100% Pass Latest Google - GCP-DE - Exam Data Engineer Overviews

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. IAM and access control 2. Data encryption and compliance - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery

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Google Data Engineer Sample Questions (Q22-Q27):

NEW QUESTION # 22
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision API by providing custom labels as recognition hints.
B. Use Cloud Vision AutoML, but reduce your dataset twice.
C. Train your own image recognition model leveraging transfer learning techniques.
D. Use Cloud Vision AutoML with the existing dataset.

Answer: D

NEW QUESTION # 23
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Cloud AutoML Natural Language
B. Cloud Speech-to-Text API
C. Cloud Natural Language API
D. Dialogflow Enterprise Edition

Answer: A

NEW QUESTION # 24
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. An hourly watermark
B. An event time trigger
C. The with Allowed Lateness method
D. A processing time trigger

Answer: D

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers

NEW QUESTION # 25
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

A. Cloud Datastore
B. Cloud SQL
C. Cloud Bigtable
D. Cloud Spanner

Answer: D

NEW QUESTION # 26
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create an additional table with only the necessary columns.
B. Export the data into a Google Sheet for virtualization.
C. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
D. Create a view on the table to present to the virtualization tool.

Answer: D

NEW QUESTION # 27
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