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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Migrate your data to multi-region BigQuery buckets.
B. Create a BigQuery table snapshot on a daily basis.
C. Access historical data by using time travel in BigQuery.
D. Export the data from BigQuery into a new table that excludes the corrupted data

Answer: C

NEW QUESTION # 125
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the WHERE_PARTITIONTIME pseudo column
B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
C. Use the TABLE_DATE_RANGE function
D. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD

Answer: C

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-app

NEW QUESTION # 126
Cloud Bigtable is Google's ______ Big Data database service.

A. SQL Server
B. mySQL
C. NoSQL
D. Relational

Answer: C

Explanation:
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail.
It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.

NEW QUESTION # 127
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service
in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If
there are any concerns about a transmission, the system re-transmits the data. How should you
deduplicate the data most efficiency?

A. Compute the hash value of each data entry, and compare it with all historical data.
B. Assign global unique identifiers (GUID) to each data entry.
C. Store each data entry as the primary key in a separate database and apply an index.
D. Maintain a database table to store the hash value and other metadata for each data entry.

Answer: D

NEW QUESTION # 128
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
B. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now on.
C. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
D. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMPtype.
Reload the data.
E. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEWto true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause ensuring that the value of IS_NEWmust be true.

Answer: E

NEW QUESTION # 129
......

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