Valid ADP Test Answers & Google ADP Exam PDF [Q34-Q48]

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Valid ADP Test Answers & Google ADP Exam PDF

Google ADP Certification Real 2026 Mock Exam

Q34. You are working on a project that requires analyzing daily social media dat a. 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.

Q35. Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Q36. You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

Q37. Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

Create a dual-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

Create a multi-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

Q38. You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Q39. Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the –no- clobber option.

Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the — daisy-chain option.

Q40. 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?

Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

Q41. 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.

Q42. 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.

Q43. Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

Migrate the Spark jobs to Dataproc Serverless.

Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

Migrate the Spark jobs to Dataproc on Compute Engine.

Q44. 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.

Q45. 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?

Use BigQuery ML to create a logistic regression model for purchase prediction.

Use Vertex Al Workbench to develop a custom model for purchase prediction.

Use Colab Enterprise to develop a custom model for purchase prediction.

Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Q46. You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Q47. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team’s existing skillset, processes data at scale, and minimizes cost. What should you do?

Use Dataflow with a custom template for the transformation logic.

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

Use Dataform to define the transformations in SQLX.

Use Dataproc to run the transformations on a cluster.

Q48. You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

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ADP Exam Questions and Valid ADP Dumps PDF: https://www.testsimulate.com/ADP-study-materials.html

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