GCP-DE Preparation Materials and Study Guide: Data Engineer - TroytecDumps

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Implement disaster recovery strategies 2. Design highly available systems 3. Scale data workloads efficiently - Managing resources and costs 1. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance
Ensuring solution quality	20%-25%	- Data quality management 1. Maintain metadata and lineage 2. Implement data governance 3. Validate and clean data - Security and compliance 1. Implement encryption and compliance controls 2. Manage IAM and access control 3. Protect sensitive data
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery - Building data processing systems 1. Implement batch processing systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement streaming data systems
Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Design batch and streaming ingestion solutions 3. Select appropriate storage systems - Designing data pipelines 1. Optimize pipeline performance and cost 2. Design orchestration workflows 3. Build ETL and ELT pipelines

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Google Data Engineer Sample Questions (Q27-Q32):

NEW QUESTION # 27
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a SideInput that returns a Boolean if the element is corrupt.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Answer: C

NEW QUESTION # 28
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the WHERE_PARTITIONTIME pseudo column
B. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
C. Use the TABLE_DATE_RANGE function
D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Answer: C

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap

NEW QUESTION # 29
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 AutoML, but reduce your dataset twice.
B. Use Cloud Vision API by providing custom labels as recognition hints.
C. Use Cloud Vision AutoML with the existing dataset.
D. Train your own image recognition model leveraging transfer learning techniques.

Answer: C

NEW QUESTION # 30
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A. Sink API
B. PCollection
C. Pipeline
D. Transform

Answer: D

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model

NEW QUESTION # 31
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
B. In the event of an emergency, use the backup copy of the table.
C. In the event of an emergency, use the backup copy of the table.
D. Set the BigQuery dataset to be regiona
E. Set the BigQuery dataset to be regiona
F. Set the BigQuery dataset to be multi-regional
G. In the event of an emergency, use a point-in-time snapshot to recover the data.
H. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
I. Set the BigQuery dataset to be multi-regional
J. In the event of an emergency, use a point-in-time snapshot to recover the data.

Answer: G

NEW QUESTION # 32
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