Question 1

A company wants to migrate a large dataset of historical data from its on-premises storage to Cloud Storage. The dataset is around 100 TB, and the company has a limited network bandwidth, which would make online transfer highly time-consuming and resource-intensive. They need a solution that allows them to quickly and securely transfer this data to Google Cloud without causing disruptions to their ongoing operations. Which data transfer tool should the company choose?

Options :

A : Transfer Appliance

B : BigQuery Data Transfer Service

C : Cloud Data Fusion

D : Storage Transfer Service

Answer: A

Question 2

You are asked to create a Looker dashboard to help the sales team understand quarterly sales performance across regions. They want to see both total sales and the number of new customers by region, with filters to view specific quarters. Which approach best fulfills this requirement?

Options :

A : Create one chart with a combined line and bar visualization for total sales and new customer count by region. Apply a quarter filter to the chart itself.

B : Use two separate charts: one for total sales by region and another for new customers by region. Apply a dashboard filter for the quarter, which will affect both charts simultaneously.

C : Create a dashboard with one tile showing total sales by month and another showing total customers by region. Apply filters for region and month, but exclude a quarter filter.

D : Create a single table chart that includes total sales, new customer count, and region columns. Set the dashboard filters to allow selection of specific regions.

Answer: B

Question 3

Which two advantages does using pretrained Google Large Language Models (LLMs) via a remote connection in BigQuery offer when working with natural language processing (NLP) tasks? (Select two)

Options :

A : Google LLMs enable the immediate use of sophisticated NLP capabilities without requiring model development or training expertise.

B : Google LLMs are primarily suited for numerical data processing, making them less ideal for NLP within BigQuery.

C : BigQuery provides built-in support for custom Python packages, allowing advanced NLP operations on Google LLMs directly.

D : Custom layers can be added to pretrained LLMs directly in BigQuery for domain-specific tuning.

E : Integration with BigQuery ML allows users to query and process large datasets using Google LLMs at scale.

Answer: A,E

Question 4

A retail business analyst needs to create a dashboard in Looker to monitor the weekly sales performance across various product categories. The business stakeholders require a single-view dashboard that shows key performance metrics such as total revenue, average order value, and weekly growth rates. Which action should the analyst take to best meet these business requirements in Looker?

Options :

A : Use a LookML model to create dimensions and measures for revenue, average order value, and growth rates, then save these as separate Looks and embed them in the dashboard.

B : Import a Looker Block that includes pre-built dashboards for sales metrics and customize the visualizations according to the business requirements.

C : Export raw sales data from Looker and upload it to an external visualization tool like Tableau for further customization.

D : Create a new dashboard directly in Looker, add tiles with visualizations for each metric, and use filters to allow users to select different time ranges.

Answer: B

Question 5

A retail company needs to transfer a large volume of historical sales data from an on-premises data warehouse to BigQuery to perform trend analysis and forecasting. They want a managed, automated solution that can schedule regular data transfers and manage any changes in data size over time. Which Google Cloud tool is best suited for this task?

Options :

A : Manual CSV uploads to Cloud Storage

B : Cloud Storage Transfer Service

C : gcloud CLI command

D : BigQuery Data Transfer Service

Answer: D

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