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Google Associate Data Practitioner (Associate Data Practitioner) - Google Cloud Exam Questions
Last updated on June 20, 2026
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106
Total Questions
1
Question
Your company’s ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?
Options
A
Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.
B
Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U
C
Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
D
Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.
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2
Question
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?
1. Dataproc Serverless
A.
2. Bigtable
B. C.
D.
1. Cloud Composer
2. Cloud SQL for MySQL
1. BigQuery
2. Analytics Hub
1. Dataflow
2. BigQuery
• 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?
1. Dataproc Serverless
A.
2. Bigtable
B. C.
D.
1. Cloud Composer
2. Cloud SQL for MySQL
1. BigQuery
2. Analytics Hub
1. Dataflow
2. BigQuery
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3
Question
You work for a retail company that collects customer data from various sources: Online transactions: Stored in a MySQL database Customer feedback: Stored as text files on a company server Social media activity: Streamed in real-time from social media platforms You need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?
Options
A
Extract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.
B
Copy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.
C
Extract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.
D
Extract and load the online transactions data into Bigtable. Import the customer feedback data into Cloud Storage. Store the social media activity in Cloud SQL for MySQL.
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4
Question
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?
Options
A
Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
B
Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
C
Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
D
Create a Cloud Run function that runs daily and deletes files older than seven days.
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5
Question
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 (Pll) 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.
1. Cloud Storage
2. CloudSQL for PostgreSQL
3. Bigtable
B.
1. Filestore
2. Cloud SQL for PostgreSQL
3. Datastore
C.
1. Cloud Storage
2. BigQuery
3. Firestore
D.
1. Filestore
2. Bigtable
3. BigQuery
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users’ personally identifiable information (Pll) 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.
1. Cloud Storage
2. CloudSQL for PostgreSQL
3. Bigtable
B.
1. Filestore
2. Cloud SQL for PostgreSQL
3. Datastore
C.
1. Cloud Storage
2. BigQuery
3. Firestore
D.
1. Filestore
2. Bigtable
3. BigQuery
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