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GCP-DE試験勉強資料のデモを無料に提供して、お客様が購入前に試験勉強資料の問題と解答をチェックします。購入前に我が社のGCP-DE試験勉強資料デモをダウンロードできます。お客様の支払い終了に、10分以内にGCP-DE試験勉強資料を受けます。

Google Data Engineer 認定 GCP-DE 試験問題:

1. You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A) Sink API
B) Data extraction
C) ParDo
D) Source API

2. 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) Instead of feeding in each feature individually, average their values in batches of 3.
B) Combine highly co-dependent features into one representative feature.
C) Eliminate features that are highly correlated to the output labels.
D) Remove the features that have null values for more than 50% of the training records.

3. What are two methods that can be used to denormalize tables in BigQuery?

A) 1) Use a partitioned table; 2) Join tables into one table
B) 1) Use nested repeated fields; 2) Use a partitioned table
C) 1) Join tables into one table; 2) Use nested repeated fields
D) 1) Split table into multiple tables; 2) Use a partitioned table

4. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Call the model from your application and process the results as labels.
B) Deploy the model using Cloud Machine Learning Engin
C) Call the Cloud Natural Language API from your applicatio
D) Call the Cloud Natural Language API from your applicatio
E) Process the generated Sentiment Analysis as labels.
F) Process the generated Entity Analysis as labels.
G) Call the model from your application and process the results as labels.
H) Build and train a text classification model using TensorFlo
I) Build and train a text classification model using TensorFlo
J) Deploy the model using a KubernetesEngine cluste

5. You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A) Cloud Bigtable
B) BigQuery
C) Cloud Datastore
D) Cloud SQL for PostgreSQL

質問と回答：