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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
You migrated a data backend for an application that serves 10 PB of historical product data for analytics.
Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

A. 1. Store the historical data in BigQuery for analytics.
2. In a Cloud SQL table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.
B. 1. Store the historical data in Cloud SQL for analytics.
2. In a separate table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.
C. 1. Store the historical data in BigQuery for analytics.
2. Use a materialized view to precompute the last state of a product.
3. Serve the last state data directly from BigQuery to the API.
D. 1. Store the products as a collection in Firestore with each product having a set of historical changes.
2. Use simple and compound queries for analytics.
3. Serve the last state data directly from Firestore to the API.

Answer: C

NEW QUESTION # 70
You need to orchestrate a pipeline with several Google Cloud services: a batch Dataflow job, then a BigQuery query job followed by a Vertex AI batch prediction. The logic is sequential. You want a lightweight, serverless orchestration solution with minimal operational overhead. What service should you use?

A. Select Dataproc with Apache Oozie.
B. Select Compute Engine with cron.
C. Select Cloud Composer.
D. Select Cloud Workflows.

Answer: D

Explanation:
Cloud Workflows is a fully serverless orchestration service designed to coordinate sequential calls to Google Cloud services and APIs. It is lightweight, requires no infrastructure management, supports step-by-step execution with built-in error handling and retries, and is ideal for orchestrating simple, sequential pipelines involving services such as Dataflow, BigQuery, and Vertex AI.

NEW QUESTION # 71
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

A. Clustering estimator
B. Unsupervised learning
C. Regressor
D. Classifier

Answer: C

Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms

NEW QUESTION # 72
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
B. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
C. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
D. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

Answer: B

NEW QUESTION # 73
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

A. You expect to store at least 10 TB of data.
B. You will not use the data to back a user-facing or latency-sensitive application.
C. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
D. You need to integrate with Google BigQuery.

Answer: D

Explanation:
For example, if you plan to store extensive historical data for a large number of remote- sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads are much slower with HDD storage.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd

NEW QUESTION # 74
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