Win IT Exam with Last Dumps 2025

You are developing a new deep learning model that predicts a customer's likelihood to buy on your ecommerce site. After running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?

A. Increase the size of the training dataset, and increase the number of input features. B. Increase the size of the training dataset, and decrease the number of input features. C. Reduce the size of the training dataset, and increase the number of input features. D. Reduce the size of the training dataset, and decrease the number of input features.

You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries.
You are looking for a low-code or no-cade option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

A. Use the Cloud Speech-to-Text API to build a Python application in App Engine. B. Use the Cloud Speech-to-Text API to build a Python application in a Compute Engine instance. C. Use Dialogflow for simple queries and the Cloud Speech-to-Text API for complex queries. D. Use Dialogflow to implement the chatbot, defining the intents based on the most common queries collected.

An aerospace company uses a proprietary data format to store its flight data. You need to connect this new data source to BigQuery and stream the data into
BigQuery. You want to efficiently import the data into BigQuery while consuming as few resources as possible. What should you do?

A. Write a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source. B. Use a standard Dataflow pipeline to store the raw data in BigQuery, and then transform the format later when the data is used. C. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format. D. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format.

An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and call the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

A. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to the Python API. B. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic. C. Write an application that makes a queue in a NoSQL database. D. Use Cloud Composer to subscribe to a Pub/Sub topic and call the Python API.

Your company wants to be able to retrieve large result sets of medical information from your current system, which has over 10 TBs in the database, and store the data in new tables for further query. The database must have a low-maintenance architecture and be accessible via SQL. You need to implement a cost-effective solution that can support data analytics for large result sets. What should you do?

A. Use Cloud SQL, but first organize the data into tables. Use JOIN in queries to retrieve data. B. Use BigQuery as a data warehouse. Set output destinations for caching large queries. C. Use a MySQL cluster installed on a Compute Engine managed instance group for scalability. D. Use Cloud Spanner to replicate the data across regions. Normalize the data in a series of tables.

You have 15 TB of data in your on-premises data center that you want to transfer to Google Cloud. Your data changes weekly and is stored in a POSIX-compliant source. The network operations team has granted you 500 Mbps bandwidth to the public internet. You want to follow Google-recommended practices to reliably transfer your data to Google Cloud on a weekly basis. What should you do?

A. Use Cloud Scheduler to trigger the gsutil command. Use the -m parameter for optimal parallelism. B. Use Transfer Appliance to migrate your data into a Google Kubernetes Engine cluster, and then configure a weekly transfer job. C. Install Storage Transfer Service for on-premises data in your data center, and then configure a weekly transfer job. D. Install Storage Transfer Service for on-premises data on a Google Cloud virtual machine, and then configure a weekly transfer job.

You are designing a system that requires an ACID-compliant database. You must ensure that the system requires minimal human intervention in case of a failure.
What should you do?

A. Configure a Cloud SQL for MySQL instance with point-in-time recovery enabled. B. Configure a Cloud SQL for PostgreSQL instance with high availability enabled. C. Configure a Bigtable instance with more than one cluster. D. Configure a BigQuery table with a multi-region configuration.

You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

A. Use Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling. B. Use Dataflow for your workflow pipelines. Use shell scripts to schedule workflows. C. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project. D. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated data. You expect a high volume of concurrent users. You need to optimize the dashboard to provide quick visualizations with minimal latency. What should you do?

A. Use BigQuery BI Engine with materialized views. B. Use BigQuery BI Engine with logical views. C. Use BigQuery BI Engine with streaming data. D. Use BigQuery BI Engine with authorized views.

Government regulations in the banking industry mandate the protection of clients' personally identifiable information (PII). Your company requires PII to be access controlled, encrypted, and compliant with major data protection standards. In addition to using Cloud Data Loss Prevention (Cloud DLP), you want to follow
Google-recommended practices and use service accounts to control access to PII. What should you do?

A. Assign the required Identity and Access Management (IAM) roles to every employee, and create a single service account to access project resources. B. Use one service account to access a Cloud SQL database, and use separate service accounts for each human user. C. Use Cloud Storage to comply with major data protection standards. Use one service account shared by all users. D. Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group.