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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Professional-Data-Engineer PDF & Professional-Data-Engineer Fragen Antworten

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q17-Q22):

17. Frage
You are designing a data lake on Google Cloud to store vast amounts of customer interaction data from various sources, such as websites, mobile apps, and social media. You need to ensure that this data, which arrives in different formats, is consistently cataloged and easy for data analysts to discover and use. You also want to perform basic data quality checks and transformations before the data is consumed by downstream applications. You need an automated and managed data governance solution. What should you do?

A. Use Cloud Storage as the central repository. Use a Cloud Run function to catalog, transform the data, and perform data quality checks.
B. Use Cloud Storage and BigQuery as repositories. Use Dataplex Universal Catalog for metadata discovery, data quality checks, and transformations.
C. Stream all the data directly into BigQuery, where it is automatically cataloged and governed.
D. Use Cloud Storage as the central repository. Use Vertex AI to classify and process the data and perform data quality checks.

Antwort: B

Begründung:
Dataplex provides a managed data governance solution with a unified catalog, automated metadata discovery, and support for data quality checks and transformations. Using Cloud Storage and BigQuery as repositories allows analysts to discover and consume data consistently across the data lake.

18. Frage
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A. Field promotion
B. Salting
C. Randomization
D. Hashing

Antwort: A

Begründung:
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

19. Frage
You are analyzing the price of a company's stock. Every 5 seconds, you need to compute a moving average of the past 30 seconds' worth of data. You are reading data from Pub/Sub and using DataFlow to conduct the analysis. How should you set up your windowed pipeline?

A. Use a fixed window with a duration of 30 seconds. Emit results by setting the following trigger:
AfterWatermark.pastEndOfWindow().plusDelayOf(Duration.standardSeconds(5))
B. Use a sliding window with a duration of 30 seconds and a period of 5 seconds.Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow()
C. Use a fixed window with a duration of 5 seconds. Emit results by setting the following trigger:
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(30))
D. Use a sliding window with a duration of 5 seconds. Emit results by setting the following trigger:
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(30))

Antwort: B

20. Frage
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

A. [1, 0, 0, 0, 0, 0, 0]
B. [0, 1]
C. [0, 0, 0, 1, 0, 0, 1]
D. [0, 5, 0, 0, 0, 0]

Antwort: A,B

Begründung:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and
1s.
Reference:
https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

21. Frage
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

A. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
B. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
C. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
D. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

Antwort: A

Begründung:
https://cloud.google.com/sql/docs/mysql/high-availability

22. Frage
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