- (Exam Topic 5)
Which software libraries are supported by Cloud Machine Learning Engine?

1. A. Theano and TensorFlow
2. B. Theano and Torch
3. C. TensorFlow
4. D. TensorFlow and Torch

Correct Answer: C
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does

- (Exam Topic 6)
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

1. A. Create a cron schedule in Cloud Dataprep.
2. B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
3. C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
4. D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Correct Answer: D

- (Exam Topic 6)
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

1. A. Select random samples from the tables using the RAND() function and compare the samples.
2. B. Select random samples from the tables using the HASH() function and compare the samples.
3. C. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sortin
4. D. Compare the hashes of each table.
5. E. Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Correct Answer: B

- (Exam Topic 1)
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs. BigQueryIO.Read
.named(“ReadLogData”)
.from(“clouddataflow-readonly:samples.log_data”)
You want to improve the performance of this data read. What should you do?

1. A. Specify the TableReference object in the code.
2. B. Use .fromQuery operation to read specific fields from the table.
3. C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
4. D. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Correct Answer: D

- (Exam Topic 6)
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

1. A. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery
2. B. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
3. C. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
4. D. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery

Correct Answer: A