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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Management and Governance | 25% | - Data quality and maintenance
|
| Data Preparation and Ingestion | 30% | - Data formats and classification
|
| Data Pipeline Orchestration | 18% | - Transformation tools selection
|
| Data Analysis and Presentation | 27% | - Data visualization and reporting
|
Google Associate Data Practitioner Sample Questions:
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. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
- B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
- C. 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.
- D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
Correct Answer: D 🗳️
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?
- A. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
- B. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
- C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
- D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
Correct Answer: B 🗳️
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
- A. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
- B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
- C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
- D. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
Correct Answer: B 🗳️
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?
- A. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
- B. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
- C. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
- D. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
Correct Answer: D 🗳️
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
- A. Use Cloud Scheduler to schedule the jobs to run.
- B. Use Cloud Tasks to schedule and run the jobs asynchronously.
- C. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- D. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
Correct Answer: C 🗳️
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