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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Monitoring and logging data processes 2. Managing infrastructure and resources - Building data pipelines 1. Orchestrating data workflows 2. Ingesting data from various sources 3. Transforming and cleaning data
Maintaining and automating data workloads	18%	- Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems - Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options
Operationalizing machine learning models	20%	- Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation - Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost
Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues - Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing
Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance - Designing for business requirements 1. Designing for reliability and fault tolerance 2. Selecting appropriate storage solutions 3. Designing for scalability and elasticity

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Google Certified Professional Data Engineer Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries.
What should you do?

A. Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.
B. Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.
C. Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.
D. Create a sales_transaction table that holds the sales_transaction_header information as rows and thesales_transaction_line rows as nested and repeated fields.

Answer: D

Explanation:
BigQuery supports nested and repeated fields, which are complex data types that can represent hierarchical and one-to-many relationships within a single table. By using nested and repeated fields, you can denormalize your data model and reduce the number of joins required for your queries. This can improve the performance and efficiency of your data analytics queries, as joins can be expensive and require shuffling data across nodes. Nested and repeated fields also preserve the data integrity and avoid data duplication. In this scenario, the sales_transaction_header and sales_transaction_line tables have a tightly coupled immutable relationship, meaning that each header row corresponds to one or more line rows, and the data is rarely modified after load.
Therefore, it makes sense to create a single sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields. This way, you can query the sales transaction data without joining two tables, and use dot notation or array functions to access the nested and repeated fields. For example, the sales_transaction table could have the following schema:
Table
Field name
Type
Mode
id
INTEGER
NULLABLE
order_time
TIMESTAMP
NULLABLE
customer_id
INTEGER
NULLABLE
line_items
RECORD
REPEATED
line_items.sku
STRING
NULLABLE
line_items.quantity
INTEGER
NULLABLE
line_items.price
FLOAT
NULLABLE
To query the total amount of each order, you could use the following SQL statement:
SQL
SELECTid,SUM(line_items.quantity*line_items.price)AStotal_amount
FROMsales_transaction
GROUPBYid;
AI-generated code. Review and use carefully. More info on FAQ.
References:
Use nested and repeated fields
BigQuery explained: Working with joins, nested & repeated data
Arrays in BigQuery - How to improve query performance and optimise storage

NEW QUESTION # 115
You have two projects where you run BigQuery jobs:
- One project runs production jobs that have strict completion time
SLAs. These are high priority jobs that must have the required compute
resources available when needed. These jobs generally never go below a
300 slot utilization, but occasionally spike up an additional 500
slots.
- The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity.
You need to ensure that both projects have the appropriate compute resources available. What should you do?

A. Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots.
Enable autoscaling up to 700 slots.
B. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignore idle slots flag to False.
C. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots.
D. Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing.

Answer: D

NEW QUESTION # 116
Your neural network model is taking days to train. You want to increase the training speed. What can you
do?

A. Subsample your test dataset.
B. Subsample your training dataset.
C. Increase the number of layers in your neural network.
D. Increase the number of input features to your model.

Answer: C

Explanation:
Explanation/Reference:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-
9f5d1c6f407d

NEW QUESTION # 117
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

A. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
B. Write a job template in Cloud Dataproc to perform the data transfer.
C. Use Cloud Dataflow and write the data to Cloud Storage.
D. Execute gsutil rsync from the on-premises servers.

Answer: D

NEW QUESTION # 118
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?

A. Create an additional project to overcome the 2K on-demand per-project quota.
B. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
C. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
D. Convert your batch BQ queries into interactive BQ queries.

Answer: B

Explanation:
Explanation
Explanation/Reference:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

NEW QUESTION # 119
......

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