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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

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