PASS GUARANTEED QUIZ GOOGLE - ACCURATE PROFESSIONAL-DATA-ENGINEER - GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM RELIABLE TEST LABS

Pass Guaranteed Quiz Google - Accurate Professional-Data-Engineer - Google Certified Professional Data Engineer Exam Reliable Test Labs

Pass Guaranteed Quiz Google - Accurate Professional-Data-Engineer - Google Certified Professional Data Engineer Exam Reliable Test Labs

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Google Professional-Data-Engineer certification exam is designed to assess an individual's ability to design, build, and maintain data processing systems using Google Cloud Platform technologies. Google Certified Professional Data Engineer Exam certification exam is intended for professionals who have experience working with data technologies and are looking to enhance their skills and knowledge in cloud-based data engineering. Professional-Data-Engineer Exam covers a wide range of topics such as data storage, data processing, data analysis, machine learning, and data visualization.

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Google Professional-Data-Engineer Certification Exam is a valuable credential for individuals seeking to advance their careers in data engineering and analytics. It validates an individual's knowledge and expertise in Google Cloud technologies and demonstrates their ability to design and implement data solutions that meet business requirements. By passing this certification exam, individuals can enhance their credibility and marketability in the competitive field of data engineering and analytics.

Google Certified Professional Data Engineer Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The
model fits well for the training data. However, when tested against new data, it performs poorly. What
method can you employ to address this?

  • A. Dimensionality Reduction
  • B. Serialization
  • C. Threading
  • D. Dropout Methods

Answer: D

Explanation:
Explanation/Reference:
Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-
tensorflow-30505541d877


NEW QUESTION # 24
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

  • A. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
  • B. Introduce resource hierarchy to leverage access control policy inheritance.
  • C. Use Cloud Deployment Manager to automate access provision.
  • D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
  • E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: A,C

Explanation:
Explanation


NEW QUESTION # 25
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

  • A. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
  • B. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
  • C. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
  • D. Put the data into Google Cloud Storage.

Answer: C


NEW QUESTION # 26
When a Cloud Bigtable node fails, ____ is lost.

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

Answer: B

Explanation:
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 # 27
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning.
Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

  • A. Tier older data onto Google Cloud Storage files and create a BigQuery table using GCS as an external data source.
  • B. Implement clustering in BigQuery on the ingest date column.
  • C. Implement clustering in BigQuery on the package-tracking ID column.
  • D. Re-create the table using data partitioning on the package delivery date.

Answer: C


NEW QUESTION # 28
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