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QUESTION 44
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Subsample your test dataset.

Subsample your training dataset.

Increase the number of input features to your model.

Increase the number of layers in your neural network.

Explanation/Reference:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-
9f5d1c6f407d

QUESTION 45
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

[0, 5, 0, 0, 0, 0]

[0, 0, 0, 1, 0, 0, 1]

[0, 1]

[1, 0, 0, 0, 0, 0, 0]

Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent ‘eye_color’ as a length 3 vector: ‘brown’ would become [1, 0, 0], ‘blue’ would become [0, 1, 0] and
‘green’ would become [0, 0, 1]. These vectors are called “sparse” because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single
1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

QUESTION 46
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

Deploy small Kafka clusters in your data centers to buffer events.

Have the data acquisition devices publish data to Cloud Pub/Sub.

Establish a Cloud Interconnect between all remote data centers and Google.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

Pubsub is global service with high message delivery capacity.

QUESTION 47
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy?

Grant the consultant the Viewer role on the project.

Grant the consultant the Cloud Dataflow Developer role on the project.

Create a service account and allow the consultant to log on with it.

Create an anonymized sample of the data for the consultant to work with in a different project.

QUESTION 48
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Subsample your test dataset.

Subsample your training dataset.

Increase the number of input features to your model.

Increase the number of layers in your neural network.

Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d

QUESTION 49
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Increase the cluster size with more non-preemptible workers.

Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

QUESTION 50
You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.
What should you do?

Send the data to Google Cloud Datastore and then export to BigQuery.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Pubsub for realtime, Dataflow for pipeline, Bigquery for analytics.

QUESTION 51
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.

Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

QUESTION 52
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Threading

Serialization

Dropout Methods

Dimensionality Reduction

Explanation/Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877

QUESTION 53
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Explanation

QUESTION 54
Which of the following statements is NOT true regarding Bigtable access roles?

Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

You can configure access control only at the project level.

To give a user access to only one table in a project, you must configure access through your application.

For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.
Reference: https://cloud.google.com/bigtable/docs/access-control

QUESTION 55
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

Use Transfer Appliance to copy the data to Cloud Storage

Use gsutil cp -Jto compress the content being uploaded to Cloud Storage

Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic

Explanation

QUESTION 56
Which Java SDK class can you use to run your Dataflow programs locally?

LocalRunner

DirectPipelineRunner

MachineRunner

LocalPipelineRunner

DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests Reference: https://cloud.google.com/dataflow/java- sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRunner

QUESTION 57
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Cloud composer is used to schedule the interdependent jobs.

QUESTION 58
Which of these statements about BigQuery caching is true?

By default, a query’s results are not cached.

BigQuery caches query results for 48 hours.

Query results are cached even if you specify a destination table.

There is no charge for a query that retrieves its results from cache.

When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query’s results are always cached except under certain conditions, such as if you specify a destination table.

QUESTION 59
You are responsible for writing your company’s ETL pipelines to run on an Apache Hadoop cluster. The
pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the
pipelines?

PigLatin using Pig

HiveQL using Hive

Java using MapReduce

Python using MapReduce

QUESTION 60
Why do you need to split a machine learning dataset into training data and test data?

So you can try two different sets of features

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

To allow you to create unit tests in your code

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

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

QUESTION 61
How can you get a neural network to learn about relationships between categories in a categorical feature?

Create a multi-hot column

Create a one-hot column

Create a hash bucket

Create an embedding column

Explanation
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn’t encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let’s say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-dee

QUESTION 62
If you’re running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Do not use a production instance.

Run your test for at least 10 minutes.

Before you test, run a heavy pre-test for several minutes.

Use at least 300 GB of data.

If you’re running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.

QUESTION 63
MJTelco Case Study
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?

Rowkey: date#device_id
Column data: data_point

Rowkey: date
Column data: device_id,data_point

Rowkey: device_id
Column data: date, data_point

Rowkey: data_point
Column data: device_id,date

Rowkey: date#data_point
Column data: device_id

QUESTION 64
You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?

Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.

Build and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the
models using Cloud Dataproc. Call the models from your application.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user’s viewing history to generate
preferences.

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