Question 1

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 2

An organization wants to keep track of different versions of their customer churn prediction model, ensuring that each version’s metadata, performance metrics, and deployment status are easily accessible. What should they prioritize in Vertex AI Model Registry to manage these requirements?

Options :

A : Scheduling batch predictions on model versions to assess performance

B : Storing models as binary files

C : Organizing models in Google Sheets with their metrics and deployment statuses

D : Using tags to label models with version numbers and descriptions

Answer: D

Question 3

A marketing analytics team wants to track the effectiveness of their digital campaigns using Looker. They need a dashboard with a line chart displaying the click-through rate (CTR) trend over time, segmented by campaign, and want the ability to filter by date range. Which steps would best allow you to implement this dashboard while meeting the requirement for interactive date filtering and campaign segmentation?

Options :

A : Create a pivot table with CTR as a row and date as columns, and filter campaigns within the pivot table.

B : Create a line chart with date on the x-axis, CTR on the y-axis, and set campaign as a series to segment the data. Add a date filter to the dashboard.

C : Create a custom visualization in Looker that directly links to campaign sources for additional drill-down capabilities.

D : Develop a line chart with CTR on the x-axis and date on the y-axis, and add a filter for campaign in the dashboard to isolate specific campaigns.

Answer: B

Question 4

A company stores large amounts of data in Cloud Storage that is only accessed a few times a year for quarterly reports. The data needs to be retained for a minimum of three years for compliance purposes, but retrieval speed is not critical. Which storage class is the most cost-effective option?

Options :

A : Nearline

B : Archive

C : Coldline

D : Standard

Answer: C

Question 5

You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

Options :

A : Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table.

B : Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

C : Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

D : Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

Answer: A

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