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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance
Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Planning for data migration and integration 3. Designing for data transformation and enrichment - 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. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows - Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic
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
Storing and managing data	20%	- Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns - Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes

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The web-based GCP-DE practice exam is similar to the desktop-based software. You can take the web-based GCP-DE practice exam on any browser without needing to install separate software. In addition, all operating systems also support this web-based Google GCP-DE Practice Exam. Both Data Engineer practice exams track your performance and help to overcome mistakes. Furthermore, you can customize your Data Engineer practice exams according to your needs.

Google Data Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
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 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.
B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
C. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
D. Use BigQuery to export the data for the table to a CSV fil
E. Add a new column called FullName to the Users tabl
F. 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.

Answer: C

NEW QUESTION # 36
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A. master node
B. worker node
C. conditional node
D. application node

Answer: A

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 37
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud SQL for storag
B. Use Cloud Spanner for storag
C. Use Cloud Spanner for storag
D. Add secondary indexes to support query patterns.
E. Use Cloud Dataflow to transform data to support query patterns.
F. Add secondary indexes to support query patterns.
G. Use Cloud Dataflow to transform data to support query patterns.
H. Use Cloud SQL for storag

Answer: E

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 38
Which of these is not a supported method of putting data into a partitioned table?

A. Create a partitioned table and stream new records to it every day.
B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
C. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".

Answer: D

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 39
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create a table on Cloud Spanner, and insert and delete rows with the job information
B. Create a table on Cloud SQL, and insert and delete rows with the job information
C. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
D. Create an API using App Engine to receive and send messages to the applications

Answer: C

NEW QUESTION # 40
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