Google Associate Data Practitioner Exam Dumps & Practice Test Questions

Question 1:

Your team is developing a near real-time data processing system to handle JSON telemetry from connected appliances. Each message is published to a Pub/Sub topic and contains a serial number field that must be capitalized before storing the data in BigQuery. The solution should use a managed service to minimize custom coding and support low-latency streaming. 

What is the most efficient and scalable approach to implement this pipeline?

A. Use a Pub/Sub to BigQuery subscription, store the data in BigQuery, and schedule a transformation query to run every five minutes.
B. Send data from Pub/Sub to Cloud Storage, then trigger a Cloud Run service for transformation and insert the results into BigQuery.
C. Use the “Pub/Sub to BigQuery” Dataflow template with a UDF to capitalize the serial number and write directly to BigQuery.
D. Use a Pub/Sub push subscription to trigger a Cloud Run service that processes and writes the transformed data to BigQuery.

Correct Answer: C

Explanation:

The optimal solution for building a real-time telemetry data pipeline that minimizes code complexity and leverages Google Cloud's managed services is C, using the "Pub/Sub to BigQuery" Dataflow template enhanced with a User Defined Function (UDF) for transformation.

Dataflow is a fully managed streaming analytics service built on Apache Beam. It allows you to process real-time data efficiently and apply transformations on the fly. The “Pub/Sub to BigQuery” template is a pre-built pipeline that simplifies the ingestion of Pub/Sub messages directly into BigQuery. By integrating a UDF, you can embed lightweight transformations like capitalizing the serial number field without writing and deploying custom pipeline code. This approach allows the system to remain highly responsive and scalable.

Let’s evaluate the other choices:

Option A involves directly inserting data into BigQuery from Pub/Sub and using scheduled queries to transform the serial number. This method lacks real-time transformation capabilities, introduces delay, and results in raw data being stored without immediate preprocessing. It’s not suitable for use cases that require instant insights.

Option B suggests storing the data in Cloud Storage, then invoking a Cloud Run service. This approach increases complexity by introducing unnecessary intermediate storage and processing steps. It requires maintaining a Cloud Run instance, storage bucket triggers, and transformation logic, which goes against the goal of reducing custom code.

Option D also relies on Cloud Run triggered via a push subscription. While this setup is workable, it again places the burden of custom code, deployment, and scaling on your team. It’s less streamlined than using Dataflow, which is purpose-built for streaming transformations.

In contrast, Option C utilizes a robust, pre-configured pipeline with minimal setup, real-time capabilities, and built-in scalability. With UDF support, transformation is both efficient and declarative, eliminating the need for complex service orchestration.

Thus, Option C is the best choice for meeting the real-time, low-maintenance requirements of this use case.

Question 2:

You receive a CSV file containing daily sales data that is stored in Cloud Storage. This file must be transformed before it is loaded into BigQuery, and it’s also important to capture any data quality issues during processing. The solution must be scalable and quick to implement. 

What is the best approach to design this data ingestion and transformation pipeline?

A. Build a batch pipeline in Cloud Data Fusion using Cloud Storage as the source and BigQuery as the destination.
B. Load the raw CSV into BigQuery and use scheduled queries to handle the necessary transformations.
C. First import the CSV into BigQuery, then create a batch pipeline in Cloud Data Fusion using BigQuery as both the source and sink.
D. Create a batch processing pipeline in Dataflow using the Cloud Storage to BigQuery batch template.

Correct Answer: A

Explanation:

The most efficient and scalable solution for processing and transforming a daily CSV file from Cloud Storage into BigQuery—while also inspecting data quality—is A, using Cloud Data Fusion to build a batch pipeline with Cloud Storage as the source and BigQuery as the sink.

Cloud Data Fusion is a fully managed, code-free data integration service that excels in designing, building, and managing data pipelines. It supports a wide range of transformation plugins and connectors, making it ideal for batch workflows like this. Using Cloud Data Fusion, you can easily configure the pipeline to extract data from a Cloud Storage bucket, perform transformations (e.g., data type casting, filtering, enrichment), and then write the cleaned and structured output directly into BigQuery.

A major advantage of Data Fusion is its data wrangling and quality control capabilities. You can add stages in the pipeline that monitor for missing values, outliers, or formatting errors, allowing you to surface potential data quality issues before loading the data into BigQuery.

Now let’s consider the other options:

Option B proposes loading the raw CSV file into BigQuery first, then using scheduled SQL queries to apply transformations. This approach is less desirable because it allows bad data into BigQuery, increases storage costs, and may expose downstream processes to inconsistent data. Also, managing complex transformations purely in SQL can become cumbersome and harder to maintain.

Option C involves using BigQuery as both the source and sink for a Data Fusion pipeline. This is inefficient and counterintuitive. The goal is to clean the data before it reaches BigQuery, not after. Using BigQuery in this way adds an unnecessary step and doesn’t address the requirement for upfront quality inspection.

Option D, which leverages a Dataflow batch template, is another technically valid choice. However, Dataflow typically requires more effort to set up and customize, particularly when transformations and data quality validations are needed. It’s more suitable for advanced or highly customized batch jobs, not quick deployments.

In conclusion, Option A offers a highly efficient, low-code, and scalable approach to ingest, transform, and validate CSV data before loading it into BigQuery—making it the most appropriate solution.

Question 3:

You oversee a Cloud Storage bucket that holds temporary files used during data processing workflows. Since these files are only required for a short period—specifically, seven days—you want to automate their deletion to reduce costs and maintain storage efficiency. 

What is the most effective way to handle this?

A. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
B. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D. Create a Cloud Run function that runs daily and deletes files older than seven days.

Correct Answer: B

Explanation:

The most straightforward and efficient way to manage object expiration in Cloud Storage is by using lifecycle management rules. Google Cloud Storage supports native lifecycle configurations, which allow you to automatically delete or transition objects based on specific conditions such as object age, creation date, or storage class.

By configuring a lifecycle rule to delete objects older than seven days, you ensure an automated and serverless cleanup strategy that doesn't require any code, maintenance, or scheduling infrastructure. This declarative approach directly integrates with Cloud Storage’s internal systems, making it the most scalable and cost-effective solution.

Choosing lifecycle rules ensures:

• Automation: Files are deleted automatically without manual intervention.

• Efficiency: There’s no need to poll the bucket or maintain any execution logic.

• No additional infrastructure: No need for Cloud Scheduler, Cloud Run, or Dataflow pipelines.

Why the other options are not ideal:

• Option A introduces unnecessary complexity by involving both Cloud Scheduler and Cloud Run. It also depends on a weekly schedule, which may result in files lingering longer than needed.

• Option C is overkill. Dataflow is meant for large-scale data transformation, not simple deletion tasks. It requires development, deployment, and maintenance.

• Option D simplifies the schedule to daily, but still requires building and maintaining a Cloud Run function, as well as tracking file metadata manually.

For a native, zero-maintenance solution, lifecycle rules offer the perfect balance of power and simplicity.

Question 4:

You are working for a healthcare provider that maintains an on-premises system storing sensitive patient data, including personally identifiable information (PII). As you begin migrating this data into Google Cloud, you need a consistent and secure method to de-identify PII from all incoming data feeds. 

Which approach best satisfies this requirement?

A. Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.
B. Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.
C. Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.
D. Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

Correct Answer: B

Explanation:

Cloud Data Fusion is the ideal solution for this scenario because it is a fully managed, visual ETL service that enables scalable and repeatable data transformations. It supports built-in de-identification and transformation plugins, allowing you to remove or mask PII fields before ingesting the data into analytical tools like BigQuery.

In the healthcare context, especially with compliance mandates like HIPAA, a tool that ensures consistent, traceable, and well-documented data transformations is essential. Cloud Data Fusion provides a GUI-based development environment where users can visually design pipelines, apply transformations, and manage data lineage—all without writing extensive code.

Additionally, Data Fusion integrates natively with Google Cloud services, making it easy to ingest from on-prem systems and output to BigQuery or Cloud Storage while ensuring that all necessary de-identification steps are followed.

Why the other options fall short:

• Option A (Cloud Run) involves custom code and orchestration logic, making the system harder to maintain and test, especially for consistent and regulated transformations like PII masking.

• Option C delays de-identification until after ingestion into BigQuery, which violates best practices by exposing raw PII data in the cloud before cleaning.

• Option D using Apache Beam is technically viable but unnecessarily complex. It requires developers to write and maintain data transformation logic in Java or Python, which is not ideal for teams needing low-code or standardized solutions.

Thus, Cloud Data Fusion (Option B) offers the best mix of compliance readiness, usability, and scalability for securely transforming sensitive healthcare data before cloud ingestion.

Question 5:

You are responsible for managing a vast amount of data in Google Cloud Storage, including raw, processed, and backup files. Due to regulatory compliance, some categories of this data must remain unchanged for predetermined periods. At the same time, you aim to reduce storage expenses while maintaining compliance with immutability rules. 

What strategy best fulfills both cost optimization and data immutability requirements?

A. Apply lifecycle rules to shift objects between storage classes depending on access frequency and enable Object Versioning for immutability.

B. Reassign storage classes based on data age and access trends, and encrypt objects using Cloud KMS with CMEK to meet retention policies.

C. Deploy a Cloud Run function to examine metadata regularly, then move objects and enforce immutability using object holds as needed.

D. Enforce object immutability using object holds, and use lifecycle management rules to move data based on access patterns and age.

Correct Answer: D

Explanation:

When dealing with large-scale cloud storage, especially in environments where data must be immutable due to compliance policies, it's essential to use native features that support both legal data retention and storage cost reduction. Option D is the most effective solution because it leverages two built-in Google Cloud features: object holds for ensuring immutability and lifecycle management for automated cost optimization.

Object holds are specifically designed to prevent deletion or modification of data for a specified duration. They come in two types—event-based and temporary holds. These are ideal for enforcing immutability, ensuring that the data remains untouched, and satisfying legal and regulatory requirements. Unlike Object Versioning, object holds prevent changes entirely rather than simply keeping track of previous versions, making them the more robust option for strict compliance needs.

Lifecycle management rules allow automated transitions of data based on object age, access frequency, or custom metadata. For instance, older or infrequently accessed data can be moved to more economical storage classes such as Nearline, Coldline, or Archive, significantly lowering storage costs without manual intervention. You retain the ability to access the data as needed, but the storage cost per GB is much cheaper.

Option A is not sufficient because Object Versioning only maintains a history of object versions but doesn’t enforce strict immutability. This means users could still overwrite or delete current versions unless other controls are implemented.

Option B incorrectly assumes that Cloud KMS with CMEK provides immutability. While it offers enhanced encryption and key control, it does not prevent data from being altered or deleted and doesn’t address cost optimization.

Option C is overly complex. While technically feasible, building a custom Cloud Run function introduces unnecessary operational overhead. Google Cloud's built-in features like object holds and lifecycle rules achieve the same result more efficiently and with less maintenance.

In summary, Option D best balances regulatory requirements and storage optimization using Google Cloud’s native capabilities, making it the most practical and compliant solution.

Question 6:

You work at an eCommerce firm that stores customer behavior data—such as purchases, demographic profiles, and website engagement—in BigQuery. You are tasked with building a machine learning model to predict which customers are likely to purchase in the coming month. 

Given the team’s limited machine learning experience and engineering capacity, which solution offers the most efficient path to implementing this prediction model?

A. Use BigQuery ML to train a logistic regression model for the prediction task.

B. Create a custom model using Vertex AI Workbench.

C. Build a model using Colab Enterprise.

D. Export data to Cloud Storage and use AutoML Tables to create a classification model.

Correct Answer: A

Explanation:

When faced with limited resources and expertise in machine learning, the best strategy is to leverage a platform that integrates closely with your existing data infrastructure and requires minimal custom development. Option A, using BigQuery ML to train a logistic regression model, is the most appropriate solution for this use case.

BigQuery ML allows data analysts and engineers to build and deploy ML models directly within BigQuery using standard SQL queries. This eliminates the need for data movement, external tools, or custom model deployment pipelines. Since your dataset already resides in BigQuery, you can train, validate, and serve a prediction model entirely within the platform. This drastically reduces development time and complexity.

Logistic regression is a solid baseline model for binary classification tasks such as predicting whether a customer will make a purchase. It’s simple to implement, interpretable, and performs well with structured data like customer demographics and transaction histories.

Option B, Vertex AI Workbench, is powerful and flexible but is more suitable for advanced ML use cases. It requires knowledge of programming languages like Python and frameworks like TensorFlow or PyTorch. For a team with limited ML experience, it introduces unnecessary complexity and operational overhead.

Option C, Colab Enterprise, is better suited for experimentation rather than production-ready ML workflows. It also demands a higher degree of manual scripting, setup, and maintenance, which goes against the requirement to minimize technical complexity.

Option D, exporting data to Cloud Storage and using AutoML Tables, is a no-code or low-code solution that automates many ML steps. However, it introduces data export and additional data management burdens, potentially increasing costs and duplicating data unnecessarily. This method is less efficient when the data already resides in BigQuery and could instead be directly used with BigQuery ML.

In conclusion, BigQuery ML is purpose-built for teams with limited ML expertise, offering a streamlined and cost-effective way to perform predictive analytics directly within your existing data warehouse using familiar SQL syntax. This makes Option A the most strategic and practical choice for your prediction project.

Question 7:

You're tasked with designing a data pipeline that begins processing as soon as files are deposited into Cloud Storage by 3:00 AM each day. The pipeline executes multiple sequential stages, where each one depends on the output of the previous stage. Sometimes, specific stages are time-consuming or might fail due to errors. When such failures occur, you must quickly identify and resolve the issue, then resume processing with minimal delay. 

Which solution offers the best combination of speed and error management to ensure timely generation of the final output?

A. Use Dataproc to run a Spark program that pauses and waits for user input upon encountering an error, allowing manual reruns after fixing the data.
B. Construct the pipeline using Dataflow's PTransforms and restart it entirely once stage-level issues are corrected.
C. Implement the process using Cloud Workflows, allowing the flow to resume at specific stages via input parameters after a failure.
D. Create the workflow as a directed acyclic graph (DAG) in Cloud Composer and reset the failed task state once the issue is resolved.

Answer: D

Explanation:

In complex data pipelines with interdependent processing stages, efficiency and resiliency are critical—especially when handling failures. Cloud Composer, built on Apache Airflow, is designed for exactly this type of use case. It allows the creation of Directed Acyclic Graphs (DAGs), which represent workflows with clearly defined task dependencies and execution order.

What makes Option D the most suitable solution is that Cloud Composer allows granular control over task execution and recovery. If a particular stage fails, the pipeline doesn’t need to be restarted from the beginning. Instead, you can simply clear the failed task’s state and re-run just that task or its downstream dependencies after addressing the issue. This dramatically reduces recovery time and ensures efficient resource usage.

In contrast, Option A uses Dataproc with Spark, which might be suitable for batch processing, but manually pausing for user input is inefficient. Automation is the goal in a production pipeline, and human intervention introduces delays and potential for inconsistency.

Option B suggests using Dataflow. While Dataflow is excellent for both streaming and batch data, it lacks the same level of stage-by-stage recovery control. Restarting the pipeline after fixing a single stage is not optimal—this results in reprocessing the entire dataset, consuming more time and compute resources than necessary.

Option C, which involves Cloud Workflows, provides some orchestration capabilities but lacks the robust task dependency and error-handling mechanisms of DAG-based tools like Composer. Jumping to specific workflow stages using input parameters is cumbersome and error-prone, especially in multi-stage pipelines where dependency tracking and conditional execution are crucial.

To summarize, Cloud Composer with DAG-based task orchestration stands out for its flexibility, fault-tolerant design, and efficient task retry logic. It ensures minimal disruption, keeps recovery times short, and maximizes throughput by resuming only the affected tasks instead of restarting everything. This makes it the ideal choice for maintaining a fast, reliable, and maintainable data pipeline.

Question 8:

Another internal team needs access to data stored in a BigQuery dataset. You want to grant them access while ensuring that the risk of the dataset being copied or misused is minimized. Additionally, you want to implement a repeatable and secure method to share data with future teams. 

Which approach provides the most secure and scalable solution?

A. Share data using authorized views created in the team's project and limit access to those views.
B. Use Analytics Hub to create a private exchange with egress restrictions, then grant the team access.
C. Apply domain-restricted sharing and grant the BigQuery Data Viewer role directly on the dataset.
D. Export the dataset to a Cloud Storage bucket managed by the team and restrict access to that bucket.

Answer: A

Explanation:

When sharing sensitive data in BigQuery, the priority should always be to control who can see what and to minimize data duplication or unauthorized usage. The most effective and secure method that supports future reuse and scalability is to leverage authorized views.

Option A is ideal because authorized views allow you to define a SQL-based abstraction layer over the data. These views can be placed in the requesting team’s project, and you can limit access to just the view rather than the full dataset. This strategy ensures that the team only sees specific fields or filtered records, greatly reducing the risk of overexposure. Furthermore, the data remains in its original location—no physical duplication is necessary—making it easier to maintain and audit.

Authorized views also serve as reusable templates, allowing administrators to replicate the same security model for other teams in the future. You can even implement row-level and column-level security policies in conjunction with the views, adding another layer of protection. Because all access is governed by IAM permissions, it becomes simple to update, revoke, or audit access as organizational needs change.

Option B proposes using Analytics Hub, which is more appropriate for external or cross-organization data sharing. While it supports features like data egress restrictions, the setup can be complex and is generally better suited for broad-scale data exchanges. For internal use cases, it introduces unnecessary complexity compared to authorized views.

Option C, which applies domain-restricted sharing combined with the BigQuery Data Viewer role, gives users read-only access to the full dataset. This opens the door for users to export the entire dataset, increasing the risk of unauthorized copying. It lacks the fine-grained control that authorized views offer and isn’t ideal when data access needs to be tightly scoped.

Option D involves exporting data to Cloud Storage, which not only creates a duplicate copy of the data but also introduces potential challenges in access control, auditing, and versioning. It’s harder to manage and doesn’t scale well if multiple teams require access to different parts of the dataset.

In conclusion, Option A—using authorized views—is the most secure, efficient, and scalable solution for controlled internal data sharing in BigQuery. It supports precise data filtering, minimizes duplication, and enables future extensibility with ease.

Question 9:

You need to analyze customer transaction data stored in a Google Cloud Storage bucket using SQL. Which of the following services should you use to accomplish this efficiently without moving the data?

A. Cloud SQL
B. Dataproc
C. BigQuery
D. Cloud Spanner

Correct Answer: C

Explanation:

The correct answer is BigQuery because it allows you to run SQL queries directly on data stored in external sources, including Google Cloud Storage (GCS), without having to move or duplicate that data. This feature is known as BigQuery federated queries.

When working with semi-structured or structured data files (such as CSV, JSON, Avro, Parquet, or ORC) in GCS, BigQuery provides a seamless way to analyze them using standard SQL syntax. You can define an external table in BigQuery that references the files in Cloud Storage and then perform queries just like you would on a native table.

Now let's examine why the other options are incorrect:

• A. Cloud SQL is a managed relational database (like MySQL or PostgreSQL) on Google Cloud. It is not designed for querying files in Cloud Storage directly. You would need to ingest the data into Cloud SQL first, which is inefficient for ad hoc analysis.

• B. Dataproc is Google Cloud’s managed Spark and Hadoop service. While you could use Dataproc to analyze data in Cloud Storage, it involves more complexity and infrastructure setup than BigQuery. It’s not as efficient or straightforward for simple SQL-based queries.

• D. Cloud Spanner is a globally distributed relational database designed for transactional workloads. It is not suitable for querying files in GCS or running ad hoc analytical queries.

BigQuery is optimized for serverless, petabyte-scale analytics, which means you don’t need to manage infrastructure, and you pay only for the data you query. It also integrates well with GCS, making it the go-to solution for analyzing large datasets stored in object storage.

For the exam, it's essential to recognize BigQuery as the preferred tool for large-scale analytics and SQL-based querying of both native and external datasets in Google Cloud.

Question 10:

A data analyst wants to automate a workflow to ingest streaming data from IoT sensors and analyze it in near real time. Which combination of Google Cloud services is most appropriate for this use case?

A. Cloud Pub/Sub + Dataflow + BigQuery
B. Cloud Functions + Cloud Storage + Dataproc
C. App Engine + Cloud SQL + Looker
D. Cloud Run + Vertex AI + Firestore

Correct Answer: A

Explanation:

The correct answer is A: Cloud Pub/Sub + Dataflow + BigQuery, which represents a standard architecture for real-time or near real-time data processing pipelines in Google Cloud.

Let’s break it down:

• Cloud Pub/Sub is a scalable, globally distributed messaging service. It is commonly used to ingest streaming data from sources like IoT devices, applications, and logs. It captures and queues the data as soon as it arrives.

• Dataflow is Google’s fully managed data processing service that supports both batch and real-time (streaming) pipelines using Apache Beam. It is ideal for transforming and enriching streaming data as it flows through the pipeline.

• BigQuery is used to store and analyze the processed data. Once Dataflow writes the output to BigQuery, you can immediately run SQL queries on it or build visualizations in tools like Looker Studio.

Now, let’s evaluate the other options:

• B. Cloud Functions + Cloud Storage + Dataproc: This combination is more suitable for event-driven architectures and batch processing. It does not offer native support for real-time streaming analytics.

• C. App Engine + Cloud SQL + Looker: App Engine is used for hosting applications, and Cloud SQL is a relational database. This stack is better for web apps with transactional workloads, not streaming analytics.

• D. Cloud Run + Vertex AI + Firestore: Cloud Run is for containerized applications, Vertex AI is for ML workflows, and Firestore is a NoSQL document database. This setup is suitable for ML-powered apps but not for ingesting and processing IoT streaming data.

In summary, Cloud Pub/Sub captures real-time data, Dataflow processes it in-stream, and BigQuery analyzes it with SQL. This combination is commonly used for IoT, log analytics, and event processing—making it the ideal solution for real-time analytics on Google Cloud.