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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring - Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation
Topic 2: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Topic 3: Designing data processing systems	20%	- Designing for business requirements 1. Designing for scalability and elasticity 2. Designing for reliability and fault tolerance 3. Selecting appropriate storage solutions - Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection
Topic 4: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
Topic 5: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options

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Google Certified Professional Data Engineer Exam Sample Questions (Q180-Q185):

NEW QUESTION # 180
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of datA. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

A. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
B. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
C. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
D. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.

Answer: D

NEW QUESTION # 181
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

A. only if
B. after
C. before
D. once

Answer: C

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 182
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

A. Grant the consultant the Cloud Dataflow Developer role on the project.
B. Grant the consultant the Viewer role on the project.
C. Create a service account and allow the consultant to log on with it.
D. Create an anonymized sample of the data for the consultant to work with in a different project.

Answer: C

NEW QUESTION # 183
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

A. Only streaming
B. BigQuery cannot be used as a sink
C. Only batch
D. Both batch and streaming

Answer: D

Explanation:
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts

NEW QUESTION # 184
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

A. Streaming job, PubSubIO, BigQueryIO, side-inputs
B. Batch job, PubSubIO, side-inputs
C. Streaming job, PubSubIO, BigQueryIO, side-outputs
D. Streaming job, PubSubIO, JdbcIO, side-outputs

Answer: A

NEW QUESTION # 185
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