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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	24%	- Planning data solutions 1. Planning for data security and compliance 2. Selecting appropriate storage solutions 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Designing for data transformation and enrichment 2. Defining architecture for batch and streaming processing 3. Planning for data migration and integration
Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Developing batch and streaming workflows 3. Handling data quality and consistency - Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation - Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs - Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management
Storing and managing data	20%	- Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns - Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately

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Google GCP-DE Fragen und Antworten, Data Engineer Prüfungsfragen

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q68-Q73):

68. Frage
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data loaded in BigQuery is not flagged as CSV.
B. The CSV data has not gone through an ETL phase before loading into BigQuery.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data has invalid rows that were skipped on import.

Antwort: D

69. Frage
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot use persistent disk.
B. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
C. A Dataproc cluster cannot have only preemptible workers.
D. Preemptible workers cannot store data.

Antwort: C,D

Begründung:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

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

Antwort: B,F

71. Frage
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision AutoML, but reduce your dataset twice.
B. Use Cloud Vision AutoML with the existing dataset.
C. Train your own image recognition model leveraging transfer learning techniques.
D. Use Cloud Vision API by providing custom labels as recognition hints.

Antwort: B

72. Frage
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Consume the stream of data in Cloud Dataflow using Kafka I
B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
C. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
E. Set a sliding time window of 1 hour every 5 minute
F. Consume the stream of data in Cloud Dataflow using Kafka I
G. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
H. If that number falls below 4000, send an alert.
I. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
K. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
L. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. If that number falls below 4000, send an alert.

Antwort: L

73. Frage
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