PMLE Practice Test 7 — 10 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.