The Google GCP-ADP exam preparation guide is designed to provide candidates with necessary information about the GCP-ADP exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Google Cloud Platform - Associate Data Practitioner (GCP-ADP) exam.
It is recommended for all the candidates to refer the GCP-ADP objectives and sample questions provided in this preparation guide. The Google GCP-ADP certification is mainly targeted to the candidates who want to build their career in Associate domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Google Associate Data Practitioner exam.
Google GCP-ADP Exam Summary:
| Exam Name | Google Associate Data Practitioner (GCP-ADP) |
| Exam Code | GCP-ADP |
| Exam Price | $125 USD |
| Duration | 120 minutes |
| Number of Questions | 50-60 |
| Passing Score | Pass / Fail (Approx 70%) |
| Recommended Training / Books | Google Cloud training |
| Schedule Exam | Google CertMetrics |
| Sample Questions | Google GCP-ADP Sample Questions |
| Recommended Practice | Google Cloud Platform - Associate Data Practitioner (GCP-ADP) Practice Test |
Google GCP-ADP Syllabus:
| Section | Objectives |
|---|---|
Data Preparation and Ingestion - 30% |
|
| Prepare and process data. Considerations include |
- Differentiate between different data manipulation methodologies (e.g., ETL, ELT, ETLT) - Choose the appropriate data transfer tool (e.g., Storage Transfer Service, Transfer Appliance) - Assess data quality - Conduct data cleaning (e.g., Cloud Data Fusion, BigQuery, SQL, Dataflow) |
| Extract and load data into appropriate Google Cloud storage systems. |
- Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables) - Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion) - Select the appropriate storage solution (e.g., Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner)
- Load data into Google Cloud storage systems using the appropriate tool (e.g., gcloud and BQ CLI, Storage Transfer Service, BigQuery Data Transfer Service, client libraries) |
Data Analysis and Presentation - 27% |
|
| Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks. |
- Define and execute SQL queries in BigQuery to generate reports and extract key insights - Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise) - Analyze data to answer business questions |
| Visualize data and create dashboards in Looker given business requirements. |
- Create, modify, and share dashboards to answer business questions - Compare Looker and Looker Studio for different analytics use cases - Manipulate simple LookML parameters to modify a data model |
| Define, train, evaluate, and use ML models. |
- Identify ML use cases for developing models by using BigQuery ML and AutoML -Use pretrained Google large language models (LLMs) using remote connection in BigQuery - Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction) - Execute SQL to create, train, and evaluate models using BigQuery ML - Perform inference using BigQuery ML models - Organize models in Model Registry |
Data Pipeline Orchestration - 18% |
|
| Design and implement simple data pipelines. |
- Select a data transformation tool (e.g., Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform) based on business requirements - Evaluate use cases for ELT and ETL - Choose products required to implement basic transformation pipelines |
| Schedule, automate, and monitor basic data processing tasks. |
- Create and manage scheduled queries (e.g., BigQuery, Cloud Scheduler, Cloud Composer) - Monitor Dataflow pipeline progress using the Dataflow job UI - Review and analyze logs in Cloud Logging and Cloud Monitoring - Select a data orchestration solution (e.g., Cloud Composer, scheduled queries, Dataproc Workflow Templates, Workflows) based on business requirements - Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery - Use Eventarc triggers in event-driven pipelines (Dataform, Dataflow, Cloud Functions, Cloud Run, Cloud Composer) |
Data Management - 25% |
|
| Configure access control and governance. |
- Establish the principles of least privileged access by using Identity and Access Management (IAM)
- Compare methods of access control for Cloud Storage (e.g., public or private access, uniform access) |
| Configure lifecycle management. |
- Determine the appropriate Cloud Storage classes based on the frequency of data access and retention requirements - Configure rules to delete objects after a specified period to automatically remove unnecessary data and reduce storage expenses (e.g., BigQuery, Cloud Storage) - Evaluate Google Cloud services for archiving data given business requirements |
| Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL. |
- Compare backup and recovery solutions offered as Google-managed services - Determine when to use replication - Distinguish between primary and secondary data storage location type (e.g., regions, dual-regions, multi-regions, zones) for data redundancy |
| Apply security measures and ensure compliance with data privacy regulations. |
- Identify use cases for customer-managed encryption keys (CMEK), customer-supplied encryption keys (CSEK), and Google-managed encryption keys (GMEK) - Understand the role of Cloud Key Management Service (Cloud KMS) to manage encryption keys - Identify the difference between encryption in transit and encryption at rest |