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?

• A.Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
• B.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
• C.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.
• D.Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery

Comment: *

Name: *

Email: *

Verification: *

Which of these statements about BigQuery caching is true?

• A.By default, a query's results are not cached.
• B.BigQuery caches query results for 48 hours.
• C.Query results are cached even if you specify a destination table.
• D.There is no charge for a query that retrieves its results from cache.

Comment: *

Name: *

Email: *

Verification: *

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

• A.Subsample your test dataset.
• B.Subsample your training dataset.
• C.Increase the number of input features to your model.
• D.Increase the number of layers in your neural network.

Comment: *

Name: *

Email: *

Verification: *

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use?
(Choose three.)

• A.Unsupervised learning to predict the location of a transaction.
• B.Supervised learning to determine which transactions are most likely to be fraudulent.
• C.Unsupervised learning to determine which transactions are most likely to be fraudulent.
• D.Clustering to divide the transactions into N categories based on feature similarity.
• E.Reinforcement learning to predict the location of a transaction.
• F.Supervised learning to predict the location of a transaction.

Comment: *

Name: *

Email: *

Verification: *

MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

• A.BigQuery and Cloud Bigtable
• B.Cloud Bigtable and Cloud SQL
• C.Cloud Datastore and Cloud Bigtable
• D.BigQuery and Cloud Storage

Comment: *

Name: *

Email: *

Verification: *