Free GCP-ADP Sample Questions — Google Cloud Platform - Associate Data Practitioner

Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

• A. Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery
• B. Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery
• C. Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery
• D. Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery

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?

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

You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model. What should you do?

• A. Compare the two different models
• B. Evaluate the data skewness
• C. Evaluate data drift
• D. Compare the confusion matrix

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 Logs Explorer page in Cloud Logging. Use filters to find 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. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details
• D. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

• A. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week
• B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week
• C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week
• D. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week

Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A. Store the data in Cloud Storage using Nearline storage
• B. Store the data in Cloud Storage using Coldline storage
• C. Store the data in Cloud Storage using Standard storage
• D. Store the data in Cloud Storage using Archive storage

Your organization plans to move their on-premises environment to Google Cloud. Your organization’s network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

• A. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage
• B. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage
• C. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage
• D. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage

Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

• A. Use Cloud Data Fusion pipelines
• B. Use Dataform workflows
• C. Use Dataflow pipelines
• D. Use Cloud Composer operators

You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

• A. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery
• B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes
• C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage
• D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery

Your company’s ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?

• A. Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink
• B. Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink
• C. Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery
• D. Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews

Your retail company collects customer data from various sources: Online transactions: Stored in a MySQL database Customer feedback: Stored as text files on a company server Social media activity: Streamed in real-time from social media platforms You are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

• A. 1. Online transactions: Cloud Storage 2. Customer feedback: Cloud Storage 3. Social media activity: Cloud Storage
• B. 1. Online transactions: BigQuery 2. Customer feedback: Cloud Storage 3. Social media activity: BigQuery
• C. 1. Online transactions: Bigtable 2. Customer feedback: Cloud Storage 3. Social media activity: CloudSQL for MySQL
• D. 1. Online transactions: Cloud SQL for MySQL 2. Customer feedback: BigQuery 3. Social media activity: Cloud Storage

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?

• A. Request the Dataflow Developer role
• B. Request the Dataflow Viewer role
• C. Request the Dataflow Worker role
• D. Request the Dataflow Admin role

You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

• A. Add a policy tag in BigQuery
• B. Create a row-level access policy
• C. Create a data masking rule
• D. Grant the appropriate IAM permissions on the dataset

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 data. 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. 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
• C. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket
• D. Grant the partners the bigquery.user IAM role on the BigQuery project

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 AEAD functions and delete keys when employees leave the organization
• B. Use dynamic data masking and revoke viewer permissions when employees leave the organization
• C. Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization
• D. Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization

Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

• A. Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation
• B. Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation
• C. Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation
• D. Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation

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?

• A. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality
• B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views
• C. Create a single Explore with all sales metrics. Build the dashboard using this Explore
• D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality

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. 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
• 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. 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

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 a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting
• B. 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
• C. 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
• 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

Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

• A. Use a Transfer Appliance
• B. Use Cloud Storage FUSE
• C. Use Storage Transfer Service
• D. Use gcloud storage commands