Professional-Data-Engineer Zertifizierung, Professional-Data-Engineer Fragen&Antworten

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Designing for reliability and fidelity 1. Recovering from failures 2. Performing data quality and validation checks 3. Planning for monitoring and alerting - Automating data processes 1. Scheduling jobs 2. Workflow orchestration 3. Continuous integration and continuous deployment (CI/CD)
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Selecting appropriate storage technologies 1. Mapping storage options to business requirements 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB - Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cluster sizing and autoscaling 3. Cost optimization - Designing data pipelines 1. Integrating with new data sources 2. AI data enrichment 3. Streaming (e.g., windowing, late arriving data) 4. Processing logic 5. Batch processing 6. Data acquisition and import
Topic 3: Preparing and using data for analysis (~15% of the exam)	15%	- Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration - Preparing data for visualization 1. Connecting to Looker and other BI tools 2. Preparing data for reporting and dashboards
Topic 4: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Auditing, privacy, and compliance 2. Data encryption 3. Identity and Access Management (IAM) - Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows) - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle
Topic 5: Storing the data (~20% of the exam)	20%	- Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Processing data 3. Monitoring the data lake - Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Designing the data model 3. Mapping business requirements 4. Deciding the degree of data normalization - Designing for a data platform 1. Building a federated governance model for distributed data systems 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage - Selecting storage systems 1. Lifecycle management of data 2. Planning for storage costs and performance 3. Analyzing data access patterns

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Professional-Data-Engineer Fragen&Antworten, Professional-Data-Engineer Buch

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q406-Q411):

406. Frage
You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
* Each department should have access only to their data.
* Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
* Each department has data analysts who need to be able to query but not modify data.
How should you set access to the data in BigQuery?

A. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
B. Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.
C. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
D. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

Antwort: D

407. Frage
You have a variety of files in Cloud Storage that your data science team wants to use in their models Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

A. Create an external table in BigQuery and use SQL to transform the data as necessary Provide the data science team access to the external tables to explore the raw data.
B. Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
C. Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
D. Load the data into BigQuery and use SQL to transform the data as necessary Provide the data science team access to staging tables to explore the raw data.

Antwort: B

Begründung:
Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure.
The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep. References:
* Dataprep overview
* Dataprep features
* Dataprep and BigQuery integration

408. Frage
You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Access historical data by using time travel in BigQuery.
B. Export the data from BigQuery into a new table that excludes the corrupted data
C. Migrate your data to multi-region BigQuery buckets.
D. Create a BigQuery table snapshot on a daily basis.

Antwort: A

409. Frage
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?

A. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
B. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
C. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
D. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

Antwort: A

Begründung:
Explanation

410. Frage
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Include multiple time series values within the row key
B. Keep your row key reasonably short
C. Keep the row keep as an 8 bit integer
D. Keep your row key as long as the field permits

Antwort: B

Begründung:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference:
https://cloud.google.com/bigtable/docs/schema-design#row-keys

411. Frage
......

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