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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

Distributed systems
Mapping storage systems to business requirements
Designing data pipelines
Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
At least once, in-order, and exactly once, etc., event processing
Data publishing and visualization (e.g., BigQuery)
Job automation and orchestration (e.g., Cloud Composer)
Online (interactive) vs. batch predictions
Designing data processing systems
Hybrid cloud and edge computing
Capacity planning
System availability and fault tolerance
Data modeling
Choice of infrastructure
Tradeoffs involving latency, throughput, transactions

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Google Certified Professional Data Engineer exam is a certification exam offered by Google for individuals who want to demonstrate their expertise in designing and building data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam is designed to test candidates on their knowledge of data processing systems, machine learning, and data analysis tools on Google Cloud Platform.

Google Certified Professional Data Engineer Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

A. Dataproc Runner
B. Dataproc Editor
C. Dataproc Worker
D. Dataproc Viewer

Answer: C

Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).

NEW QUESTION # 23
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

A. 1. Load the data to BigQuery tables.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Procoss with the Spark-BigQuery connector or BigQuery SQL.
B. 1. Define a BigLake table.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy lags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SOL.
C. 1. Deploy a long-living Dalaproc cluster with Apache Hive and Ranger enabled.
2. Configure Ranger for column level security.
3. Process with Dataproc Spark or Hive SQL.
D. 1 Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage
2. Define a BigQuery external table for SQL processing.
3. Use Dataproc Spark to process the Cloud Storage files.

Answer: D

Explanation:
For automating the CI/CD pipeline of DAGs running in Cloud Composer, the following approach ensures that DAGs are tested and deployed in a streamlined and efficient manner.
Use Cloud Build for Development Instance Testing:
Use Cloud Build to automate the process of copying the DAG code to the Cloud Storage bucket of the development instance.
This triggers Cloud Composer to automatically pick up and test the new DAGs in the development environment.
Testing and Validation:
Ensure that the DAGs run successfully in the development environment.
Validate the functionality and correctness of the DAGs before promoting them to production.
Deploy to Production:
If the DAGs pass all tests in the development environment, use Cloud Build to copy the tested DAG code to the Cloud Storage bucket of the production instance.
This ensures that only validated and tested DAGs are deployed to production, maintaining the stability and reliability of the production environment.
Simplicity and Reliability:
This approach leverages Cloud Build's capabilities for automation and integrates seamlessly with Cloud Composer's reliance on Cloud Storage for DAG storage.
By using Cloud Storage for both development and production deployments, the process remains simple and robust.
Google Data Engineer Reference:
Cloud Composer Documentation
Using Cloud Build
Deploying DAGs to Cloud Composer
Automating DAG Deployment with Cloud Build
By implementing this CI/CD pipeline, you ensure that DAGs are thoroughly tested in the development environment before being automatically deployed to the production environment, maintaining high quality and reliability.

NEW QUESTION # 24
Cloud Dataproc charges you only for what you really use with _____ billing.

A. week-by-week
B. minute-by-minute
C. hour-by-hour
D. month-by-month

Answer: B

Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.

NEW QUESTION # 25
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A. You expect future mutations to have similar features to the mutated samples in the database.
B. There are very few occurrences of mutations relative to normal samples.
C. You expect future mutations to have different features from the mutated samples in the database.
D. There are roughly equal occurrences of both normal and mutated samples in the database.
E. You already have labels for which samples are mutated and which are normal in the database.

Answer: A,B

Explanation:
Explanation
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection

NEW QUESTION # 26
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

A. Establish a Cloud Interconnect between all remote data centers and Google.
B. Deploy small Kafka clusters in your data centers to buffer events.
C. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
D. Have the data acquisition devices publish data to Cloud Pub/Sub.

Answer: D

NEW QUESTION # 27
......

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