How to Get the Google Professional-Data-Engineer Certification within the Target Period?

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q207-Q212):

NEW QUESTION # 207
Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

A. Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.
B. Enable each team to create materialized views of the data they need to access in their projects.
C. Create a BigQuery scheduled query to replicate all customer data into team projects.
D. Ask each team to create authorized views of their data. Grant the biquery. jobUser role to each team.

Answer: A

Explanation:
To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:
* Analytics Hub:
* Analytics Huballows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.
* This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.
* Data Publishing and Subscribing:
* Teams can publish datasets they control, allowing them to manage access and updates independently.
* Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.
* Minimized Operational Tasks and Costs:
* This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.
* By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.
Steps to Implement:
* Set Up Analytics Hub:
* Enable Analytics Hub in your Google Cloud project.
* Provide training to teams on how to publish and subscribe to data exchanges.
* Publish Data:
* Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.
* Subscribe to Data:
* Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.
Reference Links:
* Analytics Hub Documentation
* Publishing Data in Analytics Hub
* Subscribing to Data in Analytics Hub

NEW QUESTION # 208
Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

A. Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
B. Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.
C. Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.
D. Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

Answer: A

Explanation:
You cannot import custom or filtered billing criteria into BigQuery. There are three types of Cloud Billing data tables with a fixed schema that must further drilled-down via BigQuery views.
Reference:
https://cloud.google.com/billing/docs/how-to/export-data-bigquery#setup

NEW QUESTION # 209
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use K-means Clustering to detect faces in the pixels.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Answer: D

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the
previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.

NEW QUESTION # 210
You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?

A. Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data
B. Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.
C. Create a normalized model with tables for each entity. Use snapshots before updates to track historical data
D. Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

Answer: B

Explanation:
- A denormalized, append-only model simplifies query complexity by eliminating the need for joins. - Adding data with an ingestion timestamp allows for easy retrieval of both current and historical states. - Instead of updating records, new records are appended, which maintains historical information without the need to create separate snapshots.

NEW QUESTION # 211
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_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Manually configure the index in your index config as follows:
B. Manually configure the index in your index config as follows:
C. Set the following in your entity options: exclude_from_indexes = 'actors, tags'
D. Set the following in your entity options: exclude_from_indexes = 'date_published'

Answer: B

NEW QUESTION # 212
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