Win IT Exam with Last Dumps 2025

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? (Choose two.)

A. Use Cloud Deployment Manager to automate access provision. B. Introduce resource hierarchy to leverage access control policy inheritance. C. Create distinct groups for various teams, and specify groups in Cloud IAM policies. D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets. E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:
- Single global endpoint
- ANSI SQL support
- Consistent access to the most up-to-date data
What should you do?

A. Implement BigQuery with no region selected for storage or processing. B. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe. C. Implement Cloud SQL for PostgreSQL with the master in North America and read replicas in Asia and Europe. D. Implement Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account. B. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account. C. Create a Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account. D. Create a Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Bigtable.

You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time.
Consumers will receive the data in the following ways:
- Real-time event stream
- ANSI SQL access to real-time stream and historical data
- Batch historical exports
Which solution should you use?

A. Cloud Dataflow, Cloud SQL, Cloud Spanner B. Cloud Pub/Sub, Cloud Storage, BigQuery C. Cloud Dataproc, Cloud Dataflow, BigQuery D. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
- Decoupling producer from consumer
- Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
- Near real-time SQL query
- Maintain at least 2 years of historical data, which will be queried with SQL
Which pipeline should you use to meet these requirements?

A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files. B. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery. C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk. D. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

You are running a pipeline in Dataflow that receives messages from a Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Increase the number of max workers B. Use a larger instance type for your Dataflow workers C. Change the zone of your Dataflow pipeline to run in us-central1 D. Create a temporary table in Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Bigtable to BigQuery E. Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A. Configure your Dataflow pipeline to use local execution B. Increase the maximum number of Dataflow workers by setting maxNumWorkers in PipelineOptions C. Increase the number of nodes in the Bigtable cluster D. Modify your Dataflow pipeline to use the Flatten transform before writing to Bigtable E. Modify your Dataflow pipeline to use the CoGroupByKey transform before writing to Bigtable

You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

A. Create a Cloud Dataproc Workflow Template B. Create an initialization action to execute the jobs C. Create a Directed Acyclic Graph in Cloud Composer D. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create an API using App Engine to receive and send messages to the applications B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them C. Create a table on Cloud SQL, and insert and delete rows with the job information D. Create a table on Cloud Spanner, and insert and delete rows with the job information

You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A. The current epoch time B. A concatenation of the product name and the current epoch time C. A random universally unique identifier number (version 4 UUID) D. The original order identification number from the sales system, which is a monotonically increasing integer