Professional-Data-Engineer Fragen Und Antworten & Professional-Data-Engineer Prüfungsübungen

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	20%	- Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data - Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost
Topic 2: Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection - Designing for business requirements 1. Designing for scalability and elasticity 2. Designing for reliability and fault tolerance 3. Selecting appropriate storage solutions
Topic 3: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Topic 4: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation
Topic 5: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues

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Professional-Data-Engineer Übungsmaterialien - Professional-Data-Engineer Lernressourcen & Professional-Data-Engineer Prüfungsfragen

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q410-Q415):

410. Frage
Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

A. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
B. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
C. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
D. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

Antwort: D

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

Antwort: B

Begründung:
Bigquery understands UTF-8 encoding anything other than that will result in data issues with schema.

412. Frage
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

A. Disable caching by editing the report settings.
B. Clear your browser history for the past hour then reload the tab showing the virtualizations.
C. Disable caching in BigQuery by editing table details.
D. Refresh your browser tab showing the visualizations.

Antwort: A

Begründung:
https://support.google.com/datastudio/answer/7020039?hl=en

413. Frage
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

A. Write a job template in Cloud Dataproc to perform the data transfer.
B. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
C. Execute gsutil rsyncfrom the on-premises servers.
D. Use Cloud Dataflow and write the data to Cloud Storage.

Antwort: D

414. Frage
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Add capacity (memory and disk space) to the database server by the order of 200.
B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
C. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
D. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

Antwort: B

Begründung:
Explanation

415. Frage
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