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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost - Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation
Topic 2: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Transforming and cleaning data 2. Orchestrating data workflows 3. Ingesting data from various sources - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Topic 3: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
Topic 4: Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance - Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance
Topic 5: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q73-Q78):

73. Frage
Why do you need to split a machine learning dataset into training data and test data?

A. So you can use one dataset for a wide model and one for a deep model
B. To allow you to create unit tests in your code
C. To make sure your model is generalized for more than just the training data
D. So you can try two different sets of features

Antwort: C

Begründung:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

74. Frage
You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

A. Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors.
Configure your BigQuery ML model to use the "ingestion' dataset as the training data.
B. Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.
C. Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.
D. Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.

Antwort: D

Begründung:
Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.

75. Frage
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

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

Antwort: C,D,F

Begründung:
Explanation/Reference:

76. Frage
You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat
a. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?

A. Use BigQuery BI Engine with streaming data.
B. Use BigQuery Bl Engine with authorized views
C. Use BigQuery BI Engine with materialized views
D. Use BigQuery Bl Engine with logical reviews

Antwort: A

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

Antwort: A

78. Frage
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