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Google Associate Data Practitioner Sample Questions:

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

2. 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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute 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.

3. You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A) Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
B) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
C) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
D) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

4. Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
B) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
C) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
D) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.

5. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
B) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
C) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
D) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

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