VERIFIED PROFESSIONAL-DATA-ENGINEER ANSWERS - EXAMCOLLECTION PROFESSIONAL-DATA-ENGINEER DUMPS

Verified Professional-Data-Engineer Answers - Examcollection Professional-Data-Engineer Dumps

Verified Professional-Data-Engineer Answers - Examcollection Professional-Data-Engineer Dumps

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Google Professional-Data-Engineer certification exam tests a candidate's proficiency in using Google Cloud Platform tools and services for data processing, such as Google Cloud Dataflow, Google BigQuery, Google Cloud Dataproc, and Google Cloud Pub/Sub. Professional-Data-Engineer exam also assesses a candidate's ability to design and implement data processing systems that are secure, reliable, and cost-effective.

To become a Google Certified Professional Data Engineer, a candidate must pass the certification exam, which costs $200. Professional-Data-Engineer exam is available in English, Japanese, and Spanish and can be taken online or at a testing center. Professional-Data-Engineer Exam is valid for two years, after which a candidate must recertify to maintain their certification.

Google Professional-Data-Engineer certification has become a must-have for data engineering professionals who work with Google Cloud. Google Certified Professional Data Engineer Exam certification validates their knowledge and skills in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification also helps professionals to stand out in a competitive job market and advance their careers.

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Google Certified Professional Data Engineer Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

  • A. PCollection
  • B. Sink API
  • C. Pipeline
  • D. Transform

Answer: D

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.


NEW QUESTION # 15
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

  • A. Python using MapReduce
  • B. PigLatin using Pig
  • C. HiveQL using Hive
  • D. Java using MapReduce

Answer: A


NEW QUESTION # 16
When a Cloud Bigtable node fails, ____ is lost.

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

Answer: C

Explanation:
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


NEW QUESTION # 17
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

  • A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
  • B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
  • C. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
  • D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Answer: B

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.


NEW QUESTION # 18
You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

  • A. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
  • B. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
  • C. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
  • D. Include ORDER BY DESK on timestamp column and LIMIT to 1.

Answer: B

Explanation:
Explanation
https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts


NEW QUESTION # 19
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