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Google Associate Data Practitioner Sample Questions:

1. You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A) Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
B) Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
C) Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
D) Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

2. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A) Set temporary object holds.
B) Set a bucket retention policy.
C) Change the bucket storage class to Archive.
D) Enable Object Versioning.

3. 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?

A) Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
B) Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
C) Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D) Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

4. 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) 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.
B) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
C) 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.
D) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

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

Solutions: