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[General] Valid Professional-Data-Engineer Exam Experience | Practice Professional-Data-En

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【General】 Valid Professional-Data-Engineer Exam Experience | Practice Professional-Data-En

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In contemporary society, information is very important to the development of the individual and of society (Professional-Data-Engineer practice test), and information technology gives considerable power to those able to access and use it. Therefore, we should dare to explore, and be happy to accept new things. In terms of preparing for exams, we really should not be restricted to paper material, there are so many advantages of our electronic Professional-Data-Engineer Study Guide, such as High pass rate, Fast delivery and free renewal for a year to name but a few. I can assure you that you will pass the exam as well as getting the related certification as easy as rolling off a log.
How to book Google Professional Data Engineer ExamsThe registration for the Google Professional Data Engineer Exam follows the steps given below.
  • Step1: Visit the Google Cloud Webassessor Website
  • Step2: Sign in or sign up to your Google Cloud Webassessor account
  • Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
  • Step3: Search for the exam name Google Professional Data Engineer
Practice Professional-Data-Engineer Exam Online - Professional-Data-Engineer Best PracticeThe Professional-Data-Engineer study guide to good meet user demand, will be a little bit of knowledge to separate memory, every day we have lots of fragments of time, such as waiting in line to take when you eat, or time in buses commute on the way by subway every day, but when you add them together will be surprised to find a day we can make use of the time is so much debris. We have three version of our Professional-Data-Engineer Exam Questions which can let you study at every condition so that you can make full use of your time. And you will get the Professional-Data-Engineer certification for sure.
Google Professional-Data-Engineer Certification Exam is an online exam that tests the candidate's knowledge and skills in various areas such as data engineering, data analysis, machine learning, and big data processing. Professional-Data-Engineer exam consists of multiple-choice questions and requires a thorough understanding of the Google Cloud Platform, its services, and its features. Candidates need to demonstrate their proficiency in designing and implementing data processing systems that meet business requirements.
Google Certified Professional Data Engineer Exam Sample Questions (Q38-Q43):NEW QUESTION # 38
You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?
  • A. Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google-managed encryption key.
  • B. Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.
  • C. Create a new BigOuory table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
  • D. Create a new BigOuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old Bigauery table.
Answer: C
Explanation:
To use CMEK for BigQuery, you need to create a key ring and a key in Cloud KMS, and then specify the key resource name when creating or updating a BigQuery table. You cannot change the encryption type of an existing table, so you need to create a new table with CMEK and copy the data from the old table with Google-managed encryption key.
Reference:
Customer-managed Cloud KMS keys | BigQuery | Google Cloud
Creating and managing encryption keys | Cloud KMS Documentation | Google Cloud

NEW QUESTION # 39
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
  • A. Trigger that is a combination of other triggers
  • B. Trigger based on element count
  • C. Trigger based on element size in bytes
  • D. Trigger based on time
Answer: C
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 # 40
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?
  • A. Combine highly co-dependent features into one representative feature.
  • B. Remove the features that have null values for more than 50% of the training records.
  • C. Eliminate features that are highly correlated to the output labels.
  • D. Instead of feeding in each feature individually, average their values in batches of 3.
Answer: A

NEW QUESTION # 41
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum.
What should you do?
  • A. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
  • B. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.
  • C. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
  • D. Move your data to Cloud Storage. Run your jobs on Dataproc.
Answer: D
Explanation:
Dataproc's Compatibility with Apache Sparkataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
Cloud Storage as a Scalable Data Lake:Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
Minimizing Operational Overhead:By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
Tight Timeline and Minimal Code Changes:This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
A). Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances:This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
C). Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach:While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
D). Rewrite your jobs in Apache Beam. Run your jobs in Dataflow:Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.

NEW QUESTION # 42
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)
  • A. Reinforcement learning to predict the location of a transaction.
  • B. Unsupervised learning to predict the location of a transaction.
  • C. Clustering to divide the transactions into N categories based on feature similarity.
  • D. Supervised learning to determine which transactions are most likely to be fraudulent.
  • E. Unsupervised learning to determine which transactions are most likely to be fraudulent.
  • F. Supervised learning to predict the location of a transaction.
Answer: C,E,F
Explanation:
Fraud is not a feature, so unsupervised, location is given so supervised, Clustering can be done looking at the done with same features.

NEW QUESTION # 43
......
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