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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q307-Q312):

NEW QUESTION # 307
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.

A. master key
B. unique key
C. primary key
D. row key

Answer: D

Explanation:
Explanation
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 308
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.2. Load the booking and user profile data into a BigQuery table.
B. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the default masking value as the data masking rule.3. Assign the policy to the email field in both tables.4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
C. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the email mask as the data masking rule.3. Assign the policy to the email field in both tables. A4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
D. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.2. Load the booking and user profile data into a BigQuery table.

Answer: D

Explanation:
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by usingrecordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them.
You can then load the de-identified data into a BigQuery table for analysis. References:
De-identify sensitive data | Cloud Data Loss Prevention Documentation
Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

NEW QUESTION # 309
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B. Use the WHERE_PARTITIONTIMEpseudo column
C. Use the TABLE_DATE_RANGEfunction
D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Answer: C

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to- understand-your-mobile-app?hl=am

NEW QUESTION # 310
You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet.
What should you do?

A. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on- premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.
B. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center.
Use IP Allovlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.
C. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward- SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on- premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.
D. Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the Simba. googlebigqueryodbc. ini tile to point to your data center's NAT gateway.

Answer: A

Explanation:
To securely ingest data from an on-premises MySQL database into BigQuery without routing through the public internet, using Datastream with Private connectivity over Cloud Interconnect is the best approach.
Here's why:
* Datastream for Data Replication:
* Datastream provides a managed service for data replication from various sources, including on- premises databases, to Google Cloud services like BigQuery.
* Cloud Interconnect:
* Cloud Interconnect establishes a private connection between your on-premises data center and Google Cloud, ensuring that data transfer occurs over a secure, private network rather than the public internet.
* Private Connectivity:
* Using Private connectivity with Datastream leverages the established Cloud Interconnect to securely connect your on-premises MySQL database with Google Cloud. This method ensures that the data does not traverse the public internet.
* Encryption:
* Using Server-only encryption ensures that data is encrypted in transit between Datastream and BigQuery, adding an extra layer of security.
Steps to Implement:
* Set Up Cloud Interconnect:
* Establish a Cloud Interconnect between your on-premises data center and Google Cloud to create a private connection.
* Configure Datastream:
* Set up Datastream to use Private connectivity as the connection method and allocate an IP address range within your VPC network.
* Use Server-only encryption to ensure secure data transfer.
* Create Connection Profile:
* Create a connection profile in Datastream to define the connection parameters, including the use of Cloud Interconnect and Private connectivity.
Reference Links:
* Datastream Documentation
* Cloud Interconnect Documentation
* Setting Up Private Connectivity in Datastream

NEW QUESTION # 311
How can you get a neural network to learn about relationships between categories in a categorical feature?

A. Create an embedding column
B. Create a multi-hot column
C. Create a hash bucket
D. Create a one-hot column

Answer: A

Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.

NEW QUESTION # 312
......

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