Preparing for the Professional Cloud Architect Examination

Students in this course will prepare for the Professional Cloud Architect Certification Exam. They will rehearse useful skills including exam question reasoning and case comprehension, tips and review of topics from the Infrastructure curriculum.

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Learning Objectives

Candidates will be able to identify skill gaps and further areas of study. Candidates will also be directed to appropriate target learning resources.

 

Course Details

Course Outline

1 - Google Cloud Dataproc Overview
  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes.
  • Scaling and deleting Clusters.
  • Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
  • 2 - Running Dataproc Jobs
  • Running Pig and Hive jobs.
  • Separation of storage and compute.
  • Lab: Running Hadoop and Spark Jobs with Dataproc.
  • Lab: Submit and monitor jobs.
  • 3 - Integrating Dataproc with Google Cloud Platform
  • Customize cluster with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.
  • 4 - Making Sense of Unstructured Data with Google’s Machine Learning APIs
  • Google’s Machine Learning APIs.
  • Common ML Use Cases.
  • Invoking ML APIs.
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis.
  • 5 - Serverless data analysis with BigQuery
  • What is BigQuery.
  • Queries and Functions.
  • Lab: Writing queries in BigQuery.
  • Loading data into BigQuery.
  • Exporting data from BigQuery.
  • Lab: Loading and exporting data.
  • Nested and repeated fields.
  • Querying multiple tables.
  • Lab: Complex queries.
  • Performance and pricing.
  • 6 - Serverless, autoscaling data pipelines with Dataflow
  • The Beam programming model.
  • Data pipelines in Beam Python.
  • Data pipelines in Beam Java.
  • Lab: Writing a Dataflow pipeline.
  • Scalable Big Data processing using Beam.
  • Lab: MapReduce in Dataflow.
  • Incorporating additional data.
  • Lab: Side inputs.
  • Handling stream data.
  • GCP Reference architecture.
  • 7 - Getting started with Machine Learning
  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization.
  • Lab: Explore and create ML datasets.
  • 8 - Building ML models with Tensorflow
  • Getting started with TensorFlow.
  • Lab: Using tf.learn.
  • TensorFlow graphs and loops + lab.
  • Lab: Using low-level TensorFlow + early stopping.
  • Monitoring ML training.
  • Lab: Charts and graphs of TensorFlow training.
  • 9 - Scaling ML models with CloudML
  • Why Cloud ML?
  • Packaging up a TensorFlow model.
  • End-to-end training.
  • Lab: Run a ML model locally and on cloud.
  • 10 -
  • Feature Engineering
  • Creating good features.
  • Transforming inputs.
  • Synthetic features.
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.
  • 11 - Architecture of streaming analytics pipelines
  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline.
  • 12 - Ingesting Variable Volumes
  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions.
  • Lab: Simulator.
  • 13 - Implementing streaming pipelines
  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
  • Lab: Stream data processing pipeline for live traffic data.
  • 14 - Streaming analytics and dashboards
  • Streaming analytics: from data to decisions.
  • Querying streaming data with BigQuery.
  • What is Google Data Studio?
  • Lab: build a real-time dashboard to visualize processed data.
  • 15 - High throughput and low-latency with Bigtable
  • What is Cloud Spanner?
  • Designing Bigtable schema.
  • Ingesting into Bigtable.
  • Lab: streaming into Bigtable.
  • Actual course outline may vary depending on offering center. Contact your sales representative for more information.

    Who is it For?

    Target Audience

    This course is intended for the following participants:

    Cloud professionals who intend to take the Professional Cloud Architect certification exam.

    Other Prerequisites

    Cloud professionals who intend to take the Professional Cloud Architect certification exam Must have attended Architecting with GCP: Infrastructure course or equivalent on demand courses. Knowledge and experience with GCP, equivalent to GCP Architecting Infrastructure Knowledge of cloud solutions, equivalent to GCP Design and Process Industry experience with cloud computing

    Preparing for the Professional Cloud Architect Examination

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    Course Length : 1 Day (8 Hours)

    There are currently no scheduled dates for this course. Please contact us for more information.

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