Professional Machine Learning Engineer Certification Exam Questions with AI Explanations

QUESTION: 57

Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

The model predicts videos as popular if the user who uploads them has over 10,000 likes.
The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Answer(s): C

Explanation:

Option C is correct because it aligns evaluation with business objective: predicting the proportion of videos that become popular as measured by watch time within 30 days directly reflects user engagement and long-term value, which is a typical metric for popularity prediction in video platforms.
A) Incorrect — using uploader likes is a proxy signal and circular bias; it does not measure actual viewer engagement or video performance after upload.
B) Incorrect — 97.5% of clickbait videos by number of clicks focuses on short-term clicks, not sustained watch time, and can incentivize low-quality content.
D) Incorrect — a Pearson correlation of 0 indicates no linear relationship, not a meaningful measure of predictive success.

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QUESTION: 58

You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

Use feature construction to combine the strongest features.
Use the representation transformation (normalization) technique.
Improve the data cleaning step by removing features with missing values.
Change the partitioning step to reduce the dimension of the test set and have a larger training set.

Answer(s): B

Explanation:

Option B is correct because normalization (representation transformation) scales features to a similar range, which stabilizes and accelerates gradient-based optimization in neural networks, helping weights converge more effectively.
A) Incorrect — feature construction may help if features are weak, but it does not directly address gradient optimization stability or scaling issues.
C) Incorrect — removing features with missing values addresses data quality, not gradient optimization or feature scale; imputation or robust modeling would be more relevant.
D) Incorrect — partitioning to alter train/test sizes does not fix feature scaling or optimization dynamics and can harm evaluation validity.

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QUESTION: 59

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
Use AI Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

Answer(s): A

Explanation:

Option A is correct because Kubeflow Pipelines provides a robust framework to orchestrate ML experiments and exposes an API to access run-level metrics and lineage, enabling rapid experimentation with minimal manual effort.
B is incorrect because while AI Platform Training can run experiments, exporting metrics to BigQuery and querying via the BigQuery API adds unnecessary complexity for time-series experiment metrics tracking and does not leverage a purpose-built ML experiment-tracking workflow.
C is incorrect because Cloud Monitoring is designed for infrastructure and service metrics, not experiment-level ML metrics, and using its API is not the optimal path for tracking model accuracy across experiments.
D is incorrect because AI Platform Notebooks with Google Sheets introduces manual data collection and lacks scalable, programmatic experiment tracking and versioning that a dedicated ML pipeline provides.

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QUESTION: 60

You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

Write your data in TFRecords.
Z-normalize all the numeric features.
Oversample the fraudulent transaction 10 times.
Use one-hot encoding on all categorical features.

Answer(s): C

Explanation:

Option C is correct because oversampling the minority class (fraud) addresses severe class imbalance, improving the model’s ability to learn decision boundaries for fraud cases and typically boosting metrics like recall/precision in imbalanced fraud detection with tree-based models.
A) Incorrect — Writing data in TFRecords is an I/O/performance optimization, not a predictive feature transformation that directly improves classifier performance.
B) Incorrect — Z-normalization is not always beneficial for tree ensembles like random forests, which are tree-based and less sensitive to feature scaling.
D) Incorrect — One-hot encoding can increase sparsity and dimensionality without addressing class imbalance, potentially harming model efficiency and performance.

Reference:

https://towardsdatascience.com/how-to-build-a-machine-learning-model-to-identify-credit-card- fraud-in-5-stepsa-hands-on-modeling-5140b3bd19f1

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QUESTION: 61

You are using transfer learning to train an image classifier based on a pre-trained EfficientNet model. Your training dataset has 20,000 images. You plan to retrain the model once per day. You need to minimize the cost of infrastructure. What platform components and configuration environment should you use?

A Deep Learning VM with 4 V100 GPUs and local storage.
A Deep Learning VM with 4 V100 GPUs and Cloud Storage.
A Google Kubernetes Engine cluster with a V100 GPU Node Pool and an NFS Server
An AI Platform Training job using a custom scale tier with 4 V100 GPUs and Cloud Storage

Answer(s): D

Explanation:

Option D is correct because AI Platform Training with a custom scale tier allows on-demand, scalable training jobs (4 V100 GPUs) using Cloud Storage for data and artifacts, minimizing idle infrastructure and cost while supporting daily retraining.
A) Incorrect — 4 V100 GPUs with local storage incurs higher cost and doesn’t leverage scalable, managed training; local storage isn’t optimal for distributed training or long-term cost efficiency.
B) Incorrect — 4 V100 GPUs with Cloud Storage still uses a fixed VM setup; no managed scaling or orchestration, increasing management overhead and cost for daily retraining.
C) Incorrect — GKE with an NFS server adds complexity and persistent storage management; not optimized for cost-efficient, single-job daily retraining.

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QUESTION: 62

While conducting an exploratory analysis of a dataset, you discover that categorical feature A has substantial predictive power, but it is sometimes missing. What should you do?

Drop feature A if more than 15% of values are missing. Otherwise, use feature A as-is.
Compute the mode of feature A and then use it to replace the missing values in feature A.
Replace the missing values with the values of the feature with the highest Pearson correlation with feature
Add an additional class to categorical feature A for missing values. Create a new binary feature that indicates whether feature A is missing.

Answer(s): D

Explanation:

Option D is correct because treating missingness as a signal by adding an indicator for missing values and keeping A's original category leverages the informative nature of missingness and preserves information for models that can handle categorical features.
A) Incorrect — arbitrarily dropping based on a threshold ignores the potential predictive value of A and the missingness signal.
B) Incorrect — imputing with mode can bias category distributions and ignore missingness as information.
C) Incorrect — imputing from the single most correlated feature risks leakage and ignores the explicit missingness pattern.

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QUESTION: 63

You work for a large retailer and have been asked to segment your customers by their purchasing habits. The purchase history of all customers has been uploaded to BigQuery. You suspect that there may be several distinct customer segments, however you are unsure of how many, and you don't yet understand the commonalities in their behavior. You want to find the most efficient solution. What should you do?

Create a k-means clustering model using BigQuery ML. Allow BigQuery to automatically optimize the number of clusters.
Create a new dataset in Dataprep that references your BigQuery table. Use Dataprep to identify similarities within each column.
Use the Data Labeling Service to label each customer record in BigQuery. Train a model on your labeled data using AutoML Tables. Review the evaluation metrics to understand whether there is an underlying pattern in the data.
Get a list of the customer segments from your company's Marketing team. Use the Data Labeling Service to label each customer record in BigQuery according to the list. Analyze the distribution of labels in your dataset using Data Studio.

Answer(s): A

Explanation:

Option A is correct because k-means clustering with BigQuery ML can perform unsupervised segmentation directly on the existing BigQuery data, and BigQuery ML can automatically determine a reasonable number of clusters (e.g., via elbow method or in-model initialization), making it efficient for discovering customer segments without labeled data.
B is incorrect because Dataprep is for data cleaning/prep, not for discovering latent segments or clustering similarities across columns; it doesn’t perform unsupervised segmentation.
C is incorrect because Data Labeling Service and AutoML Tables are for supervised learning with labeled data; without labeled segments, this isn’t appropriate for discovering inherent purchase-based segments.
D is incorrect because labeling to a fixed list from Marketing introduces predefined categories; it does not discover natural segments and adds labeling overhead.

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QUESTION: 64

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

Use a built-in model available on AI Platform Training.
Build your custom container to run jobs on AI Platform Training.
Build your custom containers to run distributed training jobs on AI Platform Training.
Reconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.

Answer(s): C

Explanation:

Option C is correct because AI Platform Training supports custom containers for distributed training, enabling large-scale training with the framework’s scheduler, workers, and servers architecture when native support is lacking. The scenario requires distributed training across multiple machines, which is accommodated by custom containers on AI Platform Training. A) is incorrect because built-in models don’t address the need for your custom, large-scale, framework-based training. B) insufficient because single-container jobs don’t cover distributed training across multiple workers/servers. D) unnecessary since you can use distributed custom containers without changing to a different framework.

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