Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
0 Hour(s) 0 Minute(s)
Language
English
Taught by
Vladimir Raykov
Rating
Course Overview
![[NEW] GCP Associate Data Practitioner - 5 Full Mock Exams](https://i.udemycdn.com/course/480x270/6761289_b9ce.jpg)
Master the GCP - Associate Data Practitioner exam! 250 unique questions covering Google Cloud offerings
Prepare confidently for the GCP Associate Data Practitioner certification with this comprehensive practice exam course. Designed to mirror the official exam experience, this course provides the essential practice and in-depth understanding you need to succeed.
What you'll experience:
Five (5) Full-Length Mock Exams: Test your knowledge across all exam domains with realistic scenarios and question formats.
250 Unique, High-Quality Questions: Each question is carefully crafted to assess your understanding of core concepts and Google Cloud's data ecosystem.
Detailed Explanations for Every Option: Go beyond just knowing the right answer. Understand why the correct option is best and why the incorrect options are wrong, turning every question into a learning opportunity.
Direct Links to Official Resources: Access relevant Google Cloud documentation and study guides directly from explanations to deepen your knowledge on specific topics.
Full Alignment with the Official Exam Guide: Questions are meticulously mapped to the latest Google Cloud Associate Data Practitioner exam syllabus, ensuring targeted and effective preparation.
50+ Visual Study Cards: Accelerate your learning with comprehensive study cards covering all key Google Cloud data services and their critical exam points.
Topics covered include ():
Data Preparation and Ingestion (ETL/ELT methodologies, data transfer tools, storage solutions)
Data Analysis and Presentation (BigQuery analytics, Looker dashboards, ML models)
Data Pipeline Orchestration (Dataflow, Cloud Composer, automation and monitoring)
Data Management (Access control, lifecycle management, security and compliance)
Why choose this course? Gain the confidence to tackle the certification exam by identifying your knowledge gaps, reinforcing key concepts, and mastering the nuances of data engineering in the Google Cloud ecosystem.
Ready to validate your data practitioner skills?
Start practicing today!
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This course is not affiliated with, endorsed by, or sponsored by Google Cloud Platform (GCP) or Google LLC. Google Cloud and all Google product names are trademarks of Google LLC. All logos and trademarks are used for educational and identification purposes only.
This course contains the use of artificial intelligence.
Course Content
- 1 section(s)
- Section 1 Practice Tests
What You’ll Learn
- Pass the GCP – ADP (Associate Data Practitioner) Exam on Your First Attempt
- In-Depth Explanations + Direct Links to GCP Resources
- 50+ GCP Study Cards Covering the Most Important Services
- Differentiate Between OLAP and OLTP Systems - Understand analytical vs transactional workloads and when to use each.
- Describe the core responsibilities of a data engineer in building and maintaining data pipelines to enable reliable data analysis.
- Compare the characteristics of SQL and NoSQL databases in terms of schema, scalability, and data model to determine the appropriate use case for each.
- Contrast ETL and ELT data pipelines to identify the key differences in their data transformation stages.
- Match core Google Cloud services like Pub/Sub, Dataflow, and Cloud Storage to specific data ingestion requirements like streaming, batch, and file uploads.
- Explain the key features of BigQuery, including its serverless architecture, columnar storage, and federated queries, for large-scale data analytics.
- Differentiate between the main Google Cloud Storage classes (Standard, Nearline, Coldline, Archive) to optimize for cost and data access frequency.
- Explain the primary use case for Dataproc as a managed service for running Apache Spark and Hadoop jobs on Google Cloud.
- Describe the role of Eventarc in building event-driven architectures by routing events from Google Cloud sources to trigger services like Cloud Run.
- Describe the purpose of Cloud Data Fusion for visually building and managing code-free ETL/ELT data pipelines on Google Cloud.
- Explain how Dataflow is used to run scalable, unified data processing pipelines for both streaming and batch data using Apache Beam.
- Explain how the BigQuery Data Transfer Service automates the scheduled ingestion of data from SaaS applications and other cloud services into BigQuery.
- Describe the use of Datastream for capturing and delivering real-time data changes from source databases to Google Cloud destinations.
- Apply basic SQL clauses including JOIN, WHERE, GROUP BY, and ORDER BY to query, filter, aggregate, and sort data from a relational database.
- Describe the function of Pub/Sub as an asynchronous messaging service for ingesting event streams and building decoupled, scalable applications.
- Describe how Storage Transfer Service is used to manage and automate large-scale data migrations from online and on-premises sources to Google Cloud Storage.
- Use the gcloud storage cp command to copy files and directories between your local file system and a Google Cloud Storage bucket.
- Identify when to use the Transfer Appliance for a high-capacity, offline data transfer when network bandwidth is a limiting factor.
- Explain how to apply the principle of least privilege using IAM roles to manage user access to data resources in Google Cloud.
- Describe the function of Looker as a business intelligence platform for data modeling, analysis, and creating interactive visualizations.
- Differentiate between data in transit and data at rest, and explain how Google Cloud encrypts both by default.
- Differentiate between Cloud Composer and Cloud Scheduler based on their use cases for complex workflow orchestration versus simple, time-based job scheduling.
- Compare the characteristics of CSV, JSON, Avro, and Parquet file formats in terms of schema, storage structure, and their optimal use cases in big data pipeline
- Differentiate between BigQuery ML and Vertex AI AutoML for creating models inside BigQuery with SQL versus building them with a graphical interface for broader
Skills covered in this course
Reviews
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EEsra Saif
very good, i like it. manyyy thanks
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IIvailostefanov
The test is a good opportunity to prepare for the exam because it has excellent coverage of the material.
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OOleksandr Parniuk
I highly recommend this pack of mock exam questions as an excellent tool to prepare for the real exam, which can be quite challenging even for experienced professionals. As with all of Vladimir’s courses and mock exam packs, this one is carefully structured and accurate, and has everything you need to maximise your chances to pass the exam with the first attempt. Use it to identify and close knowledge gaps, as well as to boost your confidence before taking the exam. Good luck!
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SSergey Taran
Well-designed practice tests that mirror the real exam format. The coverage across all topics is thorough, and the explanations make complex concepts easy to understand. A great way to assess readiness and feel confident walking into the actual certification exam.
