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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	28%	- Security and governance 1. Data governance and compliance 2. IAM and access control in GCP - Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads
Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction - ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment
Designing data processing systems	22%	- Batch and streaming data processing design 1. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs - Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Batch ingestion pipelines (BigQuery, Cloud Storage) 2. Streaming ingestion (Pub/Sub, Dataflow) - Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL

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Google Certified Professional Data Engineer Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
Which methods can be used to reduce the number of rows processed by BigQuery?

A. Putting data in partitions; using the LIMIT clause
B. Splitting tables into multiple tables; using the LIMIT clause
C. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause
D. Splitting tables into multiple tables; putting data in partitions

Answer: D

Explanation:
If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day. If you use the LIMIT clause, BigQuery will still process the entire table.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 143
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A. The [myproject:mydataset.mytable] table has too many partitions
B. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
C. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
D. Users are running too many concurrent queries in the system

Answer: D

NEW QUESTION # 144
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
B. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
C. To give a user access to only one table in a project, you must configure access through your application.
D. You can configure access control only at the project level.

Answer: A

Explanation:
Explanation
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.
Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 145
What is the HBase Shell for Cloud Bigtable?

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

Answer: D

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 # 146
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Change "person" to "person.city".
B. Add ", UNNEST(person)" before the WHERE clause.
C. Change "person" to "city.person".
D. Add ", UNNEST(city)" before the WHERE clause.

Answer: B

Explanation:
Explanation
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

NEW QUESTION # 147
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