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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.

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