Google Associate Data Practitioner Quiz 1 Topic 4 Questions 1-5

Question: 1

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

ACreate a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

BLaunch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

CCreate external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

DUse the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

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Question: 2

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

ACreate a sales_region user attribute, and assign each manager's region as the value of their user attribute. Add an access_filter Explore filter on the region_name dimension by using the sales_region user attribute.

BCreate five different Explores with the sql_always_filter Explore filter applied on the region_name dimension. Set each region_name value to the corresponding region for each manager.

CCreate separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

DCreate separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

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Question: 3

Your company uses Looker to visualize and analyze sales dat

a. 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?

ACreate multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

BUse BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

CCreate a single Explore with all sales metrics. Build the dashboard using this Explore.

DUse Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

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Question: 4

You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

ASet up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

BConfigure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

CDevelop a batch process using Dataflow that runs weekly and deletes files based on their age.

DCreate a Cloud Run function that runs daily and deletes files older than seven days.

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Question: 5

You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

AUse the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

BCreate a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

CCreate a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

DCreate a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

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Google Associate Data Practitioner Quiz:1 Topic:4 Questions:1-5