PMLE Practice Exam 4 — 20 Questions

Question 1

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Accepted Answer

AutoML Video. AutoML Video supports object tracking, and Video Intelligence API provides both object detection and tracking. AutoML Vision is for images only, Natural Language for text, and Speech-to-Text for audio.

Answer

AutoML Vision

Answer

Speech-to-Text

Answer

Natural Language API

Question 2

Which Vertex AI feature allows you to reduce the size of a trained model to improve inference speed on edge devices without significant accuracy loss?

Accepted Answer

Vertex AI Model Optimization. Vertex AI Model Optimization is the correct feature because it provides model quantization, pruning, and distillation techniques specifically designed to reduce model size and improve inference latency on edge devices. This service applies post-training quantization (e.g., FP32 to INT8) and structured weight pruning to shrink the model footprint while maintaining accuracy within acceptable thresholds, directly addressing the need for efficient deployment on resource-constrained hardware.

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Matching Engine

Answer

Vertex AI Continuous Training

Question 3

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?

Accepted Answer

dsl.Condition. The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.

Answer

dsl.ParallelFor

Answer

dsl.ExitHandler

Answer

dsl.Collected

Question 4

When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?

Accepted Answer

Use Cloud Storage. Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.

Answer

Copy the dataset to each worker's local disk

Answer

Use NFS

Answer

Use Google Drive

Question 5

Which Vertex AI service is best suited for finding similar items in a large dataset based on embedding vectors, such as product recommendations or image similarity search?

Accepted Answer

Vertex AI Matching Engine. Vertex AI Matching Engine is specifically designed for high-performance vector similarity search (also known as approximate nearest neighbor search) using embedding vectors. It scales to billions of vectors and is ideal for use cases like product recommendations and image similarity search, where you need to find the most similar items based on dense vector representations.

Answer

Vertex AI Prediction Endpoint

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Feature Store

Question 6

An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?

Accepted Answer

kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json'). The Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.

Answer

gcloud ai pipelines compile command.

Answer

kfp.Client().upload_pipeline()

Answer

dsl.pipeline decorator automatically compiles at runtime.

Question 7

A company wants to classify support ticket text into categories. They have labeled historical tickets. Which Google Cloud service allows them to train a custom classification model with no code?

Accepted Answer

AutoML Natural Language. AutoML Natural Language (now part of Vertex AI) is the correct service because it enables users to train custom text classification models using labeled data without writing any code. It provides a no-code interface for uploading datasets, training models, and evaluating performance, making it ideal for classifying support ticket text into custom categories.

Answer

Vertex AI Matching Engine

Answer

Cloud Natural Language API

Answer

Document AI

Question 8

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) in Document AI allows you to route documents that the model processes with low confidence to human reviewers for validation or correction before finalizing. This directly matches the requirement to have a human review any documents the model cannot process with high confidence.

Answer

AutoML Tables model retraining

Answer

Cloud DLP for data inspection

Answer

Increase the number of processors

Question 9

A developer wants to add text translation to a mobile app. They need to translate user-generated content into multiple languages, and latency is critical. Which pre-built API should they use?

Accepted Answer

Translation API. Translation API provides fast, real-time translation for text. Natural Language API is for analysis. Text-to-Speech is for audio. Vision API is for images.

Answer

Vision API

Answer

Text-to-Speech API

Answer

Natural Language API

Question 10

A data science team is deploying a PyTorch model for real-time inference using Vertex AI Endpoints. The model requires a custom container with specific CUDA drivers and Python packages. They have created a Docker image and pushed it to Artifact Registry. The pipeline should automatically retrain the model every week and deploy the new version if it passes validation. However, the deployment step fails intermittently with the error 'The container image is not compatible with the machine type.' What is the most likely cause?

Accepted Answer

The container image requires GPU support but the machine type specified in the endpoint is a CPU-only machine.. The error 'The container image is not compatible with the machine type' indicates a mismatch between the container's hardware requirements and the machine type selected for the Vertex AI Endpoint. Since the custom container requires specific CUDA drivers, it is built for GPU acceleration. If the endpoint is configured with a CPU-only machine type (e.g., n1-standard-4), the container will fail to run because the GPU drivers cannot initialize, triggering this incompatibility error.

Answer

The service account does not have permission to pull the container from Artifact Registry.

Answer

The container's health check endpoint is not responding correctly.

Answer

The model artifact size exceeds the maximum allowed for the machine type.

Question 11

An ML team is scaling a prototype to production. The data pipeline currently reads from Cloud Storage and transforms data with a custom Python script. They need to handle higher throughput and add monitoring. Which approach should they take?

Accepted Answer

Migrate the pipeline to Apache Beam on Dataflow with Cloud Monitoring. Apache Beam on Dataflow provides a unified programming model for batch and streaming data processing, enabling automatic scaling to handle higher throughput. Cloud Monitoring integrates natively with Dataflow to track pipeline metrics, latency, and error rates, addressing the monitoring requirement. This approach is purpose-built for production-grade data pipelines, unlike ad-hoc solutions.

Answer

Deploy the Python script on a large Compute Engine instance with a cron job

Answer

Rewrite the pipeline to use Pub/Sub and Cloud Functions for processing

Answer

Use Cloud Composer to orchestrate the Python script at scale

Question 12

A team has developed a prototype of a recommendation model using a small dataset on a single VM. They need to scale to a larger dataset for production training. They plan to use Vertex AI training with a custom container. What is the best practice for handling the increased data volume?

Accepted Answer

Use TFRecord format and streaming reads.. When scaling to a larger dataset for production training on Vertex AI with a custom container, the best practice is to use TFRecord format and streaming reads. TFRecord is a binary format optimized for TensorFlow, enabling efficient data serialization and streaming from Cloud Storage without loading the entire dataset into memory. This approach supports large-scale distributed training and reduces I/O bottlenecks.

Answer

Increase the batch size to maximum.

Answer

Store all data in memory before training.

Answer

Use a single powerful VM with high memory.

Question 13

You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?

Accepted Answer

A recent code change that loads the entire dataset into memory before processing. A code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.

Answer

Increase in model size due to retraining

Answer

Decrease in the number of worker machines

Answer

Increase in input data size

Question 14

A data scientist has a TensorFlow 2.x model trained on a single GPU. They want to scale training to multiple GPUs on a single Vertex AI machine without code changes. Which strategy should they use?

Accepted Answer

MirroredStrategy. MirroredStrategy is TensorFlow's default single-machine, multi-GPU strategy. It replicates the model on each GPU and synchronizes gradients via all-reduce, and it can be enabled with minimal code changes (typically just wrapping model creation in strategy.scope()), making it the right choice for scaling on one Vertex AI machine.

Answer

MultiWorkerMirroredStrategy

Answer

TPUStrategy

Answer

CentralStorageStrategy

Question 15

A data science team has trained a TensorFlow model and wants to serve it online with minimal latency. Which Vertex AI deployment option should they use to ensure the model can handle traffic spikes without manual scaling?

Accepted Answer

Deploy the model to a Vertex AI Endpoint with automatic scaling.. Vertex AI Endpoints with automatic scaling (option B) are designed for online serving with minimal latency and can automatically adjust the number of replicas based on traffic load, handling spikes without manual intervention. This is the correct choice for a TensorFlow model requiring real-time inference and elastic scaling.

Answer

Use Vertex AI Model Garden.

Answer

Use Vertex AI Batch Prediction for offline inference.

Answer

Deploy the model to a Compute Engine VM with a load balancer.

Question 16

A company is deploying a machine learning model for real-time inference on Vertex AI. Which TWO practices improve serving performance and reliability?

Accepted Answer

Enable autoscaling to handle traffic variations.. Vertex AI's autoscaling dynamically adjusts the number of replicas based on incoming request traffic, ensuring low latency during spikes and cost savings during lulls. This is critical for real-time inference, where consistent response times are required and manual scaling would either over-provision or under-provision resources. Autoscaling uses metrics like CPU utilization or request count to scale up or down, directly improving serving performance and reliability.

Answer

Use batch prediction for all requests.

Answer

Use manual scaling with a fixed number of replicas.

Answer

Deploy all models on the same machine type for consistency.

Question 17

A company serves a PyTorch model using a custom container on Vertex AI Prediction. They notice that after a few hours, the endpoint returns 502 errors. The logs show 'Out of memory' errors. The container has a memory limit of 4GB, and the model loads a 3GB vocabulary file. What is the most likely cause and best fix?

Accepted Answer

Load the vocabulary file once at startup and reuse it.. The 502 errors and 'Out of memory' errors indicate that the container is running out of memory during inference. Since the model loads a 3GB vocabulary file, and the container has only 4GB of memory, loading this file repeatedly for each prediction request (e.g., inside the prediction handler) would quickly exhaust memory. The correct fix is to load the vocabulary file once at container startup and reuse it across all requests, which is a standard best practice for serving models with large static assets.

Answer

Increase the container memory to 8GB.

Answer

Increase the number of replicas to distribute load.

Answer

Switch to Vertex AI Batch Prediction.

Question 18

A data scientist has deployed a classification model on a Vertex AI Endpoint and wants to monitor for feature drift in the serving data compared to the training data. Which Vertex AI service should be used?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the purpose-built service that detects training-serving skew and prediction drift by comparing live serving data distributions against a baseline (typically the training dataset). It computes statistics like Jensen-Shannon divergence and can alert when drift exceeds configured thresholds. Explainable AI, Model Evaluation, and Continuous Training serve different purposes and do not provide ongoing drift detection on deployed endpoints.

Answer

Vertex AI Explainable AI

Answer

Vertex AI Model Evaluation

Answer

Vertex AI Continuous Training

Question 19

A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?

Accepted Answer

Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.. Vertex AI Model Registry provides a centralized repository for managing model versions, with support for version aliases (e.g., 'champion', 'challenger') that allow teams to promote models to production only after approval. This ensures governance and traceability, meeting the requirement that only approved models are deployed.

Answer

Store all models in a Cloud Storage bucket and manually control access via IAM permissions.

Answer

Deploy models directly from training jobs to an endpoint without version tracking.

Answer

Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.

Question 20

A company wants to monitor fairness of a model by evaluating performance metrics across demographic subgroups. They have ground truth labels stored in BigQuery. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Evaluation. Vertex AI Model Evaluation is the service designed to assess model performance using ground truth labels, including slicing metrics across demographic subgroups to detect fairness issues. It computes metrics like precision, recall, and AUC per slice, directly supporting bias and fairness analysis. This makes it the correct choice when labels are available in BigQuery.

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Prediction

Answer

Vertex AI Explainability