GCP-DE Zertifizierung - GCP-DE Testengine

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration
Topic 2: Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Optimizing query performance 2. Implementing data sharing and governance 3. Preparing datasets for querying and reporting
Topic 3: Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows - Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance
Topic 4: Storing and managing data	20%	- Implementing storage solutions 1. Using data storage services appropriately 2. Managing data lifecycle and retention 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies
Topic 5: 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. Automating deployment and management 2. Optimizing resource usage and costs

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q29-Q34):

29. Frage
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Antwort: B

30. Frage
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
C. Add a SideInput that returns a Boolean if the element is corrupt.
D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Antwort: B

31. Frage
When a Cloud Bigtable node fails, is lost.

A. all data
B. the time dimension
C. no data
D. the last transaction

Antwort: C

Begründung:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

32. Frage
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
B. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
C. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
D. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
E. Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Antwort: E

33. Frage
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud Dataflow to transform data to support query patterns.
B. Use Cloud SQL for storag
C. Use Cloud Dataflow to transform data to support query patterns.
D. Add secondary indexes to support query patterns.
E. Use Cloud Spanner for storag
F. Add secondary indexes to support query patterns.
G. Use Cloud Spanner for storag
H. Use Cloud SQL for storag

Antwort: A

Begründung:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

34. Frage
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