100% PASS QUIZ 2025 HIGH-QUALITY GOOGLE PROFESSIONAL-DATA-ENGINEER: RELIABLE GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM DUMPS PDF

100% Pass Quiz 2025 High-quality Google Professional-Data-Engineer: Reliable Google Certified Professional Data Engineer Exam Dumps Pdf

100% Pass Quiz 2025 High-quality Google Professional-Data-Engineer: Reliable Google Certified Professional Data Engineer Exam Dumps Pdf

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Google Professional-Data-Engineer exam is a certification exam offered by Google Cloud Platform for data professionals who want to demonstrate their expertise in designing, building, and managing data processing systems on Google Cloud Platform. It is a highly valued certification in the industry and is especially relevant for those looking to work with Big Data. Professional-Data-Engineer exam tests a candidate's knowledge of various data engineering tools and technologies, and passing the exam demonstrates that the candidate has the skills and knowledge to design and implement data solutions on Google Cloud Platform.

The Google Professional-Data-Engineer Exam for the Google Professional-Data-Engineer certification is a comprehensive and challenging test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions, which require candidates to apply their knowledge to real-world scenarios. Candidates are required to demonstrate their expertise in areas such as data processing, data analysis, data integration, and data visualization.

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Google Certified Professional Data Engineer Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

  • A. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.
  • B. Rewrite the job in Apache Spark.
  • C. Rewrite the job in Pig.
  • D. Increase the size of the Hadoop cluster.

Answer: B

Explanation:
Spark performs in-memory processing and faster, which results in optimization of job's processing time.


NEW QUESTION # 214
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)

  • A. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
  • B. Denormalize the data as must as possible.
  • C. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
  • D. Preserve the structure of the data as much as possible.
  • E. Use BigQuery UPDATE to further reduce the size of the dataset.

Answer: A,C


NEW QUESTION # 215
You are designing the database schema for a machine learning-based food ordering service that will
predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat

The user account information: Name, address, preferred meal times

The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data
schema. Which Google Cloud Platform product should you use?

  • A. Cloud Datastore
  • B. Cloud Bigtable
  • C. BigQuery
  • D. Cloud SQL

Answer: C


NEW QUESTION # 216
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

  • A. 1 continuous and 2 categorical
  • B. 3 continuous
  • C. 3 categorical
  • D. 2 continuous and 1 categorical

Answer: D

Explanation:
The columns can be grouped into two types-categorical and continuous columns:
A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.
A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.
Year of birth and income are continuous columns. Country is a categorical column.
You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.
Reference: https://www.tensorflow.org/tutorials/wide#reading_the_census_data


NEW QUESTION # 217
You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

  • A. Use federated tables in BigQuery with queries to detect errors and perform transformations.
  • B. Use Cloud Dataflow with Beam to detect errors and perform transformations.
  • C. Use Cloud Dataprep with recipes to detect errors and perform transformations.
  • D. Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.

Answer: C


NEW QUESTION # 218
......

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