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Google Professional-Machine-Learning-Engineer Sample Questions – Free Practice Test & Real Exam Prep
Question #1
You are developing a mode! to detect fraudulent credit card transactions. You need to prioritizedetection because missing even one fraudulent transaction could severely impact the credit cardholder. You used AutoML to tram a model on users' profile information and credit card transactiondata. After training the initial model, you notice that the model is failing to detect many fraudulenttransactions. How should you adjust the training parameters in AutoML to improve modelperformance?Choose 2 answers
A. Increase the score threshold.
B. Decrease the score threshold.
C. Add more positive examples to the training set.
D. Add more negative examples to the training set.
E. Reduce the maximum number of node hours for training.
Answer: B, C
Explanation:
The best options for adjusting the training parameters in AutoML to improve model performance are
to decrease the score threshold and add more positive examples to the training set. These options
can help increase the detection rate of fraudulent transactions, which is the priority for this use case.
The score threshold is a parameter that determines the minimum probability score that a prediction
must have to be classified as positive. Decreasing the score threshold can increase the recall of the
model, which is the proportion of actual positive cases that are correctly identified. Increasing the
recall can help reduce the number of false negatives, which are fraudulent transactions that are
missed by the model. However, decreasing the score threshold can also decrease the precision of the
model, which is the proportion of positive predictions that are actually correct. Decreasing the
precision can increase the number of false positives, which are legitimate transactions that are
flagged as fraudulent by the model. Therefore, there is a trade-off between recall and precision, and
the optimal score threshold depends on the business objective and the cost of errors1. Adding more
positive examples to the training set can help balance the data distribution and improve the model
performance. Positive examples are the instances that belong to the target class, which in this case
are fraudulent transactions. Negative examples are the instances that belong to the other class,
which in this case are legitimate transactions. Fraudulent transactions are usually rare and
imbalanced compared to legitimate transactions, which can cause the model to be biased towards
the majority class and fail to learn the characteristics of the minority class. Adding more positive
examples can help the model learn more features and patterns of the fraudulent transactions, and
increase the detection rate2.
The other options are not as good as options B and C, for the following reasons:
Option A: Increasing the score threshold would decrease the detection rate of fraudulent
transactions, which is the opposite of the desired outcome. Increasing the score threshold would
decrease the recall of the model, which is the proportion of actual positive cases that are correctly
identified. Decreasing the recall would increase the number of false negatives, which are fraudulent
transactions that are missed by the model. Increasing the score threshold would increase the
precision of the model, which is the proportion of positive predictions that are actually correct.
Increasing the precision would decrease the number of false positives, which are legitimate
transactions that are flagged as fraudulent by the model. However, in this use case, the cost of false
negatives is much higher than the cost of false positives, so increasing the score threshold is not a
good option1.
Option D: Adding more negative examples to the training set would not improve the model
performance, and could worsen the data imbalance. Negative examples are the instances that
belong to the other class, which in this case are legitimate transactions. Legitimate transactions are
usually abundant and dominant compared to fraudulent transactions, which can cause the model to
be biased towards the majority class and fail to learn the characteristics of the minority class. Adding
more negative examples would exacerbate this problem, and decrease the detection rate of the
fraudulent transactions2.
Option E: Reducing the maximum number of node hours for training would not improve the model
performance, and could limit the model optimization. Node hours are the units of computation that
are used to train an AutoML model. The maximum number of node hours is a parameter that
determines the upper limit of node hours that can be used for training. Reducing the maximum
number of node hours would reduce the training time and cost, but also the model quality and
accuracy. Reducing the maximum number of node hours would limit the number of iterations, trials,
and evaluations that the model can perform, and prevent the model from finding the optimal
hyperparameters and architecture3.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI,
Week 4: Evaluation
Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Developing highquality
ML models, 2.2 Handling imbalanced data
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Lowcode
ML Solutions, Section 4.3: AutoML
Understanding the score threshold slider
Handling imbalanced data sets in machine learning
AutoML Vision pricing
Question #2
You are developing an ML model using a dataset with categorical input variables. You have randomly
split half of the data into training and test sets. After applying one-hot encoding on the categorical
variables in the training set, you discover that one categorical variable is missing from the test set.
What should you do?
A. Randomly redistribute the data, with 70% for the training set and 30% for the test set
B. Use sparse representation in the test set
C. Apply one-hot encoding on the categorical variables in the test data.
D. Collect more data representing all categories
Answer: C
Explanation:
The best option for dealing with the missing categorical variable in the test set is to apply one-hot
encoding on the categorical variables in the test data. This option has the following advantages:
It ensures the consistency and compatibility of the data format for the ML model, as the one-hot
encoding transforms the categorical variables into binary vectors that can be easily processed by the
model. By applying one-hot encoding on the categorical variables in the test data, you can match the
number and order of the features in the test data with the training data, and avoid any errors or
discrepancies in the model prediction.
It preserves the information and relevance of the data for the ML model, as the one-hot encoding
creates a separate feature for each possible value of the categorical variable, and assigns a value of 1
to the feature corresponding to the actual value of the variable, and 0 to the rest. By applying onehot
encoding on the categorical variables in the test data, you can retain the original meaning and
importance of the categorical variable, and avoid any loss or distortion of the data.
The other options are less optimal for the following reasons:
Option A: Randomly redistributing the data, with 70% for the training set and 30% for the test set,
introduces additional complexity and risk. This option requires reshuffling and splitting the data
again, which can be tedious and time-consuming. Moreover, this option may not guarantee that the
missing categorical variable will be present in the test set, as it depends on the randomness of the
data distribution. Furthermore, this option may affect the quality and validity of the ML model, as it
may change the data characteristics and patterns that the model has learned from the original
training set.
Option B: Using sparse representation in the test set introduces additional overhead and inefficiency.
This option requires converting the categorical variables in the test set into sparse vectors, which are
vectors that have mostly zero values and only store the indices and values of the non-zero elements.
However, using sparse representation in the test set may not be compatible with the ML model, as
the model expects the input data to have the same format and dimensionality as the training data,
which uses one-hot encoding. Moreover, using sparse representation in the test set may not be
efficient or scalable, as it requires additional computation and memory to store and process the
sparse vectors.
Option D: Collecting more data representing all categories introduces additional cost and delay. This
option requires obtaining and labeling more data that contains the missing categorical variable,
which can be expensive and time-consuming. Moreover, this option may not be feasible or
necessary, as the missing categorical variable may not be available or relevant for the test data,
depending on the data source or the business problem.
Question #3
You have built a model that is trained on data stored in Parquet files. You access the data through a
Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a
CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate
your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?
A. Remove the data transformation step from your pipeline.
B. Containerize the PySpark transformation step, and add it to your pipeline.
C. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then
saves the transformed data in Cloud Storage.
D. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a
ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
Answer: C
Explanation:
The best option for parametrizing the model training in Kubeflow Pipelines is to add a ContainerOp
to the pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed
data in Cloud Storage. This option has the following advantages:
It allows the data transformation to be performed as part of the Kubeflow Pipeline, which can ensure
the consistency and reproducibility of the data processing and the model training. By adding a
ContainerOp to the pipeline, you can define the parameters and the logic of the data transformation
step, and integrate it with the other steps of the pipeline, such as the model training and evaluation.
It leverages the scalability and performance of Dataproc, which is a fully managed service that runs
Apache Spark and Apache Hadoop clusters on Google Cloud. By spinning a Dataproc cluster, you can
run the PySpark transformation on the Parquet files stored in the Hive table, and take advantage of
the parallelism and speed of Spark. Dataproc also supports various features and integrations, such as
autoscaling, preemptible VMs, and connectors to other Google Cloud services, that can optimize the
data processing and reduce the cost.
It simplifies the data storage and access, as the transformed data is saved in Cloud Storage, which is a
scalable, durable, and secure object storage service. By saving the transformed data in Cloud
Storage, you can avoid the overhead and complexity of managing the data in the Hive table or the
Parquet files. Moreover, you can easily access the transformed data from Cloud Storage, using
various tools and frameworks, such as TensorFlow, BigQuery, or Vertex AI.
The other options are less optimal for the following reasons:
Option A: Removing the data transformation step from the pipeline eliminates the parametrization
of the model training, as the data processing and the model training are decoupled and independent.
This option requires running the PySpark transformation separately from the Kubeflow Pipeline,
which can introduce inconsistency and unreproducibility in the data processing and the model
training. Moreover, this option requires managing the data in the Hive table or the Parquet files,
which can be cumbersome and inefficient.
Option B: Containerizing the PySpark transformation step, and adding it to the pipeline introduces
additional complexity and overhead. This option requires creating and maintaining a Docker image
that can run the PySpark transformation, which can be challenging and time-consuming. Moreover,
this option requires running the PySpark transformation on a single container, which can be slow and
inefficient, as it does not leverage the parallelism and performance of Spark.
Option D: Deploying Apache Spark at a separate node pool in a Google Kubernetes Engine cluster,
and adding a ContainerOp to the pipeline that invokes a corresponding transformation job for this
Spark instance introduces additional complexity and cost. This option requires creating and managing
a separate node pool in a Google Kubernetes Engine cluster, which is a fully managed service that
runs Kubernetes clusters on Google Cloud. Moreover, this option requires deploying and running
Apache Spark on the node pool, which can be tedious and costly, as it requires configuring and
maintaining the Spark cluster, and paying for the node pool usage.
Question #4
You work for a magazine publisher and have been tasked with predicting whether customers will
cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals
renew their subscription every year, and only 10% of individuals cancel their subscription. After
training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy
and predicts those who renew their subscription with 82% accuracy. How should you interpret these
results?
A. This is not a good result because the model should have a higher accuracy for those who renew
their subscription than for those who cancel their subscription.
B. This is not a good result because the model is performing worse than predicting that people will
always renew their subscription.
C. This is a good result because predicting those who cancel their subscription is more difficult, since
there is less data for this group.
D. This is a good result because the accuracy across both groups is greater than 80%.
Answer: B
Explanation:
This is not a good result because the model is performing worse than predicting that people will
always renew their subscription. This option has the following reasons:
It indicates that the model is not learning from the data, but rather memorizing the majority class.
Since 90% of the individuals renew their subscription every year, the model can achieve a 90%
accuracy by simply predicting that everyone will renew their subscription, without considering the
features or the patterns in the data. However, the models accuracy for predicting those who renew
their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that
the model is overfitting to the minority class (those who cancel their subscription), and underfitting
to the majority class (those who renew their subscription).
It implies that the model is not useful for the business problem, as it cannot identify the customers
who are at risk of churning. The goal of predicting whether customers will cancel their annual
subscription is to prevent customer churn and increase customer retention. However, the models
accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic,
as it means that the model can almost perfectly identify the customers who will churn, without any
false positives or false negatives. This may indicate that the model is cheating or exploiting some
leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the
models accuracy for predicting those who renew their subscription is 82%, which is too low and
unreliable, as it means that the model can miss many customers who will churn, and falsely label
them as renewing customers. This can lead to losing customers and revenue, and failing to take
proactive actions to retain them.
Reference:
How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery
Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery
Question #5
You work for a retailer that sells clothes to customers around the world. You have been tasked with
ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive
customer data that might be used in the models. You have identified four fields containing sensitive
data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER,
LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made
available to the data science team for training purposes?
A. Tokenize all of the fields using hashed dummy values to replace the real values.
B. Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.
C. Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single
precision. The other two fields are already as coarse as possible.
D. Remove all sensitive data fields, and ask the data science team to build their models using nonsensitive
data.
Answer: C
Explanation:
The best option for protecting sensitive customer data that might be used in the ML models is to
coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGITUDE into single
precision. This option has the following advantages:
It preserves the utility and relevance of the data for the ML models, as the coarsened data still
captures the essential information and patterns that the models need to learn. For example, putting
AGE into quantiles can group the customers into different age ranges, which can be useful for
predicting their preferences or behavior. Rounding LATITUDE_LONGITUDE into single precision can
reduce the precision of the location data, but still retain the general geographic region of the
customers, which can be useful for personalizing the recommendations or offers.
It reduces the risk of exposing the personal or private information of the customers, as the coarsened
data makes it harder to identify or re-identify the individual customers from the data. For example,
putting AGE into quantiles can hide the exact age of the customers, which can be considered
sensitive or confidential. Rounding LATITUDE_LONGITUDE into single precision can obscure the exact
location of the customers, which can be considered sensitive or confidential.
The other options are less optimal for the following reasons:
Option A: Tokenizing all of the fields using hashed dummy values to replace the real values
eliminates the utility and relevance of the data for the ML models, as the tokenized data loses all the
information and patterns that the models need to learn. For example, tokenizing AGE using hashed
dummy values can make the data meaningless and irrelevant, as the models cannot learn anything
from the random tokens. Tokenizing LATITUDE_LONGITUDE using hashed dummy values can make
the data meaningless and irrelevant, as the models cannot learn anything from the random tokens.
Option B: Using principal component analysis (PCA) to reduce the four sensitive fields to one PCA
vector reduces the utility and relevance of the data for the ML models, as the PCA vector may not
capture all the information and patterns that the models need to learn. For example, using PCA to
reduce AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE to one PCA vector
can lose some information or introduce noise in the data, as the PCA vector is a linear combination
of the original features, which may not reflect their true relationship or importance. Moreover, using
PCA to reduce the four sensitive fields to one PCA vector may not reduce the risk of exposing the
personal or private information of the customers, as the PCA vector may still be reversible or linkable
to the original data, depending on the amount of variance explained by the PCA vector and the
availability of the PCA transformation matrix.
Option D: Removing all sensitive data fields, and asking the data science team to build their models
using non-sensitive data reduces the utility and relevance of the data for the ML models, as the nonsensitive
data may not contain enough information and patterns that the models need to learn. For
example, removing AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE from the
data can make the data insufficient and unrepresentative, as the models may not be able to learn the
factors that influence the customers preferences or behavior. Moreover, removing all sensitive data
fields from the data may not be necessary or feasible, as the data protection legislation may allow
the use of sensitive data for the ML models, as long as the data is processed in a secure and ethical
manner, and the customers consent and rights are respected.
Reference:
Protecting Sensitive Data and AI Models with Confidential Computing | NVIDIA Technical Blog
Training machine learning models from sensitive data | Fast Data Science
Securing ML applications. Model security and protection - Medium
Security of AI/ML systems, ML model security | Cossack Labs
Vulnerabilities, security and privacy for machine learning models
Question #6
You work for a company that manages a ticketing platform for a large chain of cinemas. Customers
use a mobile app to search for movies theyre interested in and purchase tickets in the app. Ticket
purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline
configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have
developed a logistic regression model with BigQuery ML that predicts whether offering a promo code
for free popcorn increases the chance of a ticket purchase, and this prediction should be added to
the ticket purchase process. You want to identify the simplest way to deploy this model to production
while adding minimal latency. What should you do?
A. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the
Dataflow pipeline.
C. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint
from your streaming pipeline.
D. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo
code and the incoming request arrive together in Pub/Sub.
Answer: B
Explanation:
The simplest way to deploy a logistic regression model with BigQuery ML to production while adding
minimal latency is to export the model in TensorFlow format, and add a
tfx_bsl.public.beam.RunInference step to the Dataflow pipeline. This option has the following
advantages:
It allows the model prediction to be performed in real time, as part of the Dataflow streaming
pipeline that processes the ticket purchase requests. This ensures that the promo code offer is based
on the most recent data and customer behavior, and that the offer is delivered to the customer
without delay.
It leverages the compatibility and performance of TensorFlow and Dataflow, which are both part of
the Google Cloud ecosystem. TensorFlow is a popular and powerful framework for building and
deploying machine learning models, and Dataflow is a fully managed service that runs Apache Beam
pipelines for data processing and transformation. By using the tfx_bsl.public.beam.RunInference
step, you can easily integrate your TensorFlow model with your Dataflow pipeline, and take
advantage of the parallelism and scalability of Dataflow.
It simplifies the model deployment and management, as the model is packaged with the Dataflow
pipeline and does not require a separate service or endpoint. The model can be updated by
redeploying the Dataflow pipeline with a new model version.
The other options are less optimal for the following reasons:
Option A: Running batch inference with BigQuery ML every five minutes on each new set of tickets
issued introduces additional latency and complexity. This option requires running a separate
BigQuery job every five minutes, which can incur network overhead and latency. Moreover, this
option requires storing and retrieving the intermediate results of the batch inference, which can
consume storage space and increase the data transfer time.
Option C: Exporting the model in TensorFlow format, deploying it on Vertex AI, and querying the
prediction endpoint from the streaming pipeline introduces additional latency and cost. This option
requires creating and managing a Vertex AI endpoint, which is a managed service that provides
various tools and features for machine learning, such as training, tuning, serving, and monitoring.
However, querying the Vertex AI endpoint from the streaming pipeline requires making an HTTP
request, which can incur network overhead and latency. Moreover, this option requires paying for
the Vertex AI endpoint usage, which can increase the cost of the model deployment.
Option D: Converting the model with TensorFlow Lite (TFLite), and adding it to the mobile app so that
the promo code and the incoming request arrive together in Pub/Sub introduces additional
challenges and risks. This option requires converting the model to a TFLite format, which is a
lightweight and optimized format for running TensorFlow models on mobile and embedded devices.
However, converting the model to TFLite may not preserve the accuracy or functionality of the
original model, as some operations or features may not be supported by TFLite. Moreover, this
option requires updating the mobile app with the TFLite model, which can be tedious and timeconsuming,
and may depend on the users willingness to update the app. Additionally, this option
may expose the model to potential security or privacy issues, as the model is running on the users
device and may be accessed or modified by malicious actors.
Reference:
[Exporting models for prediction | BigQuery ML]
[tfx_bsl.public.beam.run_inference | TensorFlow Extended]
[Vertex AI documentation]
[TensorFlow Lite documentation]
Question #7
You deployed an ML model into production a year ago. Every month, you collect all raw requests that
were sent to your model prediction service during the previous month. You send a subset of these
requests to a human labeling service to evaluate your models performance. After a year, you notice
that your model's performance sometimes degrades significantly after a month, while other times it
takes several months to notice any decrease in performance. The labeling service is costly, but you
also need to avoid large performance degradations. You want to determine how often you should
retrain your model to maintain a high level of performance while minimizing cost. What should you
do?
A. Train an anomaly detection model on the training dataset, and run all incoming requests through
this model. If an anomaly is detected, send the most recent serving data to the labeling service.
B. Identify temporal patterns in your models performance over the previous year. Based on these
patterns, create a schedule for sending serving data to the labeling service for the next year.
C. Compare the cost of the labeling service with the lost revenue due to model performance
C. Compare the cost of the labeling service with the lost revenue due to model performance
degradation over the past year. If the lost revenue is greater than the cost of the labeling service,
increase the frequency of model retraining; otherwise, decrease the model retraining frequency.
D. Run training-serving skew detection batch jobs every few days to compare the aggregate statistics
of the features in the training dataset with recent serving data. If skew is detected, send the most
recent serving data to the labeling service.
Answer: D
Explanation:
The best option for determining how often to retrain your model to maintain a high level of
performance while minimizing cost is to run training-serving skew detection batch jobs every few
days. Training-serving skew refers to the discrepancy between the distributions of the features in the
training dataset and the serving data. This can cause the model to perform poorly on the new data,
as it is not representative of the data that the model was trained on. By running training-serving
skew detection batch jobs, you can monitor the changes in the feature distributions over time, and
identify when the skew becomes significant enough to affect the model performance. If skew is
detected, you can send the most recent serving data to the labeling service, and use the labeled data
to retrain your model. This option has the following benefits:
It allows you to retrain your model only when necessary, based on the actual data changes, rather
than on a fixed schedule or a heuristic. This can save you the cost of the labeling service and the
retraining process, and also avoid overfitting or underfitting your model.
It leverages the existing tools and frameworks for training-serving skew detection, such as
TensorFlow Data Validation (TFDV) and Vertex Data Labeling. TFDV is a library that can compute and
visualize descriptive statistics for your datasets, and compare the statistics across different datasets.
Vertex Data Labeling is a service that can label your data with high quality and low latency, using
either human labelers or automated labelers.
It integrates well with the MLOps practices, such as continuous integration and continuous delivery
(CI/CD), which can automate the workflow of running the skew detection jobs, sending the data to
the labeling service, retraining the model, and deploying the new model version.
The other options are less optimal for the following reasons:
Option A: Training an anomaly detection model on the training dataset, and running all incoming
requests through this model, introduces additional complexity and overhead. This option requires
building and maintaining a separate model for anomaly detection, which can be challenging and
time-consuming. Moreover, this option requires running the anomaly detection model on every
request, which can increase the latency and resource consumption of the prediction service.
Additionally, this option may not capture the subtle changes in the feature distributions that can
affect the model performance, as anomalies are usually defined as rare or extreme events.
Option B: Identifying temporal patterns in your models performance over the previous year, and
creating a schedule for sending serving data to the labeling service for the next year, introduces
additional assumptions and risks. This option requires analyzing the historical data and model
performance, and finding the patterns that can explain the variations in the model performance over
time. However, this can be difficult and unreliable, as the patterns may not be consistent or
predictable, and may depend on various factors that are not captured by the data. Moreover, this
option requires creating a schedule based on the past patterns, which may not reflect the future
changes in the data or the environment. This can lead to either sending too much or too little data to
the labeling service, resulting in either wasted cost or degraded performance.
Option C: Comparing the cost of the labeling service with the lost revenue due to model
performance degradation over the past year, and adjusting the frequency of model retraining
accordingly, introduces additional challenges and trade-offs. This option requires estimating the cost
of the labeling service and the lost revenue due to model performance degradation, which can be
difficult and inaccurate, as they may depend on various factors that are not easily quantifiable or
measurable. Moreover, this option requires finding the optimal balance between the cost and the
performance, which can be subjective and variable, as different stakeholders may have different
preferences and expectations. Furthermore, this option may not account for the potential impact of
the model performance degradation on other aspects of the business, such as customer satisfaction,
retention, or loyalty.
Question #8
You work for an online publisher that delivers news articles to over 50 million readers. You have built
an AI model that recommends content for the companys weekly newsletter. A recommendation is
considered successful if the article is opened within two days of the newsletters published date and
the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated
hourly. The model is trained on eight weeks of data, on average its performance degrades below the
acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the
models performance is above the acceptable baseline while minimizing cost. How should you
monitor the model to determine when retraining is necessary?
A. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100%
and a monitoring frequency of two days.
B. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is
created.
C. Schedule a weekly query in BigQuery to compute the success metric.
D. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
Answer: C
Explanation:
The best option for monitoring the model to determine when retraining is necessary is to schedule a
weekly query in BigQuery to compute the success metric. This option has the following advantages:
It allows the model performance to be evaluated regularly, based on the actual outcome of the
recommendations. By computing the success metric, which is the percentage of articles that are
opened within two days and read for at least one minute, you can measure how well the model is
achieving its objective and compare it with the acceptable baseline.
It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly
scalable data warehouse that can run complex queries over petabytes of data in seconds. By using
BigQuery, you can access and analyze all the information needed to compute the success metric,
such as the newsletter publication date, the article opening date, and the user reading time, without
worrying about the infrastructure or the cost.
It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled
and executed automatically using BigQuerys built-in scheduling feature. You can also set up alerts or
notifications to inform you when the success metric falls below the acceptable baseline, and trigger
the model retraining process accordingly.
The other options are less optimal for the following reasons:
Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate
of 100% and a monitoring frequency of two days introduces additional complexity and overhead.
This option requires setting up and managing a Vertex AI Model Monitoring service, which is a
managed service that provides various tools and features for machine learning, such as training,
tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the
input features may not reflect the actual performance of the model, as skew is the discrepancy
between the distributions of the features in the training dataset and the serving data, which may not
affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a
monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all
the input features every two days, which may not be needed for the model monitoring.
Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter
is created introduces additional cost and risk. This option requires creating and running a cron job in
Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are
invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be
optimal, as it may retrain the model more often than necessary, wasting compute resources and cost.
Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce
risk, as it may deploy a new model version that has not been tested or validated, potentially affecting
the quality of the recommendations.
Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric
introduces additional complexity and cost. This option requires creating and running a Dataflow job
in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow
orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data
processing and transformation. However, using Dataflow and Cloud Composer to compute the
success metric may not be necessary, as it may add more steps and overhead to the model
monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric
daily may not be optimal, as it may compute the success metric more often than needed, consuming
more compute resources and cost.
Reference:
[BigQuery documentation]
[Vertex AI Model Monitoring documentation]
[Cloud Tasks documentation]
[Cloud Composer documentation]
[Dataflow documentation]
Question #9
You need to deploy a scikit-learn classification model to production. The model must be able to serverequests 24 and you expect millions of requests per second to the production application from 8am to 7 pm. You need to minimize the cost of deployment What should you do?
A. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1
B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100
C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica countto 1.
D. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica countto 100.
Answer: B
Explanation:
The best option for deploying a scikit-learn classification model to production is to deploy an online
Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to
leverage the power and scalability of Google Cloud to serve requests 24 and handle millions of
requests per second. Vertex AI is a unified platform for building and deploying machine learning
solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction
endpoint, which can provide low-latency predictions for individual instances. An online prediction
endpoint consists of one or more replicas, which are copies of the model that run on virtual
machines. The max replica count is a parameter that determines the maximum number of replicas
that can be created for the endpoint. By setting the max replica count to 100, you can enable the
endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when
the traffic decreases. This can help minimize the cost of deployment, as you only pay for the
resources that you use. Moreover, you can use the autoscaling algorithm option to optimize the
scaling behavior of the endpoint based on the latency and utilization metrics1.
The other options are not as good as option B, for the following reasons:
Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1
would not be able to serve requests 24 and handle millions of requests per second. Setting the
max replica count to 1 would limit the endpoint to only one replica, which can cause performance
issues and service disruptions when the traffic increases. Moreover, setting the max replica count to
1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which
can increase the cost of deployment, as you pay for the resources that you do not use1.
Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the
max replica count to 1 would not be able to serve requests 24 and handle millions of requests per
second, and would increase the cost of deployment. Adding a GPU to each replica would increase the
computational power of the endpoint, but it would also increase the cost of deployment, as GPUs
are more expensive than CPUs. Moreover, setting the max replica count to 1 would limit the
endpoint to only one replica, which can cause performance issues and service disruptions when the
traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic
decreases1. Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not
optimized for GPU acceleration2.
Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the
max replica count to 100 would be able to serve requests 24 and handle millions of requests per
second, but it would increase the cost of deployment. Adding a GPU to each replica would increase
the computational power of the endpoint, but it would also increase the cost of deployment, as
GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the
endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when
the traffic decreases, which can help minimize the cost of deployment. However, scikit-learn models
do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration2. Therefore, using
GPUs for scikit-learn models would be unnecessary and wasteful.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML
Systems, Week 2: Serving ML Predictions
Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in
production, 3.1 Deploying ML models to production
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.2: Serving ML Predictions
Online prediction
Scaling online prediction
scikit-learn FAQ
Question #10
You work with a team of researchers to develop state-of-the-art algorithms for financial analysis.Your team develops and debugs complex models in TensorFlow. You want to maintain the ease ofdebugging while also reducing the model training time. How should you set up your trainingenvironment?
A. Configure a v3-8 TPU VM SSH into the VM to tram and debug the model.
B. Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.
C. Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and useParameter Server Strategy to train the model.
D. Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and useMultiWorkerMirroredStrategy to train the model.
Answer: A
Explanation:
A TPU VM is a virtual machine that has direct access to a Cloud TPU device. TPU VMs provide a
simpler and more flexible way to use Cloud TPUs, as they eliminate the need for a separate host VM
and network setup. TPU VMs also support interactive debugging tools such as TensorFlow Debugger
(tfdbg) and Python Debugger (pdb), which can help researchers develop and troubleshoot complex
models. A v3-8 TPU VM has 8 TPU cores, which can provide high performance and scalability for
training large models. SSHing into the TPU VM allows the user to run and debug the TensorFlow code
directly on the TPU device, without any network overhead or data transfer issues. Reference:
1: TPU VMs Overview
2: TPU VMs Quickstart
3: Debugging TensorFlow Models on Cloud TPUs
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