Google ADP

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A) Require users to specify a partition filter using the alter table statement in SQL.
B) Create a view that filters out rows that are older than one year.
C) Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
D) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

2. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A) Use Dataflow pipelines.
B) Use Cloud Composer operators.
C) Use Cloud Data Fusion pipelines.
D) Use Dataform workflows.

3. 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?

A) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
B) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
C) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
D) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

4. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
C) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
D) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

5. 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).

Solutions: