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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 Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
B) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow 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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

2. Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A) Create an object table.
B) Create an external table.
C) Create a native table.
D) Create a temporary table.

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

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

5. Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A) Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
B) Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C) Create BigQuery scheduled queries to define the transformations in SQL.
D) Use Dataform to define the transformations in SQLX.

Solutions: