[Oct 13, 2025] Exam4Tests Associate-Data-Practitioner Exam Practice Test Questions (Updated 108 Questions)
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Google Associate-Data-Practitioner Exam Syllabus Topics:
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NEW QUESTION # 42
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?
- A. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
- B. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
- C. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
- D. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
Answer: C
Explanation:
UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time.
It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
NEW QUESTION # 43
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?
- A. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
- B. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
- C. Grant the partners the bigquery.user IAM role on the BigQuery project.
- D. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
Answer: A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.
NEW QUESTION # 44
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost-effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?
- A. Use Dataproc to run MapReduce jobs for distributed data processing.
- B. Use Cloud Data Fusion to visually design and manage the pipelines.
- C. Use Cloud Composer to orchestrate and run data workflows.
- D. Use Dataform to build, orchestrate, and monitor the pipelines.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation:
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let's dive in:
* Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn't fully serverless (requires instance management). It lacks native source code control and parameterization.
* Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality-all meeting the requirements cost-effectively.
* Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
NEW QUESTION # 45
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?
- A. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
- B. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
- C. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
- D. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
Answer: B
Explanation:
Usingobject holdsandlifecycle management rulesis the most efficient and compliant strategy for this scenario because:
* Immutability: Object holds (temporary or event-based) ensure that objects cannot be deleted or overwritten, meeting strict compliance regulations for data immutability.
* Cost efficiency: Lifecycle management rules automatically transition objects to more cost-effective storage classes based on their age and access patterns.
* Compliance and automation: This approach ensures compliance with retention requirements while reducing manual effort, leveraging built-in Cloud Storage features.
NEW QUESTION # 46
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?
- A. Create an external table.
- B. Create a native table.
- C. Create an object table.
- D. Create a temporary table.
Answer: C
Explanation:
To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.
NEW QUESTION # 47
You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?
- A. Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.
- B. Use Cloud Run functions to create a serverless data cleaning pipeline. 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.
Answer: A
Explanation:
Using Cloud Data Fusion is the best solution for this scenario because:
Standardized managed solution: Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.
Compliance: It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.
Ease of use: Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.
NEW QUESTION # 48
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?
- A. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
- B. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
- C. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
- D. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery's scalability within Colab Enterprise.
* Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
* Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
* Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.
NEW QUESTION # 49
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?
- A. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
- B. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
- C. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
- D. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
Answer: D
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
NEW QUESTION # 50
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?
- A. Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.
- B. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
- C. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute. - D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
Answer: C
Explanation:
Using asales_region user attributeis the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding anaccess_filterExplore filter on theregion_namedimension that references thesales_regionuser attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.
NEW QUESTION # 51
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?
- A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
- B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
- C. Apply a filter to only show products with a positive profit margin.
- D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
Answer: B
Explanation:
Defining a new measure in LookML to calculate the profit margin using the existing revenue and cost fields is the most efficient and straightforward solution. This approach allows you to dynamically compute the profit margin directly within your Looker Explores and dashboards without needing to pre-calculate or create additional tables. The measure can be defined using LookML syntax, such as:
measure: profit_margin {
type: number
sql: (revenue - cost) / revenue ;;
value_format: "0.0%"
}
This method is quick to implement and integrates seamlessly into your existing Looker model, enabling accurate visualization of profit margins across your products.
NEW QUESTION # 52
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Use Colab Enterprise to develop a custom model for purchase prediction.
- B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- C. Use Vertex AI Workbench to develop a custom model for purchase prediction.
- D. Use BigQuery ML to create a logistic regression model for purchase prediction.
Answer: D
NEW QUESTION # 53
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
To calculate the weekly moving average of sales by location:
The query must group by store_id (partitioning the calculation by each store).
The ORDER BY date ensures the sales are evaluated chronologically.
The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).
The AVG(total_sales) computes the average sales over the defined rolling window.
Chosen query meets these requirements:
NEW QUESTION # 54
You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?
- A. Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.
- B. Use Cloud Run functions to create a serverless data cleaning pipeline. 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.
Answer: A
Explanation:
UsingCloud Data Fusionis the best solution for this scenario because:
* Standardized managed solution: Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.
* Compliance: It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.
* Ease of use: Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.
* It's a fully managed, cloud-native data integration service for building ETL/ELT data pipelines visually.
* It offers built-in transformations and connectors, including those suitable for data masking and de- identification.
* It provides a standardized, visual interface, making it easier to create and manage data pipelines across various data sources.
* It's designed for data integration and transformation, making it ideal for this scenario.
* It helps to achieve a standardized managed solution.
NEW QUESTION # 55
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?
- A. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
- B. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
- C. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
- D. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let's assess:
* Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.
* Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training.
Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.
* Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).
NEW QUESTION # 56
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.
Answer: C
Explanation:
Creating aBigQuery Cloud resource connectionto a remote model inVertex AIand 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.
NEW QUESTION # 57
You are designing an application that will interact with several BigQuery datasets. You need to grant the application's service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre- defined IAM role(s) should you apply to the service account?
- A. roles/bigquery.user and roles/bigquery.filteredDataViewer
- B. roles/bigquery.admin
- C. roles/bigquery.jobUser and roles/bigquery.dataOwner
- D. roles/bigquery.connectionUser and roles/bigquery.dataViewer
Answer: C
Explanation:
* roles/bigquery.jobUser:
* This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.
* From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."
* roles/bigquery.dataOwner:
* This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.
* From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."
* Why other options are incorrect:
* B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:
* roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.
* C. roles/bigquery.admin:
* roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.
* D. roles/bigquery.user and roles/bigquery.filteredDataViewer:
* roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles
/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.
* Principle of Least Privilege:
* The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.
* By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.
* Google Cloud Documentation References:
* BigQuery IAM roles:https://cloud.google.com/bigquery/docs/access-control-basic-roles
* IAM best practices:https://cloud.google.com/iam/docs/best-practices-for-using-iam
NEW QUESTION # 58
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?
- A. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
- B. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job ID.
- C. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
- D. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
Answer: A
NEW QUESTION # 59
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?
- A. Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.
- B. Use AEAD functions and delete keys when employees leave the organization.
- C. Use dynamic data masking and revoke viewer permissions when employees leave the organization.
- D. Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
Answer: D
Explanation:
Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.
NEW QUESTION # 60
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?
- A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
- B. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
- C. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
- D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
Answer: D
Explanation:
Using Dataflow to create a streaming pipeline that includes validation and transformation steps is the most efficient and scalable approach for real-time analysis. Dataflow is optimized for high-volume data processing and allows you to apply validation and cleaning logic as the data flows through the pipeline. This ensures that only clean, validated data is loaded into BigQuery, supporting real-time analysis while handling high data volumes effectively.
NEW QUESTION # 61
You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process.
Your solution should minimize costs where possible. What should you do?
- A. Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
- B. Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.
- C. Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the - custom-storage-class flag.
- D. Create parallel transfer jobs using include and exclude prefixes.
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Creating parallel transfer jobs by using include and exclude prefixes allows you to split the data into smaller chunks and transfer them in parallel.
This can significantly increase throughput and reduce the overall transfer time.
Why other options are incorrect:A: Changing the storage class to Standard will not improve transfer speed.
C: Dataflow is a complex solution for a simple file transfer task.
D: Agent-based transfer is suitable for large files or network limitations, but not for a large number of small files.
NEW QUESTION # 62
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?
- A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
- B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
- C. Apply a filter to only show products with a positive profit margin.
- D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn't calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.
NEW QUESTION # 63
You are working on a project that requires analyzing daily social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google- recommended approach. What should you do?
- A. Use Dataflow to transform the data and write the transformed data to BigQuery.
- B. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
- C. Use Cloud Run functions to transform and load the data into BigQuery.
- D. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
Answer: A
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.
Dataflow is googles recomended way to transform large datasets.
It is designed for parallel processing, making it suitable for large datasets.
Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.
B: Cloud Run functions are for stateless applications, not large data transformations.
D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.
NEW QUESTION # 64
......
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