Question 1

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query: SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:What is the most likely cause of the delay for this query?

Options :

A : Users are running too many concurrent queries in the system

B : The [myproject:mydataset.mytable] table has too many partitions

C : Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

D : Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Answer: A

Question 2

You are training a deep learning model for a classification task. The precision and recall of the model is quite low. What could you do to improve the precision and recall scores?

Options :

A : Use L1 regularization

B : Use L2 regularization

C : Use more training instances

D : Use dropout

Answer: C

Question 3

You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

Options :

A : Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

B : Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

C : Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

D : Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

Answer: B

Question 4

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Options :

A : Issue a command to restart the database servers.

B : Retry the query with exponential backoff, up to a cap of 15 minutes.

C : Retry the query every second until it comes back online to minimize staleness of data.

D : Reduce the query frequency to once every hour until the database comes back online.

Answer: B

Question 5

You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues,including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistentformatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

Options :

A : Use Data Fusion to transform the data before loading it into BigQuery.

B : Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.

C : Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.

D : Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

Answer: A

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