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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which is not a valid reason for poor Cloud Bigtable performance?

A. The table's schema is not designed correctly.
B. The workload isn't appropriate for Cloud Bigtable.
C. There are issues with the network connection.
D. The Cloud Bigtable cluster has too many nodes.

Answer: D

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 70
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
B. Add a new column called FullName to the Users tabl
C. Use BigQuery to export the data for the table to a CSV fil
D. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
E. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
F. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Answer: D

NEW QUESTION # 71
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: date#device_idColumn data: data_point
B. Rowkey: data_pointColumn data: device_id, date
C. Rowkey: date#data_pointColumn data: device_id
D. Rowkey: dateColumn data: device_id, data_point
E. Rowkey: device_idColumn data: date, data_point

Answer: B

NEW QUESTION # 72
Cloud Dataproc is a managed Apache Hadoop and Apache service.

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

Answer: A

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 # 73
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. Dialogflow Enterprise Edition
B. Cloud AutoML Natural Language
C. Cloud Speech-to-Text API
D. Cloud Natural Language API

Answer: B

NEW QUESTION # 74
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