PROFESSIONAL-DATA-ENGINEER VALID EXAM LABS & VALID PROFESSIONAL-DATA-ENGINEER TEST ANSWERS

Professional-Data-Engineer Valid Exam Labs & Valid Professional-Data-Engineer Test Answers

Professional-Data-Engineer Valid Exam Labs & Valid Professional-Data-Engineer Test Answers

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Nowadays, computers develop rapidly, and it makes our daily life and work more convenient. IT workers positions are popular in 21th century. Google Professional-Data-Engineer exam questions are also known by many IT certification candidates. If candidates can get a golden certification, senior positions with high salary and good benefits are waiting for you. Our latest and Valid Professional-Data-Engineer Exam Questions may be the best helper for candidates working for Google certifications.

The Google Professional-Data-Engineer Exam covers a wide range of topics, including data processing systems, data analysis, machine learning, and data security on Google Cloud Platform. Candidates are expected to have a thorough understanding of these topics and be able to apply them in real-world scenarios.

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Google Certified Professional Data Engineer Exam Sample Questions (Q346-Q351):

NEW QUESTION # 346
Why do you need to split a machine learning dataset into training data and test data?

  • A. So you can use one dataset for a wide model and one for a deep model
  • B. To make sure your model is generalized for more than just the training data
  • C. So you can try two different sets of features
  • D. To allow you to create unit tests in your code

Answer: B

Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/


NEW QUESTION # 347
Which of the following is not possible using primitive roles?

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

Answer: D

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 # 348
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

  • A. There are very few occurrences of mutations relative to normal samples.
  • B. There are roughly equal occurrences of both normal and mutated samples in the database.
  • C. You expect future mutations to have different features from the mutated samples in the database.
  • D. You already have labels for which samples are mutated and which are normal in the database.
  • E. You expect future mutations to have similar features to the mutated samples in the database.

Answer: A,E

Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
https://en.wikipedia.org/wiki/Anomaly_detection


NEW QUESTION # 349
Which of these is NOT a way to customize the software on Dataproc cluster instances?

  • A. Log into the master node and make changes from there
  • B. Set initialization actions
  • C. Modify configuration files using cluster properties
  • D. Configure the cluster using Cloud Deployment Manager

Answer: D

Explanation:
Explanation
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.
When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties


NEW QUESTION # 350
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

  • A. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format
  • B. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used
  • C. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
  • D. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

Answer: D


NEW QUESTION # 351
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