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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Selecting appropriate storage solutions 3. Planning for data security and compliance - 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
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. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance
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. Designing data warehouses and data lakes 3. Using data storage services appropriately
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
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. Supporting model training and serving 2. Feature engineering and data preparation

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Google Data Engineer Sample Questions (Q51-Q56):

NEW QUESTION # 51
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) Join tables into one table; 2) Use nested repeated fields
C. 1) Use nested repeated fields; 2) Use a partitioned table
D. 1) Split table into multiple tables; 2) Use a partitioned table

Answer: B

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 # 52
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. Add a new column called FullName to the Users tabl
B. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C. 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.
D. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
E. 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.
F. Use BigQuery to export the data for the table to a CSV fil

Answer: B

NEW QUESTION # 53
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Then run the Hadoop utility to copy them do HDF
B. Mount the Hive tables locally.
C. Mount the Hive tables from HDFS.
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
E. Replicate external Hive tables to the native ones.
F. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
G. Replicate external Hive tables to the native ones.
H. Mount the Hive tables locally.
I. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
J. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
K. Load the ORC files into BigQuer
L. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste

Answer: D,H

NEW QUESTION # 54
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

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

Answer: A

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 # 55
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

A. Cloud Bigtable
B. BigQuery
C. Cloud Datastore
D. Cloud SQL

Answer: B

NEW QUESTION # 56
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