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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q253-Q258):

NEW QUESTION # 253
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
B. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
C. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Answer: B

Explanation:
The recommendation requires filtering based on several TB of data, therefore BigTable is the recommended option vs Cloud SQL which is limited to 10TB.

NEW QUESTION # 254
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

A. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.
Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
B. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
C. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
D. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

Answer: D

NEW QUESTION # 255
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A. The original order identification number from the sales system, which is a monotonically increasing integer
B. A random universally unique identifier number (version 4 UUID)
C. A concatenation of the product name and the current epoch time
D. The current epoch time

Answer: B

Explanation:
References:

NEW QUESTION # 256
Which of the following is not true about Dataflow pipelines?

A. Pipelines are a set of operations
B. Pipelines can share data between instances
C. Pipelines represent a directed graph of steps
D. Pipelines represent a data processing job

Answer: B

Explanation:
Explanation
The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms Reference: https://cloud.google.com/dataflow/model/pipelines

NEW QUESTION # 257
You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA.
You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?

A. Use session windows with a 30-mmute gap duration.
B. Use session windows with a 15-minute gap duration.
C. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
D. Use hopping windows with a 15-mmute window, and a thirty-minute period.

Answer: C

Explanation:
Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary. Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity. Reference:
Windowing concepts
Session windows
Windowing in Dataflow

NEW QUESTION # 258
......

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