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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A) Use Dataflow pipelines.
B) Use Dataform workflows.
C) Use Cloud Composer operators.
D) Use Cloud Data Fusion pipelines.

2. Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A) Store the data in Cloud Storage using Standard storage.
B) Store the data in Cloud Storage using Coldline storage.
C) Store the data in Cloud Storage using Archive storage.
D) Store the data in Cloud Storage using Nearline storage.

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 Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
D) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

4. You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A) Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
B) Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
C) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

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

Solutions: