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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q57-Q62):

NEW QUESTION # 57
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 # 58
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Eliminate features that are highly correlated to the output labels.
B. Remove the features that have null values for more than 50% of the training records.
C. Combine highly co-dependent features into one representative feature.
D. Instead of feeding in each feature individually, average their values in batches of 3.

Answer: C

NEW QUESTION # 59
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data has not gone through an ETL phase before loading into BigQuery.
B. The CSV data has invalid rows that were skipped on import.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data loaded in BigQuery is not flagged as CSV.

Answer: B

NEW QUESTION # 60
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
B. Dataflow pipelines can consume data from other Google Cloud services
C. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
D. Dataflow pipelines can be programmed in Java

Answer: A

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 61
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element count
B. Trigger based on element size in bytes
C. Trigger that is a combination of other triggers
D. Trigger based on time

Answer: B

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 62
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