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Title: Valid Professional-Data-Engineer Exam Experience | Practice Professional-Data-En [Print This Page]
Author: davidki970    Time: yesterday 21:34
Title: Valid Professional-Data-Engineer Exam Experience | Practice Professional-Data-En
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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?
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?
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?
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?
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.)
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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