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

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Google Certified Professional Data Engineer Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are training a spam classifier. You notice that you are overfitting the training dat
a. Which three actions can you take to resolve this problem? (Choose three.)

A. Use a smaller set of features
B. Use a larger set of features
C. Increase the regularization parameters
D. Decrease the regularization parameters
E. Get more training examples
F. Reduce the number of training examples

Answer: B,D,E

NEW QUESTION # 27
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

A. Assign the users/groups data viewer access at the table level for each table
B. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
C. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
D. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views

Answer: C

NEW QUESTION # 28
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. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
B. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
C. Add capacity (memory and disk space) to the database server by the order of 200.
D. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

Answer: B

NEW QUESTION # 29
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 a higher-memory node so that the job runs faster
B. Migrate the workload to Google Cloud Dataflow
C. Use pre-emptible virtual machines (VMs) for the cluster
D. Use SSDs on the worker nodes so that the job can run faster

Answer: C

Explanation:
https://cloud.google.com/dataproc/docs/concepts/compute/preemptible-vms

NEW QUESTION # 30
Does Dataflow process batch data pipelines or streaming data pipelines?

A. Only Batch Data Pipelines
B. Only Streaming Data Pipelines
C. Both Batch and Streaming Data Pipelines
D. None of the above

Answer: C

Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference:
https://cloud.google.com/dataflow/

NEW QUESTION # 31
......

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