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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q71-Q76):

NEW QUESTION # 71
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Answer: B

NEW QUESTION # 72
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use a partitioned table; 2) Join tables into one table
B. 1) Use nested repeated fields; 2) Use a partitioned table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Split table into multiple tables; 2) Use a partitioned table

Answer: C

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 73
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. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
B. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
C. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Answer: D

NEW QUESTION # 74
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Create groups for your users and give those groups access to the dataset
B. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
C. Create a dummy user and grant dataset access to that use
D. Use the service account's private key to access the dataset
E. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
F. Create a service account and grant dataset access to that accoun

Answer: F

NEW QUESTION # 75
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. Transform
C. PCollection
D. Pipeline

Answer: B

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 # 76
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