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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q416-Q421):

NEW QUESTION # 416
What are two of the characteristics of using online prediction rather than batch prediction?

A. Predictions are written to output files in a Cloud Storage location that you specify.
B. It is optimized to minimize the latency of serving predictions.
C. It is optimized to handle a high volume of data instances in a job and to run more complex models.
D. Predictions are returned in the response message.

Answer: B,D

Explanation:
Online prediction
.Optimized to minimize the latency of serving predictions.
.Predictions returned in the response message.
Batch prediction
.Optimized to handle a high volume of instances in a job and to run more complex models.
.Predictions written to output files in a Cloud Storage location that you specify.

NEW QUESTION # 417
What is the HBase Shell for Cloud Bigtable?

A. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
B. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
C. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
D. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

Answer: A

Explanation:
Explanation
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell

NEW QUESTION # 418
You have spent a few days loading data from comma-separated values (CSV) files into the Google
BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience,
you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute
web session durations of users who visit your site, and you want to change its data type to the
TIMESTAMP. You want to minimize the migration effort without making future queries computationally
expensive. What should you do?

A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to
cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table
NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table
NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is
loaded into the table NEW_CLICK_STREAM.
B. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the
BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto
true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause
ensuring that the value of IS_NEWmust be true.
C. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric
values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now
on.
D. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
E. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP
type. Reload the data.

Answer: B

NEW QUESTION # 419
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. Pipeline
D. PCollection

Answer: B

Explanation:
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 # 420
Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers

A. Insert each new CDC record and corresponding operation type into a staging table in real time
B. Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.
C. Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.
D. Periodically DELETE outdated records from the reporting table
Periodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

Answer: C,D

NEW QUESTION # 421
......

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