GCP PROFESSIONAL DATA ENGINEER CERTIFICATION Diagnostic Qus P-1

webstoryworldwide.com

1 week ago

1. Cymbal Retail has acquired another company in Europe. Data access permissions and policies in this new region differ from those in Cymbal Retail’s headquarters, which is in North America. You need to define a consistent set of policies for projects in each region that follow recommended practices. What should you do?

- Create top level folders for each region, and assign policies at the folder level.
- Implement a flat hierarchy, and assign policies to each project according to its region.
- Create a new organization for all projects in Europe and assign policies in each organization that comply with regional laws.
- Implement policies at the resource level that comply with regional laws.

The correct answer is:

✅ Create top-level folders for each region, and assign policies at the folder level.
Why this is the best practice

Google Cloud recommends using folders to reflect organizational, geographic, or regulatory boundaries. In your scenario:

North America and Europe have different data access laws and policies

You want consistency, scalability, and centralized management

By creating top-level folders per region, you can:

Apply IAM policies, organization policies, and constraints once at the folder level

Automatically inherit policies to all projects in that region

Avoid duplicating policy management across individual projects

Easily onboard new projects in a region with the correct policies by default

This is the cleanest and most scalable design.

2. You are using Dataproc to process a large number of CSV files. The storage option you choose needs to be flexible to serve many worker nodes in multiple clusters. These worker nodes will read the data and also write to it for intermediate storage between processing jobs. What is the recommended storage option on Google Cloud?

- Local SSD
- Zonal persistent disks
- Cloud Storage
- Cloud SQL

The correct answer is:

✅ Cloud Storage
Why Cloud Storage is recommended

For Dataproc workloads processing large numbers of CSV files, especially when:

Many worker nodes

Multiple clusters

Concurrent read and write access

Intermediate storage between jobs

👉 Cloud Storage is the best and recommended option.

Key reasons:

Highly scalable & shared across all clusters and nodes

Supports parallel reads/writes

Decoupled from compute (clusters can be created/deleted without data loss)

Ideal for input, output, and intermediate data

Native integration with Dataproc, Spark, Hadoop, and Beam

This matches Google’s best practice:

Use Cloud Storage as the data lake for Dataproc.

Why the other options are not suitable

❌ Local SSD

Attached to a single VM

Data is lost when the VM stops

Not shareable across nodes or clusters

❌ Zonal persistent disks

Limited to a single VM or cluster

Not designed for multi-cluster shared access

Less scalable for large distributed workloads

❌ Cloud SQL

Relational database

Not suitable for large-scale file-based batch processing

Exam tip 🧠

If you see:

Dataproc

Multiple workers / clusters

Shared, scalable storage

👉 Always think Cloud Storage first.

3. You are running a user-supplied DoFn method signature pipeline in Dataflow. The function has been defined by you. The code is running slow and you want to further examine the pipeline code to get better visibility of why. What should you do?

- Use Cloud Logging
- Use Cloud Profiler
- Use Cloud Monitoring
- Use Cloud Audit Logs

The correct answer is:

✅ Use Cloud Profiler
Why Cloud Profiler is the right choice

You want better visibility into why your user-defined DoFn code is running slow. That means you need code-level performance analysis, not just metrics or logs.

Cloud Profiler:

Attaches to running Dataflow workers

Shows CPU usage, memory usage, and hot paths

Pinpoints which methods in your DoFn are slow

Helps identify:

Expensive computations

Inefficient loops

Excessive object creation

Serialization overhead

This is exactly what you need when the problem is slow pipeline code, especially inside a user-supplied DoFn.

Why the other options are not correct

❌ Cloud Logging
Logs events and messages, but does not show performance bottlenecks.

❌ Cloud Monitoring
Shows metrics (CPU, memory, throughput), but not which lines of code are slow.

❌ Cloud Audit Logs
Used for administrative and security auditing, not performance analysis.

Quick exam tip 🧠

If the question mentions:

“pipeline code is slow”

“user-defined DoFn”

“examine code behavior”

👉 Cloud Profiler is the answer.

4. You are managing the data for Cymbal Retail, which consists of multiple teams including retail, sales, marketing, and legal. These teams are consuming data from multiple producers including point of sales systems, industry data, orders, and more. Currently, teams that consume data have to repeatedly ask the teams that produce it to verify the most up-to-date data and to clarify other questions about the data, such as source and ownership. This process is unreliable and time-consuming and often leads to repeated escalations. You need to implement a centralized solution that gains a unified view of the organization's data and improves searchability. What should you do?

- Implement Looker dashboards that provide views of the data that meet each teams’ requirements.
- Implement a data lake with Cloud Storage, and create buckets for each team such as retail, sales, marketing.
- Implement a data warehouse by using BigQuery, and create datasets for each team such as retail, sales, marketing.
- Implement a data mesh with Dataplex and have producers tag data when created.

The correct answer is:

✅ Implement a data mesh with Dataplex and have producers tag data when created.
Why this is the right solution

Your problem is not storage or dashboards — it’s data discovery, ownership, trust, and governance.

You need:

A centralized view of all data assets

Clear metadata (source, owner, freshness, schema)

Better searchability

Fewer manual escalations between producers and consumers

👉 Dataplex is designed exactly for this.

What Dataplex gives you

A logical data mesh across BigQuery, Cloud Storage, etc.

Centralized metadata management

Business and technical tags (owner, domain, SLA, sensitivity)

Data discovery and search across teams

Clear producer/consumer responsibility model

Producers tag data at creation → consumers can self-serve without asking questions repeatedly.

Why the other options are insufficient
❌ Looker dashboards

Good for visualization

Does not solve data ownership, lineage, or discovery

Still requires manual clarification

❌ Data lake with Cloud Storage buckets

Only storage

No metadata governance or discovery layer

Consumers still won’t know which data is authoritative

❌ BigQuery datasets per team

Improves organization

But still lacks cross-team discovery, ownership metadata, and governance

Does not scale for many producers/consumers

Key concept (exam + real world)

When the problem is data discovery, trust, ownership, and governance → Dataplex (data mesh).

5. You are migrating on-premises data to a data warehouse on Google Cloud. This data will be made available to business analysts. Local regulations require that customer information including credit card numbers, phone numbers, and email IDs be captured, but not used in analysis. You need to use a reliable, recommended solution to redact the sensitive data. What should you do?

- Create a regular expression to identify and delete patterns that resemble credit card numbers, phone numbers, and email IDs.
- Use the Cloud Data Loss Prevention API (DLP API) to perform date shifting of any entries with credit card numbers, phone numbers, and email IDs.
- Use the Cloud Data Loss Prevention API (DLP API) to identify and redact data that matches infoTypes like credit card numbers, phone numbers, and email IDs.
- Delete all columns with a title similar to "credit card," "phone," and "email."

The correct answer is:

✅ Use the Cloud Data Loss Prevention API (DLP API) to identify and redact data that matches infoTypes like credit card numbers, phone numbers, and email IDs.
Why this is the recommended solution

You have regulatory requirements and need a reliable, Google-recommended way to handle sensitive data before analytics.

Cloud DLP API is purpose-built for this:

Uses predefined infoTypes (e.g. CREDIT_CARD_NUMBER, PHONE_NUMBER, EMAIL_ADDRESS)

Accurately detects sensitive data, even when column names or formats vary

Supports redaction, masking, tokenization, or replacement

Designed for compliance (PCI, GDPR, etc.)

Integrates cleanly with BigQuery, Cloud Storage, Dataflow

This ensures analysts can work with data without exposing sensitive information.

Why the other options are incorrect

❌ Regular expressions

Error-prone and hard to maintain

High risk of false positives/negatives

Not recommended for compliance use cases

❌ Date shifting with DLP

Date shifting is for timestamps, not PII like credit cards or emails

❌ Deleting columns by name

Column names are unreliable

Sensitive data may exist in unexpected fields

Leads to data loss instead of safe redaction

Exam tip 🧠

If the question mentions:

PII

Regulations

Redaction / masking