Professional Machine Learning Engineer Free Practice Questions — Page 4
Question 32
You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to train a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you increase the number of fraudulent transactions that are detected?
A. Add more non-fraudulent examples to the training set.
B. Reduce the maximum number of node hours for training.
C. Increase the probability threshold to classify a fraudulent transaction.
D. Decrease the probability threshold to classify a fraudulent transaction.
Show Answer
Correct Answer: D
Explanation: The goal is to increase detection of fraudulent transactions (maximize recall). Lowering the probability threshold makes the model classify more transactions as fraudulent, reducing false negatives at the cost of more false positives, which is acceptable when missing fraud is costly.
Question 33
You work as an ML researcher at an investment bank, and you are experimenting with the Gemma large language model (LLM). You plan to deploy the model for an internal use case. You need to have full control of the mode's underlying infrastructure and minimize the model's inference time. Which serving configuration should you use for this task?
A. Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.
B. Deploy the model on a Google Kubernetes Engine (GKE) cluster by using the deployment options in Model Garden.
C. Deploy the model on a Vertex AI endpoint by using one-click deployment in Model Garden.
D. Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by cresting a custom yaml manifest.
Show Answer
Correct Answer: D
Explanation: The requirement is full control over the underlying infrastructure and the ability to optimize for minimal inference latency. Deploying Gemma on GKE with a manually created custom YAML manifest gives complete control over node types, GPU selection, autoscaling, networking, container runtime, and low‑level performance tuning. Vertex AI options (A and C) abstract away infrastructure details, and Model Garden’s GKE deployment (B) is more opinionated and optimized for convenience rather than maximum control.
Question 34
Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?
A. Use a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.
B. Use Vertex AI Pipelines for model artifacts and MLflow for model lineage.
C. Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
D. Implement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.
Show Answer
Correct Answer: A
Explanation: Vertex AI Pipelines natively logs artifacts, parameters, and executions into Vertex ML Metadata, providing automatic end‑to‑end lineage. Using the Vertex AI SDK allows adding custom metadata in a reusable way. This is the simplest, fully managed, and future‑proof native solution on Google Cloud. Options involving Experiments or external/custom tools are redundant or less simple.
Question 35
You lead a data science team that is working on a computationally intensive project involving running several experiments. Your team is geographically distributed and requires a platform that provides the most effective real-time collaboration and rapid experimentation. You plan to add GPUs to speed up your experimentation cycle, and you want to avoid having to manually set up the infrastructure. You want to use the Google-recommended approach. What should you do?
A. Configure a managed Dataproc cluster for large-scale data processing. Configure individual Jupyter notebooks on VMs that each team member uses for experimentation and model development.
B. Use Colab Enterprise with Cloud Storage for data management. Use a Git repository for version control.
C. Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.
D. Configure a distributed JupyterLab instance that each team member can access on a Compute Engine VM. Use a shared code repository for version control.
Show Answer
Correct Answer: B
Explanation: The requirements emphasize real-time collaboration, rapid experimentation, GPU acceleration, and avoiding manual infrastructure setup using a Google‑recommended approach. Colab Enterprise is a fully managed, collaborative notebook environment with built‑in GPU support and no infrastructure management, making it ideal for geographically distributed teams. Vertex AI Workbench provides more control but requires notebook and infrastructure setup and is less focused on real‑time collaboration.
Question 36
You are an AI engineer that works for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers that have been identified as at risk of churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?
A. Use Vertex AI’s prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.
B. Use Vertex AI’s prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.
C. Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.
D. Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.
Show Answer
Correct Answer: C
Explanation: The use case is weekly outreach to a set of customers, which is an offline, periodic workload rather than real-time inference. Vertex AI batch prediction is purpose-built for this scenario, allowing you to run large-scale predictions on a schedule with minimal operational overhead. Deploying to a Vertex AI endpoint for batch prediction lets Google manage infrastructure, scaling, and execution, reducing maintenance compared to managing GKE, Cloud Run, or an always-on online endpoint. Scheduling the batch job weekly aligns exactly with the business requirement and is more cost-efficient than querying an online endpoint.
Question 37
Your team is experimenting with developing smaller, distilled LLMs for a specific domain. You have performed batch inference on a dataset by using several variations of your distilled LLMs and stored the batch inference outputs in Cloud Storage. You need to create an evaluation workflow that integrates with your existing Vertex AI pipeline to assess the performance of the LLM versions while also tracking artifacts. What should you do?
A. Develop a custom Python component that reads the batch inference outputs from Cloud Storage, calculates evaluation metrics, and writes the results to a BigQuery table.
B. Use a Dataflow component that processes the batch inference outputs from Cloud Storage, calculates evaluation metrics in a distributed manner, and writes the results to a BigQuery table.
C. Create a custom Vertex AI Pipelines component that reads the batch inference outputs from Cloud Storage, calculates evaluation metrics, and writes the results to a BigQuery table.
D. Use the Automatic side-by-side (AutoSxS) pipeline component that processes the batch inference outputs from Cloud Storage, aggregates evaluation metrics, and writes the results to a BigQuery table.
Show Answer
Correct Answer: C
Explanation: You already have batch inference outputs stored in Cloud Storage and need an evaluation step that integrates cleanly with an existing Vertex AI Pipeline while tracking artifacts. The correct approach is to create a custom Vertex AI Pipelines component that reads those outputs, computes evaluation metrics, and logs results and artifacts through the pipeline. AutoSxS is primarily designed to run and compare models end-to-end rather than consume precomputed batch outputs, and Python-only or Dataflow jobs would not integrate as naturally with pipeline artifact lineage.
Question 38
You are an ML engineer at a bank. You need to build a solution that provides transparent and understandable explanations for AI-driven decisions for loan approvals, credit limits, and interest rates. You want to build this system to require minimal operational overhead. What should you do?
A. Deploy the Learning Interpretability Tool (LIT) on App Engine to provide explainability and visualization of the output.
B. Use Vertex Explainable AI to generate feature attributions, and use feature-based explanations for your models.
C. Use AutoML Tables with built-in explainability features, and use Shapley values for explainability.
D. Deploy pre-trained models from TensorFlow Hub to provide explainability using visualization tools.
Show Answer
Correct Answer: B
Explanation: The requirement is transparent, understandable explanations for model decisions with minimal operational overhead. Vertex Explainable AI is a fully managed service that integrates directly with custom and managed models and automatically generates feature attributions suitable for regulatory and business explanations. It avoids deploying and maintaining separate tools (like LIT) and does not constrain you to AutoML-only workflows, making it the best balance of low overhead, flexibility, and compliance-ready explainability.
Question 39
You work for a media company that operates a streaming movie platform where users can search for movies in a database. The existing search algorithm uses keyword matching to return results. Recently, you have observed an increase in searches using complex semantic queries that include the movies’ metadata such as the actor, genre, and director.
You need to build a revamped search solution that will provide better results, and you need to build this proof of concept as quickly as possible. How should you build the search platform?
A. Use a foundational large language model (LLM) from Model Garden as the search platform’s backend.
B. Configure Vertex AI Vector Search as the search platform’s backend.
C. Use a BERT-based model and host it on a Vertex AI endpoint.
D. Create the search platform through Vertex AI Agent Builder.
Show Answer
Correct Answer: B
Explanation: The requirement is to quickly build a proof of concept that supports semantic search over movie metadata (actor, genre, director). Vertex AI Vector Search is purpose-built for semantic retrieval using embeddings and metadata filtering, making it faster and more appropriate than keyword search. Using a full LLM backend (A) or Agent Builder (D) adds unnecessary conversational overhead and latency, while hosting a custom BERT model (C) requires more setup and tuning. Vector Search provides the most direct, scalable, and efficient solution for this use case.
Question 40
You are building an application that extracts information from invoices and receipts. You want to implement this application with minimal custom code and training. What should you do?
A. Use the Cloud Vision API with TEXT_DETECTION type to extract text from the invoices and receipts, and use a pre-built natural language processing (NLP) model to parse the extracted text.
B. Use the Cloud Document AI API to extract information from the invoices and receipts.
C. Use Vertex AI Agent Builder with the pre-built Layout Parser model to extract information from the invoices and receipts.
D. Train an AutoML Natural Language model to classify and extract information from the invoices and receipts.
Show Answer
Correct Answer: B
Explanation: The requirement is to extract structured information from invoices and receipts with minimal custom code and no training. Cloud Document AI provides pre-built, purpose-built processors (such as Invoice and Receipt Parsers) that automatically extract key fields like vendor, dates, line items, and totals. Other options either require additional parsing logic, custom model training, or are not specialized for structured document extraction.
Question 41
You work for a bank. You need to train a model by using unstructured data stored in Cloud Storage that predicts whether credit card transactions are fraudulent. The data needs to be converted to a structured format to facilitate analysis in BigQuery. Company policy requires that data containing personally identifiable information (PII) remain in Cloud Storage. You need to implement a scalable solution that preserves the data’s value for analysis. What should you do?
A. Use BigQuery’s authorized views and column-level access controls to restrict access to PII within the dataset.
B. Use the DLP API to de-identify the sensitive data before loading it into BigQuery.
C. Store the unstructured data in a separate PII-compliant BigQuery database.
D. Remove the sensitive data from the files manually before loading them into BigQuery.
Show Answer
Correct Answer: B
Explanation: Company policy requires that PII remain in Cloud Storage and not be exposed in BigQuery. To still convert unstructured data into a structured, analyzable format, the sensitive fields must be transformed so they no longer contain raw PII. The Cloud DLP API is designed for this purpose: it can automatically detect and de-identify PII at scale (masking, tokenization, hashing) before loading data into BigQuery, preserving analytical value while meeting compliance requirements. Other options either leave PII in BigQuery or are not scalable.
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