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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Storing the data (~20% of the exam)	20%	- Planning for using a data warehouse 1. Mapping business requirements 2. Defining architecture to support data access patterns 3. Designing the data model 4. Deciding the degree of data normalization - Selecting storage systems 1. Planning for storage costs and performance 2. Lifecycle management of data 3. Analyzing data access patterns - 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 - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data
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. Connecting to Looker and other BI tools 2. Preparing data for reporting and dashboards
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 - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle - Performing security considerations 1. Identity and Access Management (IAM) 2. Data encryption 3. Auditing, privacy, and compliance
Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Recovering from failures 2. Performing data quality and validation checks 3. Planning for monitoring and alerting - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Scheduling jobs 3. Workflow orchestration - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage
Designing data processing systems (~30% of the exam)	30%	- 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. AI data enrichment 2. Data acquisition and import 3. Integrating with new data sources 4. Processing logic 5. Streaming (e.g., windowing, late arriving data) 6. Batch processing - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements

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Google Certified Professional Data Engineer Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

A. Cloud Dataflow connector
B. BiqQuery API
C. DataFlow SDK
D. BigQuery Data Transfer Service

Answer: A

Explanation:
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
Reference:
https://cloud.google.com/bigtable/docs/dataflow-hbase

NEW QUESTION # 112
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
B. Provide latitude and longtitude as input vectors to your neural net.
C. Create a numeric column from a feature cross of latitude and longtitude.
D. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Answer: C

Explanation:
Feature Crosses:
Feature crosses combine multiple features into a single feature that captures the interaction between them. For location data, a feature cross of latitude and longitude can capture spatial dependencies that affect housing prices.
This approach allows the neural network to learn complex patterns related to geographic location more effectively than using raw latitude and longitude values.
Numerical Representation:
Converting the feature cross into a numeric column simplifies the input for the neural network and can improve the model's ability to learn from the data.
This method ensures that the model can leverage the combined information from both latitude and longitude in a meaningful way.
Model Training:
Using a numeric column for the feature cross helps in regularizing the model and prevents overfitting, which is crucial for achieving good generalization on unseen data.
Reference:
To engineer a feature that incorporates the physical dependency of location on housing prices for a neural network, creating a numeric column from a feature cross of latitude and longitude is the most effective approach. Here's why option B is the best choice:

NEW QUESTION # 113
You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

A. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.
B. For each index, have a separate table and use a reverse timestamp as the row key design
C. Create one unique table for all of the indices, and then use the index and timestamp as the row key design
D. For each index, have a separate table and use a timestamp as the row key design

Answer: C

NEW QUESTION # 114
dataset.inventory_vm sample records:

You have an inventory of VM data stored in the BigQuery table. You want to prepare the data for regular reporting in the most cost-effective way. You need to exclude VM rows with fewer than 8 vCPU in your report. What should you do?

A. Use Dataflow to batch process and write the result to another BigQuery table.
B. Create a view with a filter to drop rows with fewer than 8 vCPU, and use the UNNEST operator.
C. Create a view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
D. Create a materialized view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.

Answer: B

NEW QUESTION # 115
You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table.
This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

A. Create a BigQuery reservation for the service account running the job.
B. Create a BigQuery reservation for the project.
C. Create a BigQuery reservation for the dataset.
D. Create a BigQuery reservation for the job.

Answer: B

NEW QUESTION # 116
......

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