GCP machine learning 2023 questions and answers verified correctly
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GCP machine learning 2023 questions and answers verified correctly
Assessment 14 - A group of attorneys has hired you to help them categorize over a million documents in an intellectual property case. The attorneys need to isolate documents that are relevant to a patent that the plaintiffs argue...
gcp machine learning 2023 questions and answers verified correctly assessment 14 a group of attorneys has hired you to help them categorize over a million documents in an intellectual property case
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GCP machine learning 2023 questions and answers
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Assessment 14 - A group of attorneys has hired you to help them categorize over a
million documents in an intellectual property case. The attorneys need to isolate
documents that are relevant to a patent that the plaintiffs argue has been infringed. The
attorneys have 50,000 labeled examples of documents, and when the model is
evaluated on training data, it performs quite well. However, when evaluated on test
data, it performs quite poorly. What would you try to improve the performance?
A. Perform feature engineering
B. Perform validation testing
C. Add more data
D. Regularization
D
Assessment 19 - You have built a deep learning neural network to perform multi-class
classification. You find that the model is overfitting. Which of the following would not be
used to reduce
overfitting?
A. Dropout
B. L2 Regularization
C. L1 Regularization
D. Logistic regression
D. Logistic regression
Assessment 20 - Your company would like to start experimenting with machine learning,
but no one in the company is experienced with ML. Analysts in the marketing
department have identified some data in their relational database that they think may be
useful for training a model. What would you recommend that they try first to build proof-
of-concept models?
A. AutoML Tables
B. Kubeflow
C. Cloud Firestore
D. Spark MLlib
A. AutoML Tables
Assessment 21 - You have several large deep learning networks that you have built
using TensorFlow. The models use only standard TensorFlow components. You have
been running the models on
an n1-highcpu-64 VM, but the models are taking longer to train than you would like.
What would you try first to accelerate the model training?
A. GPUs
B. TPUs
C. Shielded VMs
D. Preemptible VMs
B. TPUs
4.6 A startup is creating a business service for the hotel industry. The service will allow
hotels to sell unoccupied rooms on short notice using the startup's platform. The startup
,wants to make it as easy as possible for hotels to share data with the platform, so it
uses a message queue to collect data about rooms that are available for rent. Hotels
send a message for each room that is available and the days that it is available. Room
identifier and dates are the keys that uniquely identify a listing. If a listing exists and a
message is received with the same room identifier and dates, the message is
discarded. What are the minimal guarantees that you would want from the message
queue?
A. Route randomly to any instance that is building a machine learning model
B. Route based on the sensor identifier so identifiers in close proximity are used in the
same model
C. Route based on machine type so only data from one machine type is used for each
model
D. Route based on timestamp so metrics close in time to each other are used in the
same model
A. Route randomly to any instance that is building a machine learning model
9.7 - A developer has built a machine learning model to predict the category of new
stories. The possible values are politics, economics, business, health, science, and
local news. The developer has tried several algorithms, but the model accuracy is quite
high when
evaluating the model using the training data but quite low when evaluating using test
data. What would you recommend to correct this problem?
A. Use confusion matrices for evaluation
B. Use L1 or L2 regularization when evaluating
C. Use L1 or L2 regularization when training
D. Tune the hyperparameters more
C. Use L1 or L2 regularization when training
9.8 - Your e-commerce company deployed a product recommendation system six
months ago. The system uses a machine learning model trained using historical sales
data from the previous year. The model performed well initially. When customers were
shown product recommendations, the average sale value increased by 14 percent. In
the past month, the model has generated an average increase of only 2 percent. The
model has not changed since
it was deployed six months ago. What could be the cause of the decrease in
effectiveness, and what would you recommend to correct it?
A. The model is overfitting—use regularization.
B. The data used to train the model is no longer representative of current sales data,
and the model should be retrained with more recent data.
C. The model should be monitored to collect performance metrics to identity the root
cause of the decreasing effectiveness of the model.
D. The model is underfitting—train with more data.
B. The data used to train the model is no longer representative of current sales data,
and the model should be retrained with more recent data.
11.4 - You want to study deep learning and decide to start with the basics. You build a
binary classifier using an artificial neuron. What algorithm would you use to train it?
A. Perceptron
B. SVM
,C. Decision tree
D. Linear regression
A. Perceptron
11.3 - A startup is collecting IoT data from sensors placed on manufacturing equipment.
The sensors send data every five seconds. The data includes a machine identifier, a
timestamp, and several numeric values. The startup is developing a model to identify
unusual readings. What type of unsupervised learning technique would they use?
A. Clustering
B. K-means
C. Anomaly detection
D. Reinforcement learning
C. Anomaly detection
11.5 - A group of machine learning engineers has been assigned the task of building a
machine learning model to predict the price of gold on the open market. Many features
could be used, and the engineers believe that the optimal model will be complex. They
want to understand the minimum predictive value of a model that they can build from
the data that they have. What would they build?
A. Multiclass classifier
B. K clusters
C. Baseline model
D. Binary classifier
C. Baseline model
11.6 - You are preparing a dataset to build a classifier. The data includes several
continuous values, each in the range 0.00 to 100.00. You'd like to have a discrete
feature derive each continuous
value. What type of feature engineering would you use?
A. Bucketing
B. Dimension reduction
C. Principal component analysis
D. Gradient descent
A. Bucketing
11.7 - You have been tasked with developing a classification model. You have reviewed
the data that you will use for training and testing and realize that there are a number of
outliers that you think might lead to overfitting. What technique would you use to reduce
the impact of those outliers on the model?
A. Gradient descent
B. Large number of epochs
C. L2 regularization
D. Backpropagation
C. L2 regularization
11.8 - You have built a deep learning neural network that has 8 layers, and each layer
has 100 fully connected nodes. The model fits the training data quite well with an F1
score of 98 out of 100. The model performs poorly when the test data is used, resulting
in an F1 score
of 62 out of 100. What technique would you use to try to improve performance of this
model?
, A. User more epochs
B. Dropout
C. Add more layers
D. ReLU
B. Dropout
11.9 - Your team is building a classifier to identify counterfeit products on an e-
commerce site. Most of the products on the site are legitimate, and only about 3 percent
of the products are counterfeit. You are concerned that, as is, the dataset will lead to a
model that always predicts that products are legitimate. Which of the following
techniques could you use to prevent this?
A. Undersampling
B. Dropout
C. L1 regularization
D. AUC
A. Undersampling
11.10 - You are reviewing a dataset and find that the data is relatively high quality.
There are no missing values and only a few outliers. You build a model based on the
dataset that has high accuracy, precision, and recall when applied to the test data.
When you use the model
in production, however, it renders poor results. What might have caused this condition?
A. Applying L1 regularization
B. Applying dropout
C. Reporting bias
D. Automation bias
C. Reporting bias
12.11 - Your e-commerce startup has been growing rapidly since its launch six months
ago. You are starting to notice that the rate of revenue growth is slowing down. Your
board of directors
is asking you to develop a strategy to increase revenue. You decide to personalize each
customer's experience. One of the ways in which you plan to implement your strategy is
by showing customers products that they are likely to interact with next. What
recommendation
type would you use?
A. Others you may like
B. Frequently bought together
C. Recommended for you
D. Recently viewed
C. Recommended for you
You are developing a machine learning model to predict the likelihood of a device
failure. The device generates a stream of metrics every thirty seconds. The metrics
include 3 categorical values, 5 integer values, and 1 floating point value. The floating
point value ranges from 0 to 100. For the purposes of the model, the floating point value
is more precise than needed. Mapping that value to a feature with possible values
"high", "medium", and "low" is sufficient. What feature engineering technique would you
use to transform the floating point value to high medium, or low?
Regularization
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