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Google Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Google Cloud |
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| Exam Name: | Google Cloud Certified Professional Data Engineer |
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| Exam Number: | Professional-Data-Engineer |
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| Available Languages: | Japanese, English |
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| Exam Format: | Multiple-select, Multiple-choice |
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| Related Certifications: | Google Cloud Certified Professional Data Engineer |
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| Exam Duration: | 120 minutes |
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| Real Exam Qty: | 50-60 |
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| Exam Price: | $200 USD |
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| Certificate Validity Period: | 2 years |
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| Passing Score: | Not officially published (estimated ~80%) |
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| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
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| Exam Way: | Online (remote proctored) or at a testing center (Kryterion) |
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| Pre Condition: | No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. |
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| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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Google Professional-Data-Engineer exam is a certification exam offered by Google Cloud Platform. It is designed for data professionals who are looking to demonstrate their expertise in designing, building, and managing data processing systems on Google Cloud Platform. Professional-Data-Engineer Exam Tests the candidate's knowledge of data engineering, data processing, and data analysis techniques using Google Cloud Platform tools and services.
Google Certified Professional Data Engineer Exam Sample Questions (Q314-Q319):
NEW QUESTION # 314
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?
- A. A stock symbol followed by a timestamp
- B. A sequential numeric ID
- C. A timestamp followed by a stock symbol
- D. A non-sequential numeric ID
Answer: B,C
Explanation:
...using a timestamp as the first element of a row key can cause a variety of problems. In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting. Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotting
NEW QUESTION # 315
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
- A. Trigger based on element size in bytes
- B. Trigger that is a combination of other triggers
- C. Trigger based on element count
- D. Trigger based on time
Answer: A
Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way
NEW QUESTION # 316
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
- A. Pig
- B. YARN
- C. Spark
- D. Hive
Answer: A,C,D
Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference:
https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run
NEW QUESTION # 317
You are architecting a data transformation solution for BigQuery. Your developers are proficient with SQL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?
- A. Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.
- B. Use Data Fusion to build and execute ETL pipelines.
- C. Use Dataform to build, manage, and schedule SQL pipelines.
- D. Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.
Answer: C
NEW QUESTION # 318
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? (Choose two.)
- A. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- B. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
- C. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
- D. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
- E. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
Answer: B,D
NEW QUESTION # 319
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