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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection - ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment
Topic 2: Ensuring solution quality	28%	- Security and governance 1. Data governance and compliance 2. IAM and access control in GCP - Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads
Topic 3: Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Topic 4: Designing data processing systems	22%	- Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures - Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

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Google Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

A. Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.
B. Use an exception handling block in your Data Flow's Doffs code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_jnacked_messages_by_region metric on this new topic.
C. Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.
D. Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/numessages metric on this snapshot.

Answer: C

Explanation:
To ensure that messages failing to process in your Dataflow job are sent to a Pub/Sub topic for monitoring and alerting, the best approach is to use Pub/Sub's dead-letter topic feature. Here's why option C is the best choice:
Dead-Letter Topic:
Pub/Sub's dead-letter topic feature allows messages that fail to be processed successfully to be redirected to a specified topic. This ensures that these messages are not lost and can be reviewed for debugging and alerting purposes.
Monitoring and Alerting:
By specifying a new Pub/Sub topic as the dead-letter topic, you can use Cloud Monitoring to track metrics such as subscription/dead_letter_message_count, providing visibility into the number of failed messages.
This allows you to set up alerts based on these metrics to notify the appropriate teams when failures occur.
Steps to Implement:
Enable Dead-Letter Topic:
Configure your Pub/Sub pull subscription to enable dead lettering and specify the new Pub/Sub topic for dead-letter messages.
Set Up Monitoring:
Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.
Configure alerts based on this metric to notify the team of any processing failures.
Reference:
Pub/Sub Dead Letter Policy
Cloud Monitoring with Pub/Sub

NEW QUESTION # 116
Your data science team needs to perform interactive SQL queries on large datasets stored in Apache Parquet format within a Cloud Storage bucket. The team is familiar with Apache Hive and wants to leverage existing HiveQL queries. You need to provide an environment for the team to run their interactive HiveQL queries directly against the data in Cloud Storage. You want to keep operational overhead to a minimum. What should you do?

A. Load the Parquet data into a BigQuery native table and use the BigQuery Connector for Hive to run the queries.
B. Install and configure an Apache Hadoop and Hive cluster manually on a group of Compute Engine instances.
C. Configure BigQuery with an external table definition pointing to the Parquet files.
D. Deploy a Dataproc cluster with Hive services enabled.

Answer: D

Explanation:
Deploying a Dataproc cluster with Hive enabled allows the team to run interactive HiveQL queries directly against Parquet data stored in Cloud Storage using familiar Apache Hive tooling.
Dataproc is fully managed, quick to provision, integrates natively with Cloud Storage, and minimizes operational overhead compared to self-managed clusters while preserving compatibility with existing HiveQL workloads.

NEW QUESTION # 117
They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data.
Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects.
What should they do?

A. Create and share an authorized view that provides the aggregate results.
B. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
C. Create and share a new dataset and view that provides the aggregate results.
D. Create and share a new dataset and table that contains the aggregate results.

Answer: A

NEW QUESTION # 118
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. 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.
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 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
D. 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. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

Answer: B

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 # 119
Your company uses separate Google Cloud projects for development, staging, and production.
Developers need edit access in the development project, read-only access in the staging project, and no access in the production project. You need an effective and manageable solution to assign and enforce these permissions according to Google-recommended practices. What should you do?

A. Grant individual developers specific IAM roles directly on each project.
B. Create Google Groups for access levels, assign developers to groups, and grant the groups the appropriate IAM roles on each project.
C. Configure firewall rules within each project's VPC to block traffic from unauthorized developer machines.
D. Create separate VPC Service Controls perimeters for each project and use access levels.

Answer: B

Explanation:
Using Google Groups to represent access levels and assigning IAM roles to those groups at the project level provides a scalable and maintainable access control model. This approach aligns with Google-recommended best practices by centralizing identity management, simplifying permission changes, and consistently enforcing least-privilege access across development, staging, and production environments.

NEW QUESTION # 120
......

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