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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q237-Q242):

NEW QUESTION # 237
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

A. Create an individual external table for each Hive partition by using a common table name prefix.
Use wildcard table queries to reference the partitioned data.
B. Upgrade the external table to a BigLake table. Enable metadata caching for the table.
C. Change the storage class of the Hive partitioned data objects from Coldline to Standard.
D. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

Answer: B

NEW QUESTION # 238
You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

A. gsutil for both the migration and the real-time updates
B. BigQuery Data Transfer Service lor the migration, Pub/Sub and Dataproc for the real-time updates
C. gsutil for the migration; Pub/Sub and Dataflow for the real-time updates
D. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates

Answer: C

NEW QUESTION # 239
You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

A. 1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.
2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
B. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.
3. Assign permissions to the data engineering teams at the zone level.
C. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.
2 Provide each data engineering team access to the virtual lake.
D. 1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data.
2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Answer: D

Explanation:
This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12. By creating a Dataplex virtual lake for each data product,you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3. By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.
Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2. Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34. Option C is better than option A and B, but it does not create multiple zones forlanding, raw, and curated data, which may compromise the security and quality of the data products5. References:
* 1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog
* 2: Data Mesh - 7 Effective Practices to Get Started - Confluent
* 3: Best practices | Dataplex | Google Cloud
* 4: Secure your lake | Dataplex | Google Cloud
* 5: Zones | Dataplex | Google Cloud
* [6]: Managing a Data Mesh with Dataplex - ROI Training

NEW QUESTION # 240
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

A. Use pre-emptible virtual machines (VMs) for the cluster
B. Migrate the workload to Google Cloud Dataflow
C. Use SSDs on the worker nodes so that the job can run faster
D. Use a higher-memory node so that the job runs faster

Answer: A

Explanation:
https://cloud.google.com/dataproc/docs/concepts/compute/preemptible-vms

NEW QUESTION # 241
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQL Which pipeline should you use to meet these requirements?

A. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
B. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
C. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
D. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Answer: D

NEW QUESTION # 242
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