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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Identity and Access Management (IAM) 2. Data encryption 3. Auditing, privacy, and compliance - 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
Topic 3: Maintaining and automating data workloads (~15% of the exam)	15%	- Automating data processes 1. Workflow orchestration 2. Continuous integration and continuous deployment (CI/CD) 3. Scheduling jobs - Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Designing for reliability and fidelity 1. Recovering from failures 2. Planning for monitoring and alerting 3. Performing data quality and validation checks
Topic 4: Designing data processing systems (~30% of the exam)	30%	- 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) - Designing data pipelines 1. Data acquisition and import 2. Batch processing 3. Integrating with new data sources 4. AI data enrichment 5. Processing logic 6. Streaming (e.g., windowing, late arriving data)
Topic 5: Storing the data (~20% of the exam)	20%	- 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. Analyzing data access patterns 2. Lifecycle management of data 3. Planning for storage costs and performance - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Processing data 3. Monitoring the data lake - Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Mapping business requirements 3. Deciding the degree of data normalization 4. Designing the data model

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Google Certified Professional Data Engineer Exam Sample Questions (Q339-Q344):

NEW QUESTION # 339
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

A. Rewrite your models on TensorFlow, and start using Cloud ML Engine
B. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
C. Use Cloud ML Engine for training existing Spark ML models
D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Answer: C

Explanation:
Explanation

NEW QUESTION # 340
You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying dat
a. Which query type should you use?

A. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
B. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
C. Include ORDER BY DESK on timestamp column and LIMIT to 1.
D. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Answer: A

Explanation:
https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts

NEW QUESTION # 341
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster.
You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?

A. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
B. Move your data to Cloud Storage. Run your jobs on Dataproc.
C. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
D. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

Answer: B

NEW QUESTION # 342
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.
How should you optimize the cluster for cost?

A. Use pre-emptible virtual machines (VMs) for the cluster
B. Use SSDs on the worker nodes so that the job can run faster
C. Use a higher-memory node so that the job runs faster
D. Migrate the workload to Google Cloud Dataflow

Answer: D

NEW QUESTION # 343
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
B. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
C. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
D. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

Answer: C

NEW QUESTION # 344
......

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