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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Preparing and using data for analysis (~15% of the exam)	15%	- Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools - Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration
Storing the data (~20% of the exam)	20%	- Selecting storage systems 1. Planning for storage costs and performance 2. Lifecycle management of data 3. Analyzing data access patterns - Designing for a data platform 1. Building a federated governance model for distributed data systems 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage - Planning for using a data warehouse 1. Mapping business requirements 2. Defining architecture to support data access patterns 3. Designing the data model 4. Deciding the degree of data normalization - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data
Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Planning for monitoring and alerting 2. Performing data quality and validation checks 3. Recovering from failures - Automating data processes 1. Scheduling jobs 2. Continuous integration and continuous deployment (CI/CD) 3. Workflow orchestration - Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting
Ingesting and processing the data (~20% of the exam)	20%	- Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines - Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Performing security considerations 1. Identity and Access Management (IAM) 2. Auditing, privacy, and compliance 3. Data encryption
Designing data processing systems (~30% of the exam)	30%	- Selecting appropriate storage technologies 1. Mapping storage options to business requirements 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB - Designing data pipelines 1. Batch processing 2. AI data enrichment 3. Processing logic 4. Integrating with new data sources 5. Data acquisition and import 6. Streaming (e.g., windowing, late arriving data) - Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cost optimization 3. Cluster sizing and autoscaling

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Google Certified Professional Data Engineer Exam Sample Questions (Q171-Q176):

NEW QUESTION # 171
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Option C
B. Option D
C. Option B.
D. Option A

Answer: D

NEW QUESTION # 172
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

A. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
B. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.
C. Use .fromQueryoperation to read specific fields from the table.
D. Specify the TableReferenceobject in the code.

Answer: B

NEW QUESTION # 173
You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

A. Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
B. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage
C. Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.
D. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in
Cloud Storage

Answer: B

NEW QUESTION # 174
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 a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
D. 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

Answer: B

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

NEW QUESTION # 175
Which of the following statements is NOT true regarding Bigtable access roles?

A. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
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. To give a user access to only one table in a project, grant the user the Bigtable Editor role for
that table.

Answer: D

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 # 176
......

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