Win IT Exam with Last Dumps 2025

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

A. Set up a Cloud CDN in front of the bucket. B. Enable Object Versioning on the bucket. C. Store the data in a multi-region bucket. D. Store the data in Nearline storage.

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

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model. B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields. C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low). D. Apply a filter to only show products with a positive profit margin.

You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Enable access control by using IAM roles. B. Encrypt the data by using customer-managed encryption keys (CMEK). C. Update dataset privileges by using the SQL GRANT statement. D. Export the data to Cloud Storage, and use signed URLs to authorize access.

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.

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 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.

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.

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 Dataproc workflow templates to define and schedule the Spark job, and to email the report. C. Use Cloud Run functions to trigger the Spark job and email the report. D. Use Cloud Scheduler to trigger the Spark job, and use Cloud Run functions to email the report.

Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data. What should you do?

A. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project. B. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project. C. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project. D. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.