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

You are building a Looker dashboard for a sales team that wants to track daily and monthly sales performance for different product categories. They want to monitor total sales, average sale price, and the number of items sold. Which of the following features would best allow the team to switch between daily and monthly views on the dashboard without requiring separate charts?

Options :

A : Using a scatter plot with data points labeled by time

B : Adding a date filter with granularity options

C : Applying conditional formatting to highlight trends

D : Using multiple line charts for each time frame

Answer: B

Question 3

A data team is using Google Cloud to process a large dataset with inconsistent date formats, empty strings, and incorrectly capitalized text values. They want to standardize these formats, remove empty strings, and apply other basic data-cleaning operations. Which service should they use to process this data efficiently using SQL-like queries?

Options :

A : Cloud Datastore

B : Firebase Realtime Database

C : Cloud Dataflow

D : BigQuery

Answer: D

Question 4

You are tasked with designing a data pipeline that ingests daily transaction logs from Cloud Storage, performs transformations to filter and summarize the data, and loads the result into BigQuery. The pipeline needs to be managed and scheduled to run every night at midnight. Which set of Google Cloud products would best meet these requirements?

Options :

A : BigQuery ML to perform transformations, Cloud Functions for scheduling, and Cloud Storage as the final destination.

B : Cloud Composer for orchestration, Cloud Dataflow for transformations, and BigQuery as the final storage.

C : Cloud Functions to trigger, Cloud Storage for transformations, and Dataproc as the final destination.

D : Cloud Scheduler to trigger the pipeline, Pub/Sub for transformations, and BigQuery as the final storage.

Answer: B

Question 5

Your organization processes data collected from IoT sensors every day to generate analytics reports. This data is valuable for only 90 days and then becomes obsolete. Which lifecycle policy is the most efficient for managing this data in Cloud Storage?

Options :

A : Store data in the Standard class, then delete it after 90 days.

B : Store data in the Archive class and delete it after 90 days.

C : Store data in the Standard class and transition it to Archive after 30 days.

D : Store data in the Nearline class and delete it after 90 days.

Answer: A

Confidently Practice Online with Free Associate-Data-Practitioner Exam Cram

Buying Options: