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Google Cloud Associate Data Practitioner Exam Cram

Name: Google Cloud Associate Data Practitioner Exam Cram
Rating: 4.7 (7 reviews)

Unlock Your Data Career Potential: Master the Google Cloud Associate Data Practitioner Certification

Created byJoseph Holbrook

Last updated 4/2025

English

English [Auto],

What you'll learn

Master the Exam Objectives: Demystify the GCP Professional Data Engineer certification exam and understand its core components.
Implement Data Engineering Best Practices: Apply proven strategies for designing and building efficient, reliable data pipelines on GCP.
rchestrate Data Ingestion & Storage: Seamlessly extract and load data into diverse GCP storage solutions, including Cloud Storage and Bigtable.
Navigate GCP's Data Storage Ecosystem: Choose the right storage services for structured (Cloud SQL, Spanner, BigQuery), unstructured (Cloud Storage, Filestore),
Fortify Your Data Security & Compliance: Implement robust security measures and ensure adherence to stringent data privacy regulations.
Leverage Powerful Data Management Tools: Master DataPrep and Data Catalog for effective data governance.
Visualize Data Insights: Transform raw data into compelling visualizations using Data Studio.
ngineer High Availability & Disaster Recovery: Design resilient data solutions for Cloud Storage and Cloud SQL.
Harness the Power of AI/ML: Explore BigQuery ML and AutoML for advanced data analysis.

Course content

6 sections • 54 lectures • 4h 40m total length

Course Trailer1:18
Unlock the Google Cloud data universe and earn the Associate Data Practitioner certification with a strategic roadmap to master cutting edge skills, transform industries, and advance your data career.
Course Introduction3:31
Acquire foundational knowledge for the Google Cloud Associate Data Practitioner exam cram through a course introduction to data management on Google Cloud, data warehouses, ETL and ELT, and Looker dashboards.
What is the GCP Associate Data Practitioner Certification?1:50
Understand Google Cloud associate data practitioner certification, which validates the ability to manage, analyze, and visualize data on Google Cloud, with six months of experience and focus on BigQuery.
Associate Data Practitioner Certification Exam Objectives5:39
Identify the four exam objective domains for the Google Cloud associate data practitioner certification, and review key topics and services such as BigQuery, Dataflow, Looker, and Data Studio.
Certification Exam Process1:31
Prepare for the associate data practitioner exam—50–60 questions in two hours, $125 fee, web proctoring, ID checks, closed-book with an erasable whiteboard, and you will know pass/fail after submission.
Downloadable Course Resources0:57
Please feel free to download course presentation, etc
Module Review Quiz

Module Two Overview1:03
Explore the data practitioner role, data preparation and ingestion in Google Cloud, covering data quality, cleaning, profiling, formats, structures, BigQuery demos, and storage options such as Cloud SQL and Spanner.
Discussion - Importance of Data Practitioners2:19
Lets discuss why becoming a data practitioner is important and why the Google Cloud exam is a great start.
Data Quality Fundamentals6:19
Understand data quality fundamentals, including accuracy, completeness, consistency, validity, timeliness, uniqueness, and learn to enforce quality using data flow, Dataprep, BigQuery, Cloud Data Fusion, data catalog, Vertex AI, and monitoring.
Data Transfer Tools (Storage Transfer Service and Transfer Appliance)5:26
Data Cleansing and Profiling7:48
Explore data profiling and cleansing to ensure consistent, high-quality data with proper security as you discover, profile, cleanse, and standardize data structures for BigQuery using dataplex.
Data Transfer Formats5:50
Assess data transfer formats for Google Cloud by weighing use case and service support. Use Parquet, Avro, or ORC for analytics; CSV or JSON for readability, with Dataflow templates.
Data Structures, Files and Types6:02
Explore data types, structures, formats, and fields used in Google Cloud handling. Differentiate qualitative and quantitative data, and structured, semi-structured, and unstructured data with formats like JSON, CSV, and Parquet.
Whiteboard - Mapping business requirements to Use Cases6:41
BigQuery Overview16:16
Explore BigQuery, a serverless, columnar data warehouse with separate storage and compute, enabling scalable batch and streaming queries, machine learning with sql, and federated data access.
Demonstration - BigQuery9:43
Demonstrates loading data into BigQuery from cloud storage, local files, streaming inserts, and data transfer service; use bq command line, API, and sheets, then query, save, and export results.
Cloud SQL and Spanner4:31
Compare Cloud SQL and Cloud Spanner by contrasting relational and NoSQL data models, strong and eventual consistency, scalability, and data import options such as cloud storage import and migration services.
Understanding Storage on GCP9:04
Understand Google Cloud storage options and choose the right storage for cost, speed, and regional needs across Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, and Spanner.
Module Two Review2:32
Let's go thru a short review of the main topics in this module.
Module Two Review Questions2:58
Identify BigQuery as the data warehouse with a tabular schema, apply data modeling to design the schema, then use profiling to spot missing values, outliers, and data distribution patterns.
Module Two Review Quiz

Module Three Overview1:12
Explore data pipeline orchestration in Google Cloud by evaluating etl and elt use cases to select transformation tools, demonstrate Dataflow pipelines, scheduled queries, Cloud Logging, Cloud Monitoring, and Eventarc.
Selecting a data transformation tool in Google Cloud9:31
Select the right Google Cloud tool by weighing volume, velocity, and complexity, then compare Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, and Dataform for real-time, batch, and governance needs.
Evaluate use cases for ETL/ELT9:39
Compare etl and elt approaches, evaluate use cases for data pipelines and requirements, and decide when to transform before or after loading in google cloud with bigquery and dataflow.
Choose products required to implement transformation pipelines5:54
Identify Google Cloud Platform services to implement ETL and ELT pipelines, matching use cases to Cloud Composer, Cloud Data Fusion, Dataflow, Data Prep, Cloud Pub Sub, Cloud Storage, and BigQuery.
Demonstration: Monitor Dataflow pipeline progress using the Dataflow job UI3:16
How Can you Monitor your Dataflow Pipeline?
Some Examples:

●Set up an alert in Cloud Monitoring to notify you if the error rate of your pipeline exceeds a certain threshold.
●Create a dashboard in Cloud Monitoring to visualize the throughput and latency of your pipeline.
●Use Cloud Logging to search for specific error messages in your pipeline logs.
●Use the Dataflow Monitoring UI to view the execution graph of your pipeline and identify performance bottlenecks.
Demonstration Scenario
●
We’ll walk thru some important concepts for monitoring a Dataflow pipeline.

•Dataflow UI
Demonstration: Review and analyze logs in Cloud Logging and Cloud Monitoring5:32
Demonstration Scenario
We’ll dive into Cloud Monitoring and Logging, review logs, and walk through monitoring dashboards.
Whiteboard Discussion: Select a data orchestration solution2:29
We’ll walk through a customer challenge scenario and discuss which GCP tools would be the best or correct choices for event-driven data ingestion
Whiteboard Discussion- Identify use cases for event-driven data ingestion4:45
We’ll walk thru various features and then create a trigger event in Eventarc

This upload event will trigger a Cloud Function via Eventarc, which will then process the image and store the processed image in another Cloud Storage bucket
Demonstration - Use Eventarc triggers in event-driven pipelines5:35
We’ll walk thru various features and then create a trigger event in Eventarc

This upload event will trigger a Cloud Function via Eventarc, which will then process the image and store the processed image in another Cloud Storage bucket.
Module Review3:12
Let's go thru a short review of the main topics in this module.
Module Three Review Questions3:35
Migrate from on-prem to cloud with a full load first, then a delta load for updates, and use dataflow for streaming from pub/sub into bigquery with cloud functions.
Module Three Review Quiz

Module Overview1:25
Identify trends and insights with BigQuery and generate reports via SQL queries. Visualize data, build dashboards, compare Looker and Looker Studio, and train, evaluate, and perform inference with BigQuery ML.
Identify data trends, patterns, and insights by using BigQuery4:56
Explore using Google Cloud BigQuery with Jupyter notebooks to uncover data trends, patterns, and insights by connecting datasets through pandas and the BigQuery client in an interactive notebook environment.
Demonstration: Define and execute SQL queries in BigQuery to generate reports a8:16
Demonstration Scenario

We’ll walk through how to use BigQuery for generating queries.

We will use sample data to run queries, generate reports, and then visualize that data.
Demonstration: Visualize data and create dashboards in Looker given business req7:20
Learn to visualize data and build dashboards with Looker and Looker Studio, using LookML for modeling, exploring data from BigQuery, and creating scheduled, shareable reports.
ML Overview6:02
Define, train, evaluate, and use ML models6:02
Execute SQL to create, train, and evaluate models using BigQuery ML8:05
Execute sql to create, train, and evaluate models with BigQuery ML, using linear regression, logistic regression, and k means clustering, and learn the SQL functions that drive this process.
Perform Inference with BigQuery ML3:00
Organize Models in Model Registry2:26
Whiteboard Discussion: Plan an actual ML Project9:48
Walk through planning and deploying a machine learning project on Google Cloud, defining smart objectives, data sources, preprocessing, model selection and evaluation, deployment, monitoring, and ML ops with Vertex AI.
Module Summary2:05
Module Four Review Questions4:24
Explore Looker Studio data sources, understand cross validation for model evaluation, and compare binary classification with logistical regression, highlighting training/test splits.
Module Four Review Quiz

Module Five Overview0:56
Explore data management on Google Cloud by applying identity and access management to secure environments and control cloud storage access, review lifecycle management, replication and high availability, encryption, archiving options.
Identity and Access Management (IAM)9:28
Master identity and access management in Google Cloud by applying least-privileged access through roles and policies, and crafting custom roles and service accounts for BigQuery and storage.
Cloud Storage Access Management10:16
Demonstration - Analytics Hub5:43
Explore Analytics Hub as an exchange platform for sharing data assets, publishing datasets to the marketplace, and subscribing to third-party data with enhanced security and governance.
Demonstration - Lifecycle Management – Google Cloud Storage10:16
Compare public and private access controls in Google Cloud Storage, implement uniform bucket level access, and manage permissions with IAM roles and service accounts for data lakes and warehouses.
Whiteboard Discussion - Replication and High Availability6:49
Discuss how regions and zones influence replication and high availability in Google Cloud, using Cloud Storage, cross region replication, and Cloud SQL read replicas for durability and compliance.
Comparing Replication and Migration Services4:34
Compare replication and migration on Google Cloud using Cloud SQL, Spanner, and Cloud Storage to achieve high availability, read scalability, and one-time data migration options.
Encryption Fundamentals6:21
Explore encryption fundamentals on google cloud, including data at rest and in transit, data in use, de-identification, aes 256, tls, kms, and hardware security module options.
Module Summary2:13
Module Five Review Questions3:21
Explore Cloud SQL recovery options, highlighting point-in-time recovery for restoring data after accidental deletion or corruption, and compare read replicas, failover replicas, cross-region replicas for availability and disaster recovery.
Module Five Review Quiz

Requirements

olid understanding of fundamental data management, engineering, and privacy concepts.
At least six months of practical experience with GCP services (networking, security, storage).
Hands-on experience with SQL databases and relational database concepts

Description

Course Description

Are you ready to transform your career and become a sought-after data professional? This comprehensive course provides the focused training you need to conquer the Google Cloud Associate Data Practitioner certification and elevate your expertise in the booming field of cloud data engineering.

Why This Certification Matters:

Stand Out in a Competitive Market: In a data-driven world, employers are actively seeking certified professionals. This certification validates your fundamental knowledge of Google Cloud data services, instantly setting you apart from the competition.
Boost Your Earning Power: Industry studies consistently show that IT certifications, particularly those from leading cloud providers such as Google Cloud, are associated with higher salaries. Invest in your future and unlock your earning potential.
Gain Real-World Google Cloud Expertise: This course goes beyond theory, providing hands-on experience with Google Cloud Platform (GCP), the industry-leading platform for cloud data services. Whether you aspire to work at Google or any organization leveraging GCP, this practical experience is invaluable.
Massive Job Market Opportunity: With over 12,000 data engineer jobs currently listed on LinkedIn in the USA alone and the Google Cloud Data Engineer certification consistently ranked as one of the most valuable certifications, your skills are in high demand.

Who Should Enroll:

This course is designed for aspiring data professionals with a foundational understanding of data concepts and at least six months of practical experience with Google Cloud services. If you have a background in computer science, statistics, informatics, information systems, or any quantitative field and are eager to advance your data career, this course is for you.

What You Will Achieve:

Upon completion of this course, you will be able to:

Master the Exam Objectives: Confidently navigate the GCP Associate Data Practitioner certification exam by thoroughly understanding its objectives.
Apply Google Cloud Data Engineering Best Practices: Implement effective data engineering strategies tailored to the Google Cloud environment.
Efficiently Extract and Load Data: Design and execute data ingestion processes into appropriate Google Cloud storage systems.
Select and Utilize Key GCP Storage Services: Leverage Cloud Storage and Bigtable for optimal data storage solutions.
Choose the Right Services for Structured Data: Implement Cloud SQL, Cloud Spanner, and BigQuery for structured data management.
Effectively Manage Unstructured Data: Utilize Cloud Storage and Filestore for unstructured data storage and processing.
Optimize Semi-Structured Data Handling: Employ Cloud Datastore and Bigtable for efficient semi-structured data management.
Implement Robust Data Security and Compliance: Apply security measures and ensure compliance with data privacy regulations.
Utilize Powerful GCP Data Management Tools: Master tools like DataPrep and Data Catalog for effective data management.
Create Compelling Data Visualizations: Utilize GCP services like Data Studio to transform data into actionable insights.
Design High Availability and Disaster Recovery Strategies: Implement resilient data solutions in Cloud Storage and Cloud SQL.
Leverage AI/ML Services: Gain practical insights into BigQuery ML and AutoML for advanced data analysis.

Prerequisites:

To maximize your learning experience, you should have:

Familiarity with foundational data concepts, including data management, data engineering, and data privacy.
At least six months of hands-on experience with Google Cloud services, including networking, security, and storage.
Practical experience with SQL databases and relational database concepts.

Your Next Steps:

After completing this course, you can further enhance your expertise by:

Enrolling in advanced data engineering courses to delve deeper into complex concepts.
Exploring AI services within Google Cloud to leverage cutting-edge technologies.

Enroll now and take the definitive step towards becoming a certified Google Cloud Data Practitioner!

Who this course is for:

Anyone who is looking to pass this exam
Data professionals such as data analysts, data practitioners, data engineers
IT professionals wishing to level up their data skills

Google Cloud Associate Data Practitioner Exam Cram

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 15min

Data Preparation and Ingestion14 lectures • 1hr 27min

Data Pipeline Orchestration (65 minutes)11 lectures • 55min

Data Analysis and Presentation12 lectures • 1hr 4min

Data Management10 lectures • 1hr

Course Closeout and Practice Test1 lecture • 1min

Requirements

Description

Who this course is for: