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NEW QUESTION 49
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and- shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery.
You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

• A. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
• B. Use Cloud ML Engine for training existing Spark ML models
• C. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
• D. Rewrite your models on TensorFlow, and start using Cloud ML Engine

Answer: A

NEW QUESTION 50
Which software libraries are supported by Cloud Machine Learning Engine?

• A. TensorFlow
• B. Theano and TensorFlow
• C. TensorFlow and Torch
• D. Theano and Torch

Answer: A

Explanation:
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.

NEW QUESTION 51
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

• A. 'bigquery-public-data.noaa_gsod.gsod'*
• B. 'bigquery-public-data.noaa_gsod.gsod'
• C. 'bigquery-public-data.noaa_gsod.gsod*`
• D. bigquery-public-data.noaa_gsod.gsod*

Answer: D

NEW QUESTION 52
Case Study: 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments ?development/test, staging, and production ?
to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

• A. Rowkey: date#device_idColumn data: data_point
• B. Rowkey: dateColumn data: device_id, data_point
• C. Rowkey: device_idColumn data: date, data_point
• D. Rowkey: data_pointColumn data: device_id, date
• E. Rowkey: date#data_pointColumn data: device_id

Answer: D

NEW QUESTION 53
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

• A. Modify your Cloud Dataflow pipeline to use the Flattentransform before writing to Cloud Bigtable
• B. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkersin PipelineOptions
• C. Modify your Cloud Dataflow pipeline to use the CoGroupByKeytransform before writing to Cloud Bigtable
• D. Configure your Cloud Dataflow pipeline to use local execution
• E. Increase the number of nodes in the Cloud Bigtable cluster

Answer: A,C

Explanation:
Explanation/Reference: https://www.slideshare.net/LucasArruda3/how-to-build-an-etl-pipeline-with-apache-beam-on- google-cloud-dataflow

NEW QUESTION 54
......

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