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Google Associate Data Practitioner Sample Questions:

1. Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A) Apply a filter to only show products with a positive profit margin.
B) Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
C) Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
D) Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

2. You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A) Enable access control by using IAM roles.
B) Export the data to Cloud Storage, and use signed URLs to authorize access.
C) Encrypt the data by using customer-managed encryption keys (CMEK).
D) Update dataset privileges by using the SQL GRANT statement.

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?

A) Create a single Explore with all sales metrics. Build the dashboard using this Explore.
B) Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
C) Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
D) Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

4. Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
B) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
D) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.

5. 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?

A) Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
B) Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
C) Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
D) Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

Solutions: