USE REAL GOOGLE PROFESSIONAL-DATA-ENGINEER EXAM QUESTIONS AND ACHIEVE BRILLIANT RESULTS

Use Real Google Professional-Data-Engineer Exam Questions And Achieve Brilliant Results

Use Real Google Professional-Data-Engineer Exam Questions And Achieve Brilliant Results

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Google Professional-Data-Engineer certification exam is a rigorous and comprehensive exam that requires individuals to have a deep understanding of data engineering technologies and concepts. Professional-Data-Engineer exam consists of multiple choice and scenario-based questions that assess an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer Exam is timed and individuals have a limited amount of time to complete the exam. To pass the exam, individuals must score 70% or higher.

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Google Professional-Data-Engineer Certification is a highly respected and in-demand certification for data professionals. Google Certified Professional Data Engineer Exam certification is designed for individuals who possess the knowledge and skills to design, build, maintain, and troubleshoot data processing systems with a particular emphasis on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is offered by Google and is recognized globally as a valuable credential for professionals in the data engineering field.

Google Certified Professional Data Engineer Exam Sample Questions (Q164-Q169):

NEW QUESTION # 164
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

  • A. Continuous features
  • B. Weights
  • C. Input values
  • D. Biases

Answer: B,D

Explanation:
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground


NEW QUESTION # 165
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

  • A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
  • B. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
  • C. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
  • D. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

Answer: D

Explanation:
A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite).
https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design


NEW QUESTION # 166
Which of the following is not possible using primitive roles?

  • A. Give GroupA owner access and GroupB editor access for all datasets in a project.
  • B. Give UserA owner access and UserB editor access for all datasets in a project.
  • C. Give a user access to view all datasets in a project, but not run queries on them.
  • D. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.

Answer: C

Explanation:
Explanation
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles


NEW QUESTION # 167
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user- level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

  • A. Create and share an authorized view that provides the aggregate results.
  • B. Create and share a new dataset and view that provides the aggregate results.
  • C. Create and share a new dataset and table that contains the aggregate results.
  • D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Answer: D

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/bigquery/docs/access-control


NEW QUESTION # 168
You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

  • A. Insufficient permissions
  • B. Graph or pipeline construction
  • C. Job validation
  • D. Exceptions in worker code

Answer: D

Explanation:
https://cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline#detect_an_exception_in_worker_c While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface.


NEW QUESTION # 169
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