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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. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Orchestrating data workflows 2. Transforming and cleaning data 3. Ingesting data from various sources
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
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
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 business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance - Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance

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Google Certified Professional Data Engineer Exam Sample Questions (Q293-Q298):

NEW QUESTION # 293
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/ Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

A. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
C. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
D. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination

Answer: B

Explanation:
Increase in number of undelivered messages shows that the messages are not getting subscribed.

NEW QUESTION # 294
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. To give a user access to only one table in a project, you must configure access through your application.
C. You can configure access control only at the project level.
D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

Answer: A

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.

NEW QUESTION # 295
You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

A. Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.
B. Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
C. Load files from AWS and Azure to Cloud Storage with Cloud Shell gsutil rsync arguments.
D. Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.

Answer: A

NEW QUESTION # 296
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

A. Create an individual external table for each Hive partition by using a common table name prefix.
Use wildcard table queries to reference the partitioned data.
B. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.
C. Upgrade the external table to a BigLake table. Enable metadata caching for the table.
D. Change the storage class of the Hive partitioned data objects from Coldline to Standard.

Answer: C

NEW QUESTION # 297
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A. 1 TB
B. 1 GB
C. 500 TB
D. 500 GB

Answer: A

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions.
It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options

NEW QUESTION # 298
......

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