Question 1

Why do you need to split a machine learning dataset into training data and test data?

Options :

A : So you can try two different sets of features

B : To make sure your model is generalized for more than just the training data

C : To allow you to create unit tests in your code

D : So you can use one dataset for a wide model and one for a deep model

Answer: B

Question 2

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every thirdmonth the schema of the files changes. Your requirements for implementing these transformations include:Executing the transformations on a scheduleEnabling non-developer analysts to modify transformationsProviding a graphical tool for designing transformationsWhat should you do?

Options :

A : Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

B : Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

C : Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

D : Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Answer: D

Question 3

Which Java SDK class can you use to run your Dataflow programs locally?

Options :

A : LocalRunner

B : DirectPipelineRunner

C : MachineRunner

D : LocalPipelineRunner

Answer: B

Question 4

You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.

Options :

A : Publisher throughput quota is too small.

B : Total outstanding messages exceed the 10-MB maximum.

C : Error handling in the subscriber code is not handling run-time errors properly.

D : The subscriber code cannot keep up with the messages.

E : The subscriber code does not acknowledge the messages that it pulls.

Answer: C,D

Question 5

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

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