PASS GUARANTEED QUIZ 2025 GOOGLE THE BEST PROFESSIONAL-DATA-ENGINEER: GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM EXAM PASSING SCORE

Pass Guaranteed Quiz 2025 Google The Best Professional-Data-Engineer: Google Certified Professional Data Engineer Exam Exam Passing Score

Pass Guaranteed Quiz 2025 Google The Best Professional-Data-Engineer: Google Certified Professional Data Engineer Exam Exam Passing Score

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Google Certified Professional Data Engineer Exam Sample Questions (Q314-Q319):

NEW QUESTION # 314
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. Remove the features that have null values for more than 50% of the training records.
  • B. Combine highly co-dependent features into one representative feature.
  • 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: B


NEW QUESTION # 315
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

  • A. Regressor
  • B. Unsupervised learning
  • C. Clustering estimator
  • D. Classifier

Answer: A

Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms


NEW QUESTION # 316
Your company is implementing a data warehouse using BigQuery and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

  • A. Denormalize the data
  • B. Shard the data by customer ID
  • C. Materialize the dimensional data in views
  • D. Partition the data by transaction date

Answer: D


NEW QUESTION # 317
How would you query specific partitions in a BigQuery table?

  • A. Use the EXTRACT(DAY) clause
  • B. Use the __PARTITIONTIME pseudo-column in the WHERE clause
  • C. Use DATE BETWEEN in the WHERE clause
  • D. Use the DAY column in the WHERE clause

Answer: B

Explanation:
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date- based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND
TIMESTAMP('2017-01-02')
Reference: https://cloud.google.com/bigquery/docs/partitioned-
tables#the_partitiontime_pseudo_column


NEW QUESTION # 318
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

  • A. An hourly watermark
  • B. A processing time trigger
  • C. The with Allowed Lateness method
  • D. An event time trigger

Answer: B

Explanation:
Explanation
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline.
Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers


NEW QUESTION # 319
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