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所在平台: Udemy |
课程主页: https://www.udemy.com/course/gcp-google-professional-data-engineer-practice-test-2023/
课程评论：没有评论
课程名称：GCP Google专业数据工程师实践测试2023 课程概述：本课程旨在帮助学员在首次尝试时通过GCP Google专业数据工程师考试，成为获得认证的专业数据工程师。每道题都有详细的解释，阐明正确选项的原因以及其他选项的错误之处。所有练习考试中的问答都是经过精心设计和更新，旨在符合专业数据工程师认证考试的要求。根据谷歌的考试要求，最低及格分数为70%。 课程内容简介： 1. **设计数据处理系统** - 选择合适的存储技术 - 设计数据管道 - 设计数据处理解决方案 - 数据仓库和数据处理的迁移理念 2. **构建和运营数据处理系统** - 构建和运营存储系统 - 构建和运营数据管道 - 构建和运营处理基础设施 3. **机器学习模型的运营** - 利用预构建的ML模型作为服务 - 部署ML管道 - 选择合适的训练和服务基础设施 - 测量、监控与故障排除机器学习模型 4. **确保解决方案质量** - 设计安全与合规性 - 确保可扩展性和效率 - 确保可靠性和准确性 - 确保灵活性和可移植性 该课程为准备谷歌专业数据工程师考试的学习者提供了一个全面的复习平台，帮助他们掌握数据处理、机器学习和解决方案设计的技能，保证能够有效应对认证考试的挑战。
Pass your Professional Data Engineer exam on the first attempt and become GCP Google Certified Professional Data Engineer. Every question has a detailed explanation of why an option is correct and why the other options are wrong. All questions and answers in these practice exams have been carefully design and updated to be fit for Professional Data Engineer Certification exam.Exam details according to Google, require minimum 70% score to pass:Section 1: Designing data processing systems1.1 Selecting the appropriate storage technologies. Considerations include: ● Mapping storage systems to business requirements ● Data modeling ● Trade-offs involving latency, throughput, transactions ● Distributed systems ● Schema design1.2 Designing data pipelines. Considerations include: ● Data publishing and visualization (e.g., BigQuery) ● Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka) ● Online (interactive) vs. batch predictions ● Job automation and orchestration (e.g., Cloud Composer)1.3 Designing a data processing solution. Considerations include: ● Choice of infrastructure ● System availability and fault tolerance ● Use of distributed systems ● Capacity planning ● Hybrid cloud and edge computing ● Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions) ● At least once, in-order, and exactly once, etc., event processing1.4 Migrating data warehousing and data processing. Considerations include: ● Awareness of current state and how to migrate a design to a future state ● Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking) ● Validating a migrationSection 2: Building and operationalizing data processing systems2.1 Building and operationalizing storage systems. Considerations include: ● Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore) ● Storage costs and performance ● Life cycle management of data2.2 Building and operationalizing pipelines. Considerations include: ● Data cleansing ● Batch and streaming ● Transformation ● Data acquisition and import ● Integrating with new data sources2.3 Building and operationalizing processing infrastructure. Considerations include: ● Provisioning resources ● Monitoring pipelines ● Adjusting pipelines ● Testing and quality controlSection 3: Operationalizing machine learning models3.1 Leveraging pre-built ML models as a service. Considerations include: ● ML APIs (e.g., Vision API, Speech API) ● Customizing ML APIs (e.g., AutoML Vision, Auto ML text) ● Conversational experiences (e.g., Dialogflow)3.2 Deploying an ML pipeline. Considerations include: ● Ingesting appropriate data ● Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML) ● Continuous evaluation3.3 Choosing the appropriate training and serving infrastructure. Considerations include: ● Distributed vs. single machine ● Use of edge compute ● Hardware accelerators (e.g., GPU, TPU)3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include: ● Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics) ● Impact of dependencies of machine learning models ● Common sources of error (e.g., assumptions about data)Section 4: Ensuring solution quality4.1 Designing for security and compliance. Considerations include: ● Identity and access management (e.g., Cloud IAM) ● Data security (encryption, key management) ● Ensuring privacy (e.g., Data Loss Prevention API) ● Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))4.2 Ensuring scalability and efficiency. Considerations include: ● Building and running test suites ● Pipeline monitoring (e.g., Cloud Monitoring) ● Assessing, troubleshooting, and improving data representations and data processing infrastructure ● Resizing and autoscaling resources4.3 Ensuring reliability and fidelity. Considerations include: ● Performing data preparation and quality control (e.g., Dataprep) ● Verification and monitoring ● Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis) ● Choosing between ACID, idempotent, eventually consistent requirements4.4 Ensuring flexibility and portability. Considerations include: ● Mapping to current and future business requirements ● Designing for data and application portability (e.g., multicloud, data residency requirements) ● Data staging, cataloging, and discovery
