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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Orchestration | 18% | - Transformation tools selection
|
| Topic 2: Data Analysis and Presentation | 27% | - Data visualization and reporting
|
| Topic 3: Data Management and Governance | 25% | - Compliance and governance
|
| Topic 4: Data Preparation and Ingestion | 30% | - Data formats and classification
|
Google Associate Data Practitioner Sample Questions:
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. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery 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. 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.
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 Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
- C. 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.
- D. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
Correct Answer: D 🗳️
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?
- A. Require users to specify a partition filter using the alter table statement in SQL.
- B. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
- C. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
- D. Create a view that filters out rows that are older than one year.
Correct Answer: C 🗳️
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Use BigQuery ML to create a logistic regression model for purchase prediction.
- B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- C. Use Vertex Al Workbench to develop a custom model for purchase prediction.
- D. Use Colab Enterprise to develop a custom model for purchase prediction.
Correct Answer: A 🗳️
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
- A. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
- B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- C. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
- D. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
Correct Answer: D 🗳️
