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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Monitoring and troubleshooting pipelines 3. Ensuring reliability and recoverability - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Topic 2: Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows - Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic
Topic 3: Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration - Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance
Topic 4: Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes
Topic 5: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Optimizing query performance 3. Preparing datasets for querying and reporting - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation

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Google Data Engineer Sample Questions (Q70-Q75):

NEW QUESTION # 70
Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Blaze
B. Ignite
C. Fire
D. Spark

Answer: D

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

NEW QUESTION # 71
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. after
B. before
C. only if
D. once

Answer: B

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 72
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Answer: B

NEW QUESTION # 73
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

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

Answer: D

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 # 74
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. Set the BigQuery dataset to be multi-regional
B. In the event of an emergency, use the backup copy of the table.
C. Set the BigQuery dataset to be regiona
D. Set the BigQuery dataset to be regiona
E. In the event of an emergency, use a point-in-time snapshot to recover the data.
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 backup
I. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
J. In the event of an emergency, use the backup copy of the table.

Answer: G

NEW QUESTION # 75
......

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