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  • Associate-Data-Practitioner - Certification Exam Questions
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Associate-Data-Practitioner Practice Questions


You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days.
What should you do?

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

Answer(s): B

Explanation:

Configuring a Cloud Storage lifecycle rule to automatically delete objects older than seven days is the best solution because:

Built-in feature: Cloud Storage lifecycle rules are specifically designed to manage object lifecycles, such as automatically deleting or transitioning objects based on age.

No additional setup: It requires no external services or custom code, reducing complexity and maintenance.

Cost-effective: It directly achieves the goal of deleting files after seven days without incurring additional compute costs.



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?

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

Answer(s): B

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.



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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Answer(s): D

Explanation:

Using object holds and lifecycle management rules is 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.



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?

  1. Use BigQuery ML to create a logistic regression model for purchase prediction.
  2. Use Vertex AI Workbench to develop a custom model for purchase prediction.
  3. Use Colab Enterprise to develop a custom model for purchase prediction.
  4. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Answer(s): A

Explanation:

Using BigQuery ML is the best solution in this case because:

Ease of use: BigQuery ML allows users to build machine learning models using SQL, which requires minimal ML expertise.

Integrated platform: Since the data already exists in BigQuery, there's no need to move it to another service, saving time and engineering resources.

Logistic regression: This is an appropriate model for binary classification tasks like predicting the likelihood of a customer making a purchase in the next month.



You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible.
What should you do?

  1. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
  2. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
  3. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
  4. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

Answer(s): D

Explanation:

Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:

Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.

Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.

Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.



Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of dat

  1. You also want to create a reusable framework in case you need to share this data with other teams in the future.
    What should you do?
  2. Create authorized views in the team's Google Cloud project that is only accessible by the team.
  3. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
  4. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
  5. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.

Answer(s): B

Explanation:

Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.



Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular.

You need to design a storage system that is simple and cost-effective.
What should you do?

  1. Create a single-region bucket with Autoclass enabled.
  2. Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.
  3. Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.
  4. Create a single-region bucket with Archive as the default storage class.

Answer(s): C

Explanation:

Creating a single-region bucket with custom Object Lifecycle Management policies based on upload date is the most appropriate solution. This approach allows you to automatically transition objects to less expensive storage classes as their access frequency decreases over time. For example, frequently accessed files can remain in the Standard storage class initially, then transition to Nearline, Coldline, or Archive storage as their popularity wanes. This strategy ensures a cost-effective and efficient storage system while maintaining simplicity by automating the lifecycle management of video files.



You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege.
What should you do?

  1. Request the Dataflow Developer role.
  2. Request the Dataflow Viewer role.
  3. Request the Dataflow Worker role.
  4. Request the Dataflow Admin role.

Answer(s): A

Explanation:

The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.



You need to create a new data pipeline. You want a serverless solution that meets the following requirements:

· Data is streamed from Pub/Sub and is processed in real-time.

· Data is transformed before being stored.

· Data is stored in a location that will allow it to be analyzed with SQL using Looker.



Which Google Cloud services should you recommend for the pipeline?

  1. 1. Dataproc Serverless
    2. Bigtable
  2. 1. Cloud Composer
    2. Cloud SQL for MySQL
  3. 1. BigQuery
    2. Analytics Hub
  4. 1. Dataflow
    2. BigQuery

Answer(s): D

Explanation:

To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.



Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard.
What should you do?

  1. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.
  2. Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.
  3. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
  4. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

Answer(s): A

Explanation:

Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.



You have a BigQuery dataset containing sales dat

  1. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum.
    What should you do?
  2. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
  3. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage.
    Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
  4. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
  5. Store all data in a single BigQuery table without partitioning or lifecycle policies.

Answer(s): B

Explanation:

Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.



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?

  1. Create a sales_region user attribute, and assign each manager's region as the value of their user attribute. Add an access_filter Explore filter on the region_name dimension by using the sales_region user attribute.
  2. Create five different Explores with the sql_always_filter Explore filter applied on the region_name dimension. Set each region_name value to the corresponding region for each manager.
  3. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
  4. Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

Answer(s): A

Explanation:

Using a sales_region user attribute is the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding an access_filter Explore filter on the region_name dimension that references the sales_region user 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.



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