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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q396-Q401):

396. Frage
You work for a large real estate firm and are preparing 6 TB of home sales data to be used for machine learning. You will use SQL to transform the data and use BigQuery ML to create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

A. Preprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the input data.
B. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
C. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. Before requesting predictions, use a saved query to transform your raw input data, and then use ML.EVALUATE.
D. Use a BigQuery view to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.

Antwort: C

397. Frage
Which of these statements about exporting data from BigQuery is false?

A. Data can only be exported in JSON or Avro format.
B. To export more than 1 GB of data, you need to put a wildcard in the destination filename.
C. The only compression option available is GZIP.
D. The only supported export destination is Google Cloud Storage.

Antwort: A

Begründung:
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.

398. Frage
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)

A. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.
C. Introduce data compression for each file to increase the rate file of file transfer.
D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Antwort: B,E

399. Frage
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformations. Use the diagnosecommand to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
B. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
Configure the job to use non-default Compute Engine machine types when needed.
C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
D. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Antwort: A

Begründung:
Explanation

400. Frage
Which of these statements about BigQuery caching is true?

A. By default, a query's results are not cached.
B. BigQuery caches query results for 48 hours.
C. Query results are cached even if you specify a destination table.
D. There is no charge for a query that retrieves its results from cache.

Antwort: D

Begründung:
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours. Query results are not cached if you specify a destination table. A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference:
https://cloud.google.com/bigquery/querying-data#query-caching

401. Frage
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