[Jul 27, 2026] ADP Exam Brain Dumps - Study Notes and Theory
Pass Google ADP Test Practice Test Questions Exam Dumps
NEW QUESTION # 25
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 Cloud Composer to orchestrate and run data workflows.
- B. Use Dataform to build, orchestrate, and monitor the pipelines.
- C. Use Dataproc to run MapReduce jobs for distributed data processing.
- D. Use Cloud Data Fusion to visually design and manage the pipelines.
Answer: B
NEW QUESTION # 26
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?
- A. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
- B. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
- C. Migrate the Spark jobs to Dataproc on Compute Engine.
- D. Migrate the Spark jobs to Dataproc Serverless.
Answer: D
NEW QUESTION # 27
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 I
- 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 Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
Answer: A
NEW QUESTION # 28
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. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
- D. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
Answer: A
NEW QUESTION # 29
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
- A. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
- B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
- C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- D. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
Answer: C
NEW QUESTION # 30
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?
- A. 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.
- B. 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.
- C. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
- D. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
Answer: C
NEW QUESTION # 31
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?
- A. Create a Cloud Run function that runs daily and deletes files older than seven days.
- B. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
- C. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
- D. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
Answer: B
NEW QUESTION # 32
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?
- A. BigQuery Analytics Hub
- B. Dataflow BigQuery
- C. Dataproc Serverless Bigtable
- D. Cloud Composer Cloud SQL for MySQL
Answer: B
NEW QUESTION # 33
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. Evaluate data drift.
- B. Compare the two different models.
- C. Compare the confusion matrix.
- D. Evaluate the data skewness.
Answer: A
NEW QUESTION # 34
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
- A. Use Cloud Composer to orchestrate the Spark job and email the report.
- B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- C. Use Cloud Run functions to trigger the Spark job and email the report.
- D. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
Answer: D
NEW QUESTION # 35
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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- B. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
- C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
Answer: B
NEW QUESTION # 36
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?
- A. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
- B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
- C. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
- D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
Answer: C
NEW QUESTION # 37
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?
- A. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
- B. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
- C. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
- D. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
Answer: C
NEW QUESTION # 38
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?
- A. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
- B. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
- C. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
- D. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
Answer: A
NEW QUESTION # 39
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. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: C
NEW QUESTION # 40
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?
- A. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
- B. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
- C. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
- D. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
Answer: C
NEW QUESTION # 41
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?
- A. Create a custom quota for each analyst in BigQuery.
- B. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
- C. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
- D. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
Answer: B
NEW QUESTION # 42
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