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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A) Grant each partner read access to the BigQuery dataset by using 1AM roles.
B) Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
C) Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D) Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

2. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A) Cloud Storage BigQuery Firestore
B) Filestore Bigtable BigQuery
C) Filestore Cloud SQL for PostgreSQL Datastore
D) Cloud Storage CloudSQL for PostgreSQL Bigtable

3. Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
B) Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
C) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D) Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

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

5. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A) Use Dataform to define the transformations in SQLX.
B) Use Dataflow with a custom template for the transformation logic.
C) Use Dataproc to run the transformations on a cluster.
D) Use Cloud Data Fusion to visually design and manage the pipeline.

Solutions: