[Nov-2026] Google Associate-Data-Practitioner Official Cert Guide PDF [Q37-Q58]

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[Nov-2026] Google Associate-Data-Practitioner Official Cert Guide PDF

Exam Associate-Data-Practitioner: Google Cloud Associate Data Practitioner – PrepAwayPDF

Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic	Details
Topic 1	Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Topic 2	Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.\| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 3	Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

NEW QUESTION 37
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?

Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

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.

Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

NEW QUESTION 38
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

Enable Object Versioning.

Change the bucket storage class to Archive.

Set a bucket retention policy.

Set temporary object holds.

NEW QUESTION 39
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

NEW QUESTION 40
You have a BigQuery dataset containing sales data. 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?

Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

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.

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.

Store all data in a single BigQuery table without partitioning or lifecycle policies.

NEW QUESTION 41
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?

Use Cloud Data Fusion to visually design and manage the pipelines.

Use Dataform to build, orchestrate, and monitor the pipelines.

Use Dataproc to run MapReduce jobs for distributed data processing.

Use Cloud Composer to orchestrate and run data workflows.

NEW QUESTION 42
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?

Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Use Cloud Run functions to transform and load the data into BigQuery.

Use Dataflow to transform the data and write the transformed data to BigQuery.

Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

NEW QUESTION 43
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?

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.

Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

NEW QUESTION 44
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

Retry messages until they are acknowledged.
B Implement flow control on the subscribers

Forward unacknowledged messages to a dead-letter topic.

Seek back to the last acknowledged message.

NEW QUESTION 45
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?

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations.Store the cleaned data in BigQuery.

NEW QUESTION 46
Your organization’s business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

Disable BiqQuery query result caching.

Modify the schema to use parameterized data types.

Create a scheduled query to calculate and store intermediate results.

Create materialized views.

NEW QUESTION 47
Your organization’s website uses an on-premises MySQL as a backend database. You need to migrate the on- premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.

Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

NEW QUESTION 48
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?

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.

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.

Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

NEW QUESTION 49
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?

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.

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.

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.

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.

NEW QUESTION 50
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?

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.

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.

Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

Grant the partners the bigquery.user IAM role on the BigQuery project.

NEW QUESTION 51
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?

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.

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.

Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

NEW QUESTION 52
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?

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

NEW QUESTION 53
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?

Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

Create a Cloud Run function that runs daily and deletes files older than seven days.

NEW QUESTION 54
You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the dat a. What should you do?

Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.

Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.

Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.

Use BigQuery’s built-in functions to perform data quality transformations.

NEW QUESTION 55
You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement,and previous click behavior. After training the model, you want to generate predictions on new data. Which model type should you use in BigQuery ML?

Linear regression

Matrix factorization

Logistic regression

K-means clustering

NEW QUESTION 56
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?

Use AEAD functions and delete keys when employees leave the organization.

Use dynamic data masking and revoke viewer permissions when employees leave the organization.

Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.

Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

NEW QUESTION 57
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 a. 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?

Create authorized views in the team’s Google Cloud project that is only accessible by the team.

Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.

Export the dataset to a Cloud Storage bucket in the team’s Google Cloud project that is only accessible by the team.

NEW QUESTION 58
Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Use Looker’s custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

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Google Associate-Data-Practitioner Exam Syllabus Topics:

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