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Google Data Engineer 認定 GCP-DE 試験問題:

1. Which of these is not a supported method of putting data into a partitioned table?

A) Create a partitioned table and stream new records to it every day.
B) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
C) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
D) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".

2. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
B) Use the WHERE_PARTITIONTIME pseudo column
C) Use the TABLE_DATE_RANGE function
D) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD

3. 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 dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

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

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

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

質問と回答：