PMLE Solving business challenges with ML Practice Test 2 — 15 Questions

Question 1

A company has a large dataset of 1 million unlabeled images for object detection. They want to use AutoML Vision but need to minimize labeling effort. Which strategy should they use?

Accepted Answer

Use Vertex AI Active Learning to choose a subset for labeling. Vertex AI Active Learning is the correct strategy because it intelligently selects the most informative unlabeled images for human labeling, maximizing model accuracy while minimizing labeling effort. This approach uses the model's uncertainty to prioritize data points that will most improve performance, making it ideal for large datasets where manual labeling of all images is impractical.

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Apply data augmentation techniques to increase dataset size

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Manually label all 1 million images

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Train a custom object detection model on unlabeled data with unsupervised learning

Question 2

A data scientist wants to perform feature engineering on a large dataset stored in BigQuery before training a model. Which feature engineering tool is most appropriate?

Accepted Answer

Use BigQuery ML TRANSFORM clause. Option A is correct because BigQuery ML TRANSFORM clause allows creating transformed features directly in SQL. Option B is wrong because Cloud Dataflow is for pipelines, not direct interactive feature engineering. Option C is wrong because Vertex AI Feature Store is for storing already created features. Option D is wrong because Cloud Dataproc is for Hadoop/Spark, not integrated with BigQuery as directly.

Answer

Use Vertex AI Feature Store to store engineered features

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Export data to Cloud Dataproc for feature engineering

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Create a Dataflow pipeline to compute features

Question 3

A model deployed on Vertex AI Prediction is returning high latency for real-time requests. The model is a small TensorFlow model. Which troubleshooting step should the team take first?

Accepted Answer

Check if the machine type is too small and enable autoscaling. Option B is correct because high latency for real-time predictions from a small TensorFlow model often indicates that the serving infrastructure is under-provisioned. Checking the machine type and enabling autoscaling directly addresses whether the instance is too small to handle the request volume, which is the most common first step in diagnosing latency issues on Vertex AI Prediction.

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Retrain the model with a larger batch size

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Use a custom container with optimized runtime

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Enable Cloud Armor to reduce traffic

Question 4

A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?

Accepted Answer

Use BigQuery to join logs and account data, train on Vertex AI, deploy to an endpoint. Option D is correct because it leverages BigQuery's ability to join structured account data with semi-structured logs (via federated queries or external tables), then uses Vertex AI for end-to-end ML training and deployment. This architecture minimizes data movement, keeps the pipeline serverless, and directly addresses the requirement for an automated pipeline with both data sources.

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Load both data sources into AutoML Tables and train directly

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Export logs from Cloud Storage to Cloud Dataproc for preprocessing, then train

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Use Cloud Functions to preprocess data, then train on AI Platform

Question 5

A company is evaluating Google Cloud ML solutions. Which TWO services are appropriate for building custom machine learning models (not using pre-built APIs)? (Choose TWO.)

Accepted Answer

Vertex AI Workbench. Vertex AI Workbench is correct because it provides a Jupyter-based development environment where data scientists can write custom code, train models from scratch, and manage the entire ML workflow without relying on pre-built APIs. It supports custom containers, frameworks like TensorFlow and PyTorch, and integrates with Vertex AI Training for distributed training.

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Cloud Translation API

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Cloud AutoML

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Cloud Vision API

Question 6

A team is deploying a model on Vertex AI Prediction. Which THREE configuration settings have a direct impact on both latency and cost? (Choose THREE.)

Accepted Answer

Minimum and maximum number of nodes (autoscaling). Option B is correct because the minimum and maximum number of nodes in autoscaling directly control how many compute instances are provisioned to handle prediction requests. A higher minimum node count increases baseline cost and reduces cold-start latency, while a lower maximum can cause queuing and higher latency under load, directly impacting both metrics.

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Size of training dataset

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Model architecture (e.g., number of layers)

Question 7

A company wants to use pre-built Google Cloud APIs for text analysis. Which TWO APIs can they use? (Choose TWO.)

Accepted Answer

Cloud Natural Language API. The Cloud Natural Language API provides pre-built machine learning models for text analysis tasks such as entity recognition, sentiment analysis, and syntax analysis. The Cloud Translation API can translate text between languages, which is a form of text analysis. Both are pre-built Google Cloud APIs that directly address the company's need for text analysis without requiring custom model training.

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Cloud Vision API

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Video Intelligence API

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Document AI

Question 8

Refer to the exhibit. This IAM policy is applied at the project level. What is the effect of the condition?

Accepted Answer

The service account can only access AI Platform resources that start with 'projects/ml-'. Option A is correct because the condition block uses the `resource.name.startsWith` condition key to restrict access to AI Platform resources whose names begin with `projects/ml-`. This means the service account can only interact with AI Platform resources (such as models, jobs, or endpoints) that have a resource name starting with that prefix, effectively scoping the permission to a specific set of projects or resources.

Answer

The service account can only be used in projects whose ID starts with 'ml-'

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The role is granted only if the project's name contains 'ml-'

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The condition is ignored because conditions are not supported for service accounts

Question 9

Refer to the exhibit. A user attempts to upload a model to Vertex AI Model Registry using the gcloud CLI. The command fails with the error shown. What is the most likely cause?

Accepted Answer

The --artifact-uri points to a directory instead of a model file. The error indicates that the `--artifact-uri` flag points to a directory (e.g., `gs://bucket/model/`) rather than a specific model file (e.g., `gs://bucket/model/saved_model.pb`). Vertex AI Model Registry requires a direct path to the model artifact file, not a container directory, because the service needs to locate and register the exact model binary for deployment.

Answer

The region us-central1 does not support Vertex AI

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The --container-command flag is misspelled

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The --container-ports flag expects a comma-separated list

Question 10

Refer to the exhibit. A data scientist runs this Vertex AI training job code. What will be the outcome?

Accepted Answer

A HyperparameterTuningJob is created and runs trials.. The code uses `HyperparameterTuningJob` with `parallel_trial_count=1` and `max_trial_count=10`. This creates a hyperparameter tuning job that runs up to 10 trials, each trial being a separate training run with different hyperparameter values. The `parallel_trial_count=1` means trials run sequentially, not in parallel, but this is valid and does not cause failure.

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The job runs as a regular custom training with 10 replicas.

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A CustomJob is created with hyperparameters from the spec.

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The job fails because parallel_trial_count cannot be less than max_trial_count.

Question 11

A retail company wants to predict customer churn using their transaction history and customer demographics. They have limited ML expertise and want to use a managed service on Google Cloud. Which service should they use?

Accepted Answer

Vertex AI AutoML (Tables). Vertex AI AutoML (Tables) is the correct choice because it is a managed service specifically designed for tabular data, requiring no ML expertise. It automates model training, hyperparameter tuning, and deployment for classification tasks like churn prediction, directly handling transaction history and demographic features.

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AI Platform Notebooks

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Cloud TPU

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BigQuery ML

Question 12

A data scientist deployed a classification model on Vertex AI Endpoints. After a week, the model's accuracy drops significantly from 92% to 78%. The data scientist suspects training-serving skew. What is the first step to confirm this?

Accepted Answer

Compare feature distributions between training and serving data using Vertex AI Model Monitoring. Option B is correct because Vertex AI Model Monitoring provides a built-in capability to automatically detect training-serving skew by comparing feature distributions between the training data and the live serving data. This is the most direct and efficient first step to confirm whether the accuracy drop is due to a shift in the input data distribution, which is the hallmark of training-serving skew. The data scientist can set up monitoring jobs that compute statistical distance metrics (e.g., Jensen-Shannon divergence) and alert when significant deviations occur.

Answer

Look for data leakage in the training pipeline

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Examine the feature importance of the model

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Check the prediction confidence over time

Question 13

A media company wants to build a real-time recommendation system for articles. They have a large user base (10M+) and frequent updates to user interactions. They need to handle cold-start users and new articles. Which architecture on Vertex AI is most suitable?

Accepted Answer

Implement a two-tower model (user and item towers) with embeddings and nearest neighbor search. The two-tower model (user and item towers) with embeddings and nearest neighbor search is the most suitable because it handles cold-start users and new articles by learning separate embeddings for users and items, enabling efficient retrieval via approximate nearest neighbor (ANN) search. This architecture supports real-time updates and scales to 10M+ users by decoupling user and item representations, allowing incremental training on new interactions without full retraining.

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Deploy a Deep Learning Recommendation Model (DLRM) for prediction

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Use a contextual bandit algorithm for exploration only

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Use matrix factorization with collaborative filtering

Question 14

A financial company is building a fraud detection model. The dataset has 1% fraud cases and 99% legitimate transactions. Which technique should they use to handle the class imbalance?

Accepted Answer

Use class weighting or synthetic oversampling (SMOTE) during training. Class weights or resampling techniques like SMOTE are standard for imbalanced datasets. Option A is correct. Option B (undersampling majority) can lose information. Option C (collect more data) is impractical. Option D (no alterations) will bias the model.

Answer

Randomly undersample the majority class to balance the dataset

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Collect more data until the fraud rate increases

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Train without any modifications; the model will naturally handle it

Question 15

A data scientist trained a custom TensorFlow model using Vertex AI Training and wants to deploy it for online predictions with low latency (<100ms). Which deployment option on Google Cloud is best?

Accepted Answer

Deploy on Vertex AI Endpoints. Vertex AI Endpoints is the correct choice because it is purpose-built for deploying TensorFlow models with optimized serving infrastructure, including automatic scaling, GPU/TPU support, and built-in monitoring for latency-sensitive online predictions. It provides a managed endpoint that can achieve sub-100ms latency by leveraging model optimization techniques like TensorFlow Serving and hardware accelerators, which are not available in the other options.

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Deploy on Cloud Run with a custom container

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Deploy on Cloud Functions

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Deploy on AI Platform Prediction (legacy)