Google ADP Exam Questions : Associate Data Practitioner

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Use Dataflow to transform the data and write the transformed data to BigQuery.
B) Use Cloud Run functions to transform and load the data into BigQuery.
C) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

2. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
B) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
C) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D) Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

3. 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) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
C) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

4. You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A) BigQuery Analytics Hub
B) Dataproc Serverless Bigtable
C) Dataflow BigQuery
D) Cloud Composer Cloud SQL for MySQL

5. 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) 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.
B) 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.
C) Grant each partner read access to the BigQuery dataset by using 1AM roles.
D) Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Solutions: