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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Scheduling jobs
  • 2. Workflow orchestration
  • 3. Continuous integration and continuous deployment (CI/CD)
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Planning for monitoring and alerting
  • 3. Recovering from failures
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Storing the data (~20% of the exam)20%- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Deciding the degree of data normalization
  • 3. Mapping business requirements
  • 4. Designing the data model
- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Planning for storage costs and performance
  • 3. Lifecycle management of data
- Using a data lake
  • 1. Managing the lake (data discovery, access, cost controls)
  • 2. Monitoring the data lake
  • 3. Processing data
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
- Performing security considerations
  • 1. Identity and Access Management (IAM)
  • 2. Auditing, privacy, and compliance
  • 3. Data encryption
Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Batch processing
  • 2. AI data enrichment
  • 3. Processing logic
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. Data acquisition and import
  • 6. Integrating with new data sources
- Designing data processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements

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Google Certified Professional Data Engineer Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
* The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
* Support for publish/subscribe semantics on hundreds of topics
* Retain per-key ordering
Which system should you choose?

Answer: D

Explanation:
These are the functionalities which are currently lagging/not-available with Pub/Sub.


NEW QUESTION # 205
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

Answer: D

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.


NEW QUESTION # 206
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority
of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the
cost of queries, your organization created a view called events, which queries only the last 14 days of
data. The view is described in legacy SQL. Next month, existing applications will be connecting to
BigQuery to read the eventsdata via an ODBC connection. You need to ensure the applications can
connect. Which two actions should you take? (Choose two.)

Answer: A,B


NEW QUESTION # 207
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Answer: C

Explanation:
Reference https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877


NEW QUESTION # 208
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

Answer: B

Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
Cloud Data Fusion:
Cloud Data Fusionis a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user- friendly GUI for pipeline creation and management.
Custom Encryption Keys:
Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
Centralizing Data:
Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
Set Up Cloud Data Fusion:
Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
Create ETL Pipelines:
Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage.
Configure the pipelines to use your custom encryption keys.
Run and Monitor Pipelines:
Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference Links:
Cloud Data Fusion Documentation
Using Customer-Managed Encryption Keys (CMEK)


NEW QUESTION # 209
......

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