PMLE Practice Test 21 — 15 Questions

Question 1

An ML team wants to run a hyperparameter tuning job on Vertex AI using a pre-built pipeline component. Which component should they use?

Accepted Answer

HyperparameterTuningJobRunOp. The HyperparameterTuningJobRunOp is the correct pre-built Vertex AI pipeline component specifically designed to launch a hyperparameter tuning job. It wraps the Vertex AI HyperparameterTuningJob API, allowing you to specify the worker pool spec, metric target, and parameter specifications directly within a Kubeflow Pipelines (KFP) or Vertex AI Pipelines orchestration context.

Answer

AutoMLTabularTrainingJobRunOp

Answer

CustomTrainingJobRunOp with hyperparameter arguments.

Answer

ModelTrainComponent

Question 2

Your team has deployed a model on Vertex AI endpoints and you are planning an A/B test to compare a new challenger model (v2) against the current champion (v1). The test should measure business metrics such as click-through rate. Which THREE steps should you take to set up the A/B test correctly? (Choose 3 correct answers)

Accepted Answer

Deploy the challenger model (v2) to the same endpoint as the champion (v1).. Option A is correct because Vertex AI endpoints natively support deploying multiple models to the same endpoint, which is the prerequisite for serving both champion v1 and challenger v2 behind a single prediction URL. Option E is correct because once both models share an endpoint, you configure a traffic split (e.g., 90% to v1 and 10% to v2) via the deployed model's traffic percentage, which is exactly how Vertex AI routes online prediction requests between model versions for an A/B test. Option B is correct because to measure business metrics such as click-through rate per variant, the application must log which model version (or deployed model ID) served each prediction so that downstream clicks can be attributed to v1 or v2. Option C is not appropriate because shifting DNS traffic is a coarse, infrastructure-level approach that does not use Vertex AI's built-in traffic splitting and cannot reliably target a percentage of prediction requests. Option D is not appropriate because Vertex AI Experiments is designed for tracking and comparing training/evaluation runs and metrics, not for routing live prediction traffic between deployed model versions.

Answer

Create a new endpoint for v2 and gradually shift DNS traffic.

Answer

Use Vertex AI Experiments to compare model performance.

Question 3

You are designing a batch prediction pipeline using Vertex AI. The input data is 50 TB in CSV format on GCS. The model requires feature engineering that involves complex transformations (e.g., datetime parsing, one-hot encoding). Which TWO services or steps should you include in your pipeline?

Accepted Answer

Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.. Option D is correct because Dataflow is the managed, horizontally scalable Apache Beam service designed for large-scale ETL like 50 TB of CSV, and it can perform the required complex transformations (datetime parsing, one-hot encoding) before writing the engineered features to GCS in TFRecord format, which is an efficient binary format for ML training and prediction. Option C is correct because a Vertex AI batch prediction job can then consume those processed TFRecord files directly from GCS as its input source, letting the model run inference at scale without re-doing feature engineering. Option A is not appropriate because Cloud Functions is an event-driven, short-lived serverless compute service with limited memory and execution time, making it unsuitable for transforming 50 TB of data file-by-file. Option B is not appropriate because Cloud SQL is a relational OLTP database, not a scalable store for massive intermediate ML feature data. Option E is not appropriate because writing engineered features to BigQuery does not produce the TFRecord input that Vertex AI batch prediction expects, and BigQuery is not the target format for this pipeline's model input.

Answer

Use Cloud Functions to transform each file individually.

Answer

Use Cloud SQL to store intermediate results.

Answer

Use Dataflow to read CSV, perform feature engineering, and write to BigQuery.

Question 4

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.

Answer

Deploy on Cloud Run with a custom container

Answer

Deploy on Cloud Functions

Answer

Deploy on AI Platform Prediction (legacy)

Question 5

You need to set up monitoring for a Vertex AI model that serves predictions in real-time. The model is expected to have a latency SLA of under 100ms. Which metric should you configure an alert on to ensure the SLA is met?

Accepted Answer

p99 latency of prediction requests. P99 latency measures the worst-case latency experienced by 99% of requests, which is the standard metric for enforcing a strict SLA like under 100ms. Monitoring p99 ensures that even the slowest 1% of requests do not violate the threshold, providing a robust guarantee for real-time predictions.

Answer

p50 latency of prediction requests

Answer

Prediction drift score

Answer

Number of prediction requests per second

Question 6

A machine learning engineer is training a TensorFlow model on Vertex AI using distributed training with the MultiWorkerMirroredStrategy. The training job uses 4 workers with 4 GPUs each. The engineer notices that the training is not scaling linearly. What is the most likely cause?

Accepted Answer

Communication overhead due to gradient synchronization. With MultiWorkerMirroredStrategy, each worker computes gradients independently on its local batch, then all-reduces gradients across workers via collective communication (e.g., NCCL or gRPC). As the number of workers increases, the communication overhead for gradient synchronization grows, often dominating the per-step time and preventing linear scaling. This is the most common bottleneck in distributed TensorFlow training, especially with many workers or small batch sizes per worker.

Answer

The model architecture is too simple to benefit from distribution

Answer

The workers are not using the same version of TensorFlow

Answer

The GPUs are not configured correctly

Question 7

Your Vertex AI endpoint receives many identical prediction requests (same input features). You want to cache responses to reduce latency and cost. Which Google Cloud service should you use?

Accepted Answer

Cloud Memorystore for Redis. Cloud Memorystore for Redis is an in-memory data store that provides sub-millisecond latency, making it ideal for caching prediction responses. By caching identical prediction requests, you can reduce the number of calls to the Vertex AI endpoint, lowering latency and cost. Redis supports key-value storage with TTL, perfect for caching.

Answer

Cloud CDN

Answer

Bigtable

Answer

Cloud Storage with object versioning

Question 8

A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?

Accepted Answer

Copy the new_data table to the US region using the BigQuery UI or CLI. The simplest fix is to move the new_data table to the same region as the model (US). BigQuery ML requires that the model and the data used for predictions reside in the same multi-region or regional location. Copying the table via the BigQuery UI or CLI (e.g., `bq cp`) is a straightforward, no-code operation that avoids retraining or exporting the model.

Answer

Recreate the model in the EU region using the same training data

Answer

Enable cross-region query in BigQuery settings

Answer

Export the model from US and import it to EU

Question 9

A team uses Vertex AI Feature Store with an online store for low-latency serving. They need to support frequent updates to features (e.g., every minute) and require high write throughput (thousands of writes per second). Which online store type should they choose?

Accepted Answer

Bigtable online store. Bigtable online store is designed for high-throughput, low-latency serving with frequent updates, making it suitable for thousands of writes per second and minute-level feature refreshes. It leverages Bigtable's scalable NoSQL architecture, which handles high write loads efficiently. Optimized online store is for low-latency but may not sustain such high write throughput, while Firestore and Cloud SQL have lower write limits.

Answer

Optimized online store

Answer

Firestore online store

Answer

Cloud SQL online store

Question 10

The pipeline fails during the evaluate component with error "Model not found". What is the most likely cause?

Accepted Answer

The model_id parameter is referencing the wrong output. The error 'Model not found' during the evaluate component indicates that the model_id parameter is referencing an output that does not exist or is incorrectly named. In Vertex AI Pipelines, the evaluate component takes the model artifact from a previous training step via an output parameter or artifact reference. If the model_id parameter points to a wrong output (e.g., a different step's output or a misspelled reference), the pipeline cannot locate the model. This is the most likely cause because the error is specific to model resolution, not dataset or threshold issues.

Answer

The dataset_id is misspelled

Answer

The training container did not produce a model artifact

Answer

The threshold value is invalid

Question 11

A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

Accepted Answer

Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.. BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without requiring manual feature engineering, aligning with the goal of minimizing low-code ML effort. This approach leverages BigQuery's native SQL interface and scales automatically, making it ideal for the described e-commerce scenario.

Answer

Use a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.

Answer

Write a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.

Answer

Export the data to CSV and use AutoML Tables to train a recommendation model.

Question 12

An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?

Accepted Answer

Time travel for accessing previous versions. Delta Lake provides time travel (B), allowing queries against earlier snapshots of a table via version numbers or timestamps, which directly supports the team's data versioning requirement. It also delivers schema enforcement and evolution (C), rejecting writes that violate the table schema while permitting controlled schema changes such as adding columns. Additionally, Delta Lake brings ACID transactions (D) to data lakes, ensuring atomic, consistent, isolated, and durable writes on top of object storage like GCS. Options A and E are not Delta Lake benefits: encryption at rest is handled by the underlying storage service (e.g., Google Cloud Storage), not Delta Lake itself, and Delta Lake is a storage/transaction layer rather than a built-in real-time streaming engine.

Answer

Automatic data encryption at rest

Answer

Built-in real-time streaming

Question 13

A data-processing pipeline using Dataflow needs to incorporate a custom ML prediction step. The team wants to maintain fast processing and minimize latency. What is the optimal approach?

Accepted Answer

Use a custom ParDo transform in Dataflow that calls Vertex AI Prediction API directly. Using a custom ParDo transform in Dataflow allows the pipeline to call the Vertex AI Prediction API synchronously within each worker, avoiding the overhead of external triggers, intermediate storage, or asynchronous messaging. This keeps the data in-memory and minimizes latency by processing predictions inline with the Dataflow streaming or batch pipeline.

Answer

Write the data to Cloud Storage, trigger a Cloud Function to call the model, and write results back

Answer

Send data to a Pub/Sub topic and have a separate subscriber that runs predictions

Answer

Stream data through Cloud Functions that serve predictions and write to BigQuery

Question 14

You need to run a custom training job on Vertex AI using a pre-built container for scikit-learn. Which container image should you specify?

Accepted Answer

us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0.23-0. For a scikit-learn custom training job on Vertex AI, you must specify a pre-built container image that includes scikit-learn. The correct image is us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0.23-0, which is the official Vertex AI pre-built training container for scikit-learn version 0.23 on CPU. Other images correspond to different frameworks (PyTorch, XGBoost, TensorFlow).

Answer

us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-9

Answer

us-docker.pkg.dev/vertex-ai/training/xgboost-cpu.1-3

Answer

us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-6

Question 15

An ML team is deploying a model to Vertex AI for the first time. Which THREE are best practices for scaling from prototype to production?

Accepted Answer

Store all features in a Feature Store for consistency.. Option B is correct because storing features in a Vertex AI Feature Store ensures the same feature transformations are used at training and serving time, preventing training-serving skew and enabling feature reuse and consistency across models. Option D is correct because production models degrade over time due to data drift and concept drift, so continuous monitoring of prediction quality, accuracy, and drift metrics is essential to detect degradation and trigger remediation. Option E is correct because automating retraining and deployment with Vertex AI Pipelines creates a repeatable, auditable MLOps workflow that reduces manual error and lets the model adapt to new data efficiently. Option A is not a best practice because manual scaling based on historical patterns is reactive and error-prone; production should use autoscaling driven by live metrics such as CPU, GPU, or request load. Option C is not a best practice because a single large instance creates a single point of failure and does not scale horizontally, whereas Vertex AI supports distributed serving with multiple replicas for resilience and elasticity.

Answer

Manually scale instances based on historical traffic patterns.

Answer

Use a single large instance to simplify management.