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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%	- Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Recovering from failures 3. Planning for monitoring and alerting - Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Scheduling jobs 3. Workflow orchestration
Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Data encryption 2. Auditing, privacy, and compliance 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 - Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases
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. AI data enrichment 2. Integrating with new data sources 3. Data acquisition and import 4. Streaming (e.g., windowing, late arriving data) 5. Batch processing 6. Processing logic - Designing data processing resources 1. Cost optimization 2. Cluster sizing and autoscaling 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
Storing the data (~20% of the exam)	20%	- Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Designing the data model 3. Mapping business requirements 4. Deciding the degree of data normalization - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data - 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

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Free PDF Quiz 2026 Marvelous Professional-Data-Engineer: Google Certified Professional Data Engineer Exam Valid Exam Testking

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Google Certified Professional Data Engineer Exam Sample Questions (Q360-Q365):

NEW QUESTION # 360
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Add capacity (memory and disk space) to the database server by the order of 200.
B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: B

Explanation:
Explanation

NEW QUESTION # 361
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

A. categorical_column_with_vocabulary_list
B. categorical_column_with_unknown_values
C. sparse_column_with_keys
D. categorical_column_with_hash_bucket

Answer: D

Explanation:
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance?
Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference:
https://www.tensorflow.org/tutorials/wide

NEW QUESTION # 362
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

A. Write a job template in Cloud Dataproc to perform the data transfer.
B. Use Cloud Dataflow and write the data to Cloud Storage.
C. Execute gsutil rsync from the on-premises servers.
D. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

Answer: C

NEW QUESTION # 363
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger that is a combination of other triggers
B. Trigger based on element size in bytes
C. Trigger based on element count
D. Trigger based on time

Answer: B

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 364
You need to choose a database to store time series CPU and memory usage for millions of computers.
You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

A. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
C. Create a table in BigQuery, and append the new samples for CPU and memory to the table
D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Answer: A

Explanation:
https://cloud.google.com/bigtable/docs/schema-design-time-series

NEW QUESTION # 365
......

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