch processing approach, not real-time.
Cloud Storage and scheduled jobs are not designed for near real-time analysis.
This does not meet the real time requirement of the question.
C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the
Pub/Sub topic, apply transformations, and insert the data into BigQuery:
While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
Cloud run does not provide a visual interface.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.
A
Explanation:
Pushing event information to a Pub/Sub topic and then creating a Dataflow job using the Dataflow job builder is the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations is A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here’s why:
Pub/Sub and Dataflow:
Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
Dataflow is built for real time streaming and applying transformations.
Let’s break down why the other options are less suitable:
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a
BigQuery scheduled job that executes once each day to apply transformations:
This is a batch processing approach, not real-time.
Cloud Storage and scheduled jobs are not designed for near real-time analysis.
This does not meet the real time requirement of the question.
C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the
Pub/Sub topic, apply transformations, and insert the data into BigQuery:
While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
Cloud run does not provide a visual interface.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.
Question #110
You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage.
What should you do?
- A . Set up a Cloud CDN in front of the bucket.
- B . Enable Object Versioning on the bucket.
- C . Store the data in a multi-region bucket.
- D . Store the data in Nearline storaqe.
Correct Answer: C
C
Explanation:
Storing the data in a multi-region bucket ensures high availability and durability, even in the event of
a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.
A single-zone outage requires high availability across zones or regions.
Cloud Storage offers location-based redundancy options:
Option A: Cloud CDN caches content for web delivery but doesn’t protect against underlying storage outages―it’s for performance, not availability of the source data.
Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn’t ensure availability during a zone failure (still tied to one location).
Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.
Option D: Nearline storage is a low-cost class with lower availability (single-region by default), not designed for outage resilience.
Why C is Best: Multi-region storage offers geo-redundancy, guaranteeing data access during outages with no additional configuration. It’s the simplest, most reliable choice for pipeline continuity.
Extract from Google Documentation: From "Cloud Storage Bucket Locations"
(https://cloud.google.com/storage/docs/locations): "Multi-region buckets replicate data across
multiple regions within a geography, providing high availability and durability, ensuring access even if a single zone or region experiences an outage."
Reference: Google Cloud Documentation – "Cloud Storage Locations"
(https://cloud.google.com/storage/docs/locations).
C
Explanation:
Storing the data in a multi-region bucket ensures high availability and durability, even in the event of
a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.
A single-zone outage requires high availability across zones or regions.
Cloud Storage offers location-based redundancy options:
Option A: Cloud CDN caches content for web delivery but doesn’t protect against underlying storage outages―it’s for performance, not availability of the source data.
Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn’t ensure availability during a zone failure (still tied to one location).
Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.
Option D: Nearline storage is a low-cost class with lower availability (single-region by default), not designed for outage resilience.
Why C is Best: Multi-region storage offers geo-redundancy, guaranteeing data access during outages with no additional configuration. It’s the simplest, most reliable choice for pipeline continuity.
Extract from Google Documentation: From "Cloud Storage Bucket Locations"
(https://cloud.google.com/storage/docs/locations): "Multi-region buckets replicate data across
multiple regions within a geography, providing high availability and durability, ensuring access even if a single zone or region experiences an outage."
Reference: Google Cloud Documentation – "Cloud Storage Locations"
(https://cloud.google.com/storage/docs/locations).