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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q419-Q424):

NEW QUESTION # 419
Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

A. 1 Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own lake's data assets.
B. 1. Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all zones' data assets.
C. 1 Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each to the BigQuory datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own zone's data assets.
D. 1 Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all lakes' data assets.

Answer: C

Explanation:
To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:
* Data Mesh Architecture:
* Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.
* Dataplex Lakes and Zones:
* Lakesin Dataplex are logical containers for managing data at scale, andzonesare subdivisions within lakes for organizing data based on domains, teams, or other criteria.
* Domain and Team Management:
* By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.
* This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.
Implementation Steps:
* Create Lakes and Zones:
* Create separate lakes in Dataplex for each team (analytics and data science).
* Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).
* Attach BigQuery Datasets:
* Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.
* Decentralized Management:
* Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.
Reference Links:
* Dataplex Documentation
* BigQuery Documentation
* Data Mesh Principles

NEW QUESTION # 420
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2 Create a separate DAG for each table that needs to go through the pipeline
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
B. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline
3 Schedule the DAG to run hourly
C. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline.
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
D. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators
2 Create a separate DAG for each table that needs to go through the pipeline
3 Schedule the DAGs to run hourly

Answer: A

Explanation:
This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket. By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performance of these services12. By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3. By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for the corresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4 .
Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket. Reference:
1: Dataproc Operator | Cloud Composer | Google Cloud
2: BigQuery Operator | Cloud Composer | Google Cloud
3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud
4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud
[5]: Triggering DAGs | Cloud Composer | Google Cloud
[6]: Cloud Storage Operator | Cloud Composer | Google Cloud

NEW QUESTION # 421
Which is not a valid reason for poor Cloud Bigtable performance?

A. There are issues with the network connection.
B. The Cloud Bigtable cluster has too many nodes.
C. The workload isn't appropriate for Cloud Bigtable.
D. The table's schema is not designed correctly.

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.

NEW QUESTION # 422
You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

A. Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.
B. Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word "street" appears.
C. Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that
D. Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Answer: D

Explanation:
includes the STREET_ADDRESS infoType.
Explanation:
To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here's why option A is the best choice:
Cloud Data Loss Prevention (DLP):
Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre-defined infoTypes for various kinds of sensitive data, including street addresses.
Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.
Deep Inspection Job:
A deep inspection job allows you to scan entire tables for sensitive information.
By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.
Scalability and Accuracy:
Cloud DLP is scalable and can handle large datasets efficiently.
It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.
Steps to Implement:
Set Up Cloud DLP:
Enable the Cloud DLP API in your Google Cloud project.
Create an Inspection Template:
Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.
Run Deep Inspection Jobs:
Create and run a deep inspection job for each table in your dataset using the inspection template.
Review the inspection job results to retrieve all occurrences of street addresses.
Reference:
Cloud DLP Documentation
Creating Inspection Jobs

NEW QUESTION # 423
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 at the minute level and use L1 regularization during optimization.
B. Create a numeric column from a feature cross of latitude and longtitude.
C. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
D. Provide latitude and longtitude as input vectors to your neural net.

Answer: A

Explanation:
Reference https://cloud.google.com/bigquery/docs/gis-dataa

NEW QUESTION # 424
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