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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q349-Q354):

NEW QUESTION # 349
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

A. [1, 0, 0, 0, 0, 0, 0]
B. [0, 1]
C. [0, 5, 0, 0, 0, 0]
D. [0, 0, 0, 1, 0, 0, 1]

Answer: A,B

Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent
'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

NEW QUESTION # 350
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

A. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
B. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
C. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
D. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

Answer: B

NEW QUESTION # 351
Your startup has a web application that currently serves customers out of a single region in Asia.
You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A. Use a Cloud SQL for PostgreSQL zonal instance first, and Bigtable with US, Europe, and Asia after securing funding.
B. Use Cloud Spanner to configure a single region instance initially, and then configure multi-region Cloud Spanner instances after securing funding.
C. Use a Cloud SQL for PostgreSQL zonal instance first, and Cloud SQL for PostgreSQL with highly available configuration after securing funding.
D. Use a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US, Europe, and Asia replication after securing funding.

Answer: B

Explanation:
When you create a Cloud Spanner instance, you must configure it as either regional (that is, all the resources are contained within a single Google Cloud region) or multi-region (that is, the resources span more than one region).
You can change the instance configuration to multi-regional (or global) at anytime.

NEW QUESTION # 352
Your company is in a highly regulated industry. One of your requirements is to ensure individual users
have access only to the minimum amount of information required to do their jobs. You want to enforce this
requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

A. Restrict access to tables by role.
B. Disable writes to certain tables.
C. Restrict BigQuery API access to approved users.
D. Use Google Stackdriver Audit Logging to determine policy violations.
E. Ensure that the data is encrypted at all times.
F. Segregate data across multiple tables or databases.

Answer: A,C,D

NEW QUESTION # 353
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

A. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. Introduce resource hierarchy to leverage access control policy inheritance.
E. Use Cloud Deployment Manager to automate access provision.

Answer: C,E

NEW QUESTION # 354
......

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