Google Associate Data Practitioner Sample Questions:

1. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A) Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B) Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
C) Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

2. You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A) Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
B) Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
C) Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
D) Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

3. You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A) Use the gcloud storage command to transfer the data over the internet.
B) Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C) Use Cloud Client Libraries to transfer the data over the internet.
D) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

4. Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A) Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
B) Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
C) Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
D) Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.

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

Solutions: