Google Associate Data Practitioner Practice Exam

Google Associate Data Practitioner Practice Exam

About Google Associate Data Practitioner Exam

The Google Associate Data Practitioner certification exam has been developed validates the skills and ability to secure and manage data on Google Cloud. The Associate Data Practitioner exam is ideal for professionals with experience in data ingestion, transformation, pipeline orchestration, analysis, machine learning, and visualization, this certification requires a foundational understanding of cloud computing concepts, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

Skills Evaluated

The exam evaluates your skills in four key areas:

• Data Preparation and Ingestion: Skills in data cleaning, quality assessment, extraction, and loading into Google Cloud storage systems using the appropriate tools and methodologies.
• Data Analysis and Visualization: Expertise in identifying trends using BigQuery, creating dashboards with Looker, and applying machine learning models.
• Data Pipeline Orchestration: Ability to design, schedule, automate, and monitor data pipelines with Google Cloud tools like Dataflow and Cloud Composer.
• Data Management: Proficiency in configuring access control, lifecycle management, security measures, and disaster recovery strategies.

Exam Details

• Exam Duration: 2 hours
• Exam Format: 50-60 Questions
• Types of Questions: Multiple-choice and multiple-select 
• Delivery Methods: Online-proctored or onsite at a test center
• Exam Language: English
• Recommended Experience: 6+ months working with Google Cloud data solutions
• Exam Validity: 3 years (recertification required within the eligibility window)

Course Outline

The Google Associate Data Practitioner Exam covers the following -

Section 1: Data Preparation and Ingestion (Approx 30%)

Prepare and Process Data

• Understand ETL, ELT, and ETLT methodologies.
• Evaluate and use appropriate data transfer tools, e.g., Storage Transfer Service.
• Perform data quality assessment and cleaning using tools like Cloud Data Fusion, BigQuery, SQL, and Dataflow.

Extract and Load Data

• Recognize data formats (e.g., CSV, JSON, Apache Parquet, Avro).
• Select the right extraction tools (e.g., BigQuery Data Transfer Service, Cloud Data Fusion).
• Choose appropriate storage solutions (Cloud Storage, BigQuery, Cloud SQL) considering location types and data structures.

Section 2: Data Analysis and Presentation (Approx 27%)

Analyze Data

• Use BigQuery and Jupyter notebooks for data analysis.
• Execute SQL queries to generate insights and reports.

Create Visualizations

• Develop dashboards using Looker and Looker Studio tailored to business requirements.
• Manipulate LookML parameters for basic data modeling.

Machine Learning Integration

• Identify ML use cases and create models using BigQuery ML and AutoML.
• Evaluate and execute ML workflows and organize models in Model Registry.

Section 3: Data Pipeline Orchestration (Approx 18%)

Design Data Pipelines

• Select suitable transformation tools, e.g., Dataproc, Cloud Data Fusion.
• Implement ELT or ETL strategies based on requirements.

Automate Data Processing

• Schedule queries and monitor pipelines with tools like Cloud Composer and Dataflow.
• Use event-driven systems such as Pub/Sub and Eventarc triggers.

Section 4: Data Management (~25%)

Access Control and Governance

• Apply least privilege access principles with IAM roles.
• Establish appropriate access controls for data sharing and storage.

Lifecycle Management

• Configure rules for data retention and deletion to optimize storage.
• Choose suitable archiving solutions based on business needs.

High Availability and Disaster Recovery

• Compare Google Cloud’s backup and replication options.
• Select primary and secondary storage locations for redundancy.

Security and Compliance

• Differentiate between CMEK, CSEK, and GMEK encryption options.
• Use Cloud Key Management Service (KMS) for encryption and compliance.