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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer |
| Exam Number: | Professional-Data-Engineer |
| Related Certifications: | Google Cloud Certified Professional Data Engineer |
| Exam Format: | Multiple-choice, Multiple-select |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Real Exam Qty: | 50-60 |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Japanese |
| Passing Score: | Not officially published (estimated ~80%) |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online (remote proctored) or at a testing center (Kryterion) |
| Pre Condition: | No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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Google Professional-Data-Engineer certification exam is a highly sought-after certification in the field of data engineering. Google Certified Professional Data Engineer Exam certification is designed for professionals who possess the necessary skills and knowledge to design, build, and maintain data processing systems. Professional-Data-Engineer Exam is intended to test the candidate's ability to work with large volumes of data, design scalable data processing systems, and implement solutions that are highly available, reliable, and secure.
NEW QUESTION # 393
You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?
Answer: C
NEW QUESTION # 394
Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?
Answer: C
Explanation:
To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:
Data Mesh Architecture:
Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.
Dataplex Lakes and Zones:
Lakes in Dataplex are logical containers for managing data at scale, and zones are subdivisions within lakes for organizing data based on domains, teams, or other criteria.
Domain and Team Management:
By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.
This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.
Implementation Steps:
Create Lakes and Zones:
Create separate lakes in Dataplex for each team (analytics and data science).
Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).
Attach BigQuery Datasets:
Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.
Decentralized Management:
Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.
Reference:
Dataplex Documentation
BigQuery Documentation
Data Mesh Principles
NEW QUESTION # 395
Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?
Answer: D
Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
NEW QUESTION # 396
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
Answer: A,B
Explanation:
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
NEW QUESTION # 397
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
- Decoupling producer from consumer
- Space and cost-efficient storage of the raw ingested data, which is
to be stored indefinitely
- Near real-time SQL query
- Maintain at least 2 years of historical data, which will be queried
with SQL
Which pipeline should you use to meet these requirements?
Answer: C
NEW QUESTION # 398
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