Question 1

A customer insights team at scrumtuous.com needs to quickly build and train a sentiment model on review text with their own labels such as “delight” and “frustration”. They have about 3,000 annotated examples, they want strong predictive accuracy, and they prefer a solution that does not require writing code. Which Google Cloud service should they use?

Question 2

You are training an object detection model on Vertex AI using a single worker with one GPU and epochs are slow. What should you do to reduce total training time without degrading model quality?

Question 3

You lead the machine learning platform team at a digital media analytics startup, and you need a single place to track experiment lineage, parameters, executions, and generated artifacts so that results remain reproducible across projects. Which management solution should your team adopt?

Question 4

Which approach ensures that the same preprocessing is used in the Dataflow Apache Beam training pipeline and in low latency Vertex AI predictions to prevent input skew?

Question 5

At a digital newspaper publisher you trained a TensorFlow model on Vertex AI using multiple years of subscriber history to predict who will renew in the next 18 months, and the model is now serving online predictions; stakeholders want to see which single customer feature most influenced each individual prediction at request time, so what should you do?

Question 6

You need to predict same-day purchases using tabular data stored in BigQuery and provide explanations for each prediction. Which Google Cloud approach provides instance-level feature attributions?

Question 7

A commerce team at mcnz.com is building a BigQuery ML linear regression model to estimate the likelihood that a visitor will buy a product, and the customer_city field is one of the most predictive features with about 180 unique values. You want the training table to be fully columnar for the model and you want to keep this feature while doing as little coding as possible. What should you do?

Question 8

Which Google Cloud solution provides a fully managed and scalable way to automate notebook-based steps for data validation, training, and evaluation on a three-week schedule as the dataset grows from 250 GB to 25 TB?

Question 9

You work for a meal delivery platform. A model chooses which promo to display on the checkout page based on the basket contents and the customer’s profile. The prediction service on Google Cloud merges the live cart with a row from a BigQuery table named cust_purchase_log that holds 180 days of transaction history and then sends those features to the model. The web team reports that the promo widget times out because predictions are not fast enough to render with the rest of the page. What change should you make to reduce total latency?

Question 10

You collect confirmed rider check ins for shuttle stops on Google Kubernetes Engine at least 18 hours in advance. Based on this confirmed demand, which approach should you use to plan routes and determine bus size?

Question 11

At AuroraFinTech you are running a synchronous training job on two GPUs and the profiler shows the GPUs often wait on input. The dataset is split across 1,500 TFRecord files in Cloud Storage and step time is dominated by file reads. You want to reduce input latency and accelerate training without changing the model architecture. What should you do?

Question 12

Which validation method best evaluates a time series demand model using the most recent behavior before moving to production?

Question 13

A midmarket logistics analytics team at Northwind Mobility runs PySpark-based data science pipelines on its own servers and now wants to test moving a single PySpark job to Google Cloud with the least setup and cost. What should you do first to start this proof of concept?

Question 14

Which GCP approach delivers low latency streaming predictions by reading from a Pub/Sub request topic, automatically reloading the model from Cloud Storage about every 30 minutes, and publishing results to BigQuery and to a Pub/Sub response topic?

Question 15

At BrightWave Insights you trained a model in a Vertex AI Workbench notebook that reports strong validation RMSE. You plan to search across 24 hyperparameters with defined ranges. You want a tuning approach that finishes in the least wall clock time, and you also want to keep cost, reproducibility, model quality, and scalability in mind as long as they do not slow the job. What should you do?

Question 16

In Vertex AI Workbench, how should you track experiment runs with parameters, metrics, datasets, and models and then promote the winning approach to production?

Question 17

An ad tech startup named LumaDSP trained a scikit-learn model for click prediction and plans to deploy it on Vertex AI for both real time and batch predictions. The model expects 32 input features and several fields must be normalized during inference, and you want to package the solution with as little extra code as possible. What should you do?

Question 18

Which event-driven method in Google Cloud triggers a Vertex AI Pipelines run only when a new Cloud Storage object is created while keeping compute costs low?

Question 19

Solstice Analytics has built a Vertex AI Pipelines workflow that trains custom models with about 50 executions each week. The team wants the easiest way to collaborate when comparing metrics across executions through both programmatic access and interactive visualizations in Google Cloud. What should you add to the pipeline and which tools should the team use?

Question 20

In a photo policy classifier, which Vertex AI AutoML objective should you choose to minimize false negatives for the noncompliant class?

Question 21

You are designing a product recommendation system for a midmarket home furnishings retailer. Three years of purchase history is stored in BigQuery and roughly 90 GB of clickstream logs are saved as CSV files in Cloud Storage. You need to run exploratory analysis, clean data, and train models repeatedly while trying different algorithms, and you want to keep costs and setup effort low. How should you set up your working environment?

Question 22

What is the simplest way to use a BigQuery ML logistic regression model for real time predictions within a Dataflow streaming pipeline while keeping latency under 30 milliseconds per event?

Question 23

At Northstar Retail’s analytics group the machine learning team needs to run many quick trials using different feature sets, model variants, and tuning parameters. They want a low maintenance approach that automatically records accuracy metrics for every run and allows engineers to retrieve those metrics over time through an API for comparisons and dashboards. Which approach should they adopt?

Question 24

Only 0.2% of labels are failures and a standard training approach predicts the majority class. How should you address this extreme class imbalance so the model learns to detect the rare failure class?

Question 25

At the streaming service mcnz.com your team has deployed a model on a Vertex AI endpoint and a Vertex AI Pipeline retrains the model when a Cloud Function is invoked. You want to keep the model current while keeping training spend predictable and low. How should you trigger retraining to balance freshness and cost?

Question 1

The correct option is Vertex AI AutoML for Text. It lets the team train a custom sentiment classifier on their own labels without writing code and it works well with a few thousand annotated reviews, which fits the 3,000 labeled examples they have.

This service provides a guided console workflow to import labeled text, automatically extracts features, and selects and tunes models to deliver strong predictive accuracy. It also provides built in evaluation and simple deployment so the team can move quickly from data to predictions without managing infrastructure or writing code.

BigQuery ML is not a match because it requires SQL to build and evaluate models and it typically needs manual text preprocessing and feature engineering, which does not meet the no code preference and is better suited to tabular use cases.

Cloud Natural Language API offers pretrained sentiment scores and magnitudes but it cannot learn custom categories such as delight and frustration, so it does not satisfy the need to train on the team’s own labels.

Vertex AI Training targets custom training with user provided code and containers, which adds complexity and does not meet the no code requirement for this scenario.

Question 2

The correct option is Use MultiWorkerMirroredStrategy on Vertex AI for distributed training.

Use MultiWorkerMirroredStrategy on Vertex AI for distributed training scales training across multiple workers with synchronous data parallelism so you increase throughput and reduce wall clock time per epoch. On Vertex AI you can run custom training with multiple workers that coordinate gradients, and if you keep the effective global batch size and learning rate consistent then model quality is preserved while time to train decreases.

Vertex AI Vizier hyperparameter tuning explores parameter configurations across multiple trials and can improve model quality, yet it does not make a single training run faster. It often increases total compute time because it runs many jobs.

Increase machine memory to 512 GB and raise batch size targets host memory rather than GPU memory, which is usually not the bottleneck for GPU training. Raising batch size can change optimization dynamics and can harm convergence, and it does not guarantee shorter epoch times.

Move the job to a single Cloud TPU v5e does not provide distributed scaling and may require code changes for TPU compatibility. Performance gains are workload dependent, and moving to a single different accelerator does not reliably reduce training time without risking changes to training behavior.

Question 3

The correct option is Use Vertex ML Metadata to track lineage, artifacts, and executions.

This service provides a managed metadata store that captures experiment lineage, parameters, executions, and generated artifacts so teams can reproduce results across projects. It integrates with Vertex AI workflows and builds lineage graphs that connect datasets, code versions, runs, and models, which enables auditability and repeatability.

Store training run logs and metrics in BigQuery is not sufficient because BigQuery is a data warehouse that can store numbers and logs but it does not model ML specific entities like executions and artifacts or their lineage relationships, and it does not automatically connect pipeline steps for reproducibility.

Google Cloud operations suite focuses on infrastructure and application monitoring and logging, so it does not capture experiment lineage, parameters, and artifact relationships, and it is not an ML metadata system.

Vertex TensorBoard visualizes training curves and metrics and helps compare runs, but it is not designed to track end to end lineage, artifacts, and executions across projects.

Question 4

The correct option is Refactor Beam transforms into a shared library and run the same code in Vertex AI prediction.

The shared library approach removes duplicate logic and guarantees that the exact same transformation code is used during both Dataflow training and online prediction. You package your Beam transform logic as reusable utilities or composite transforms and include that library in your Dataflow pipeline for training while also installing it in the Vertex AI prediction container. This keeps preprocessing consistent which prevents training serving skew and it also keeps latency low because preprocessing runs inside the prediction container rather than through an external service.

The shared library approach also enables stronger testing. You can unit test the transform functions once and rely on the same tests to validate both training and serving behavior. This makes the system easier to maintain and reduces the chance of silent divergence in preprocessing.

Use Dataflow streaming to preprocess each request before the endpoint is not suitable because it adds an extra network hop and queueing which increases latency and creates an unnecessary dependency during serving. It also does not ensure that the code path is identical to training and it complicates scaling and cost.

Add request schema checks only validates structure and types so it cannot guarantee that the same feature engineering and normalization steps are applied. Schema checks reduce bad inputs but they do not remove skew caused by different preprocessing logic.

Use Vertex AI Feature Store helps with managing and serving precomputed features and can reduce some kinds of skew when you materialize features consistently. However it does not ensure that your Dataflow Apache Beam transforms are executed identically at prediction time and it is unnecessary if your preprocessing logic needs to run per request inside the prediction container.

Question 5

The correct option is Enable Vertex AI Explainable AI on the deployed endpoint and request predictions with explanations to return sampled Shapley attributions for each instance.

With Explainable AI enabled on the endpoint, each online prediction includes feature attribution values that quantify how much each input feature contributed to the prediction for that specific instance. You can surface the top attribution by magnitude to identify the single most influential customer feature at request time. This works with your TensorFlow model and keeps explanations aligned with the model you are actually serving.

Train a Logistic Regression model in BigQuery ML on the same data and use coefficient magnitudes to infer which features matter most provides global importance for a different model and not local explanations for each served prediction. Coefficients from a separate linear model do not explain individual outputs of your deployed TensorFlow model.

Use the What If Tool in TensorBoard to remove features one at a time and compare the change in overall model performance supports exploratory analysis but it is about dataset level or scenario comparisons and not about returning per instance attributions with each online prediction. It does not integrate explanations directly into the endpoint response.

Write predictions and features to BigQuery and calculate Pearson correlations with the label using the CORR function yields global associations across the dataset and not local influence on a single prediction. Correlation does not provide per request feature attributions and can be misleading for non linear models or interacting features.

Question 6

The correct option is Vertex AI AutoML Tabular with attributions enabled.

Vertex AI AutoML Tabular with attributions enabled uses Vertex Explainable AI to return per prediction feature attributions for tabular models. When explanations are enabled, each prediction includes scores that show how much each input feature pushed the prediction toward or away from the outcome, which satisfies the need for instance level attributions. You can train on BigQuery data and request explanations for both online and batch predictions.

BigQuery ML logistic regression using coefficients is not correct because coefficients provide global weights learned by the model and they describe overall influence rather than how features affected a specific row’s prediction, so they are not per example attributions.

Vertex AI custom model without explanations is not correct because if explanations are not enabled and configured, predictions do not include feature attributions, therefore there is no instance level explanation.

BigQuery ML boosted trees with ML.FEATURE_IMPORTANCE is not correct because ML.FEATURE_IMPORTANCE reports aggregate or global importance across the training data and it does not provide per row attributions for individual predictions.

Question 7

The correct option is Use Dataprep to apply one hot encoding to the customer_city field so that each city becomes its own binary indicator column.

One hot encoding turns a single categorical field into multiple numeric indicator columns so the training data becomes fully columnar and remains expressive across about 180 cities. Using Dataprep provides a largely no code path to generate those columns and write the result to BigQuery, which satisfies the requirement to keep the feature with minimal coding. Note that Dataprep has been retired on Google Cloud, so on newer exams this workflow may appear as an equivalent preparation step using other managed options or native SQL transformations, yet among the given choices it best matches the requirement.

Use BigQuery to create a view that removes the customer_city column is incorrect because it discards one of the most predictive features and does not meet the requirement to keep it.

Use TensorFlow to build a categorical vocabulary for customer_city and upload the vocabulary along with the model to BigQuery ML is incorrect because it introduces unnecessary custom modeling and operational complexity and does not provide a minimal coding solution inside BigQuery. BigQuery ML does not take a separately uploaded vocabulary for native models in this workflow.

Use Cloud Data Fusion to convert each city to a numeric region code such as 1 to 5 and train on that single code is incorrect because it collapses many distinct categories into a few labels and imposes an artificial numeric order, which can mislead a linear model and reduce predictive power.

Question 8

The correct option is Vertex AI Pipelines with a TFX pipeline. This choice provides a fully managed and scalable way to orchestrate data validation, training and evaluation on a fixed schedule as data volume grows from hundreds of gigabytes to tens of terabytes.

Vertex AI Pipelines with a TFX pipeline turns the steps you prototype in notebooks into reusable pipeline components and stages. TFX provides built in components for data validation, model training and evaluation, and Vertex AI Pipelines manages execution, tracking and artifact lineage without you managing infrastructure. You can schedule recurring runs every three weeks and the service elastically scales underlying resources to handle large datasets.

This approach integrates with distributed training and data processing services on Google Cloud, which lets you process 250 GB today and scale to 25 TB as the pipeline reruns. You also gain reproducibility and auditability through managed metadata and versioning, which are important for production ML automation.

BigQuery ML with scheduled queries is not a good fit because it is limited to SQL driven model training and prediction inside BigQuery and it does not orchestrate multi step ML workflows such as separate data validation and model evaluation, nor does it automate notebook code.

Cloud Composer with BashOperator can schedule tasks but it leaves you to script ML steps yourself and manage operator reliability. It lacks the ML specific components, metadata tracking and integrated model lifecycle that a managed ML pipeline service provides.

Kubeflow Pipelines on GKE can run pipelines but it is self managed on your own GKE clusters. The question calls for a fully managed approach, so running and maintaining clusters yourself makes this a less suitable choice.

Question 9

The correct option is Serve the customers’ historical features from a low latency database.

The current bottleneck is fetching and joining 180 days of history from BigQuery during a synchronous web request. BigQuery is optimized for analytical scans and is not designed for per-request millisecond lookups. Moving the precomputed customer features into an online store lets the prediction path perform a single key-based read and then run inference, which cuts end-to-end latency and removes variability. On Google Cloud you can store online features in Vertex AI Feature Store�s online store or in a low latency database such as Cloud Bigtable, then fetch them at inference time in a few milliseconds while the live cart data remains in memory.

Attach an NVIDIA T4 GPU to the model server is not addressing the root cause. The slow step is retrieving and joining historical features at request time, not the model compute. A GPU can speed up heavy model inference but it will not materially reduce the time spent waiting on BigQuery.

Create a BigQuery materialized view that precomputes the join to each customer’s last 180 days of purchases still leaves you issuing a BigQuery query during page rendering. Materialized views can accelerate analytics but BigQuery remains an analytical warehouse with second-level interactive latencies and is not intended for sub-100 millisecond per-request lookups in a web path. Freshness lag also makes it less suitable for real-time personalization.

Increase the number of model serving replicas behind a load balancer can improve throughput when there is queueing, yet it does not make a single prediction faster when the latency is dominated by the upstream BigQuery fetch. Without moving features to an online store, requests will continue to time out.

Question 10

The correct option is Capacitated route on confirmed stops.

Because rider check ins are confirmed well in advance, this is a deterministic planning problem. The right approach is to formulate a capacity constrained vehicle routing problem that assigns stops to buses and selects appropriate bus sizes while respecting capacity and time constraints. This approach can be solved efficiently with mature optimization tools and will directly produce feasible routes and fleet sizing decisions from the known demand.

Reinforcement learning for routing is not appropriate here because there is no need to learn a policy when demand is fixed and constraints must be strictly satisfied. Classical optimization will deliver high quality solutions more reliably and with far less tuning for this kind of deterministic problem.

Vertex AI Forecast targets time series prediction problems yet there is nothing to predict when riders have already confirmed check ins. Forecasting would introduce avoidable error and would still not create the actual routes or vehicle assignments that are required.

Tree based regression for demand would build a model to estimate demand even though demand is already known. It would add complexity without value and you would still need a routing optimization step to produce implementable plans.

Question 11

The correct option is Enable parallel interleave in the input pipeline to read from multiple files simultaneously. This approach issues concurrent reads across many TFRecord shards which hides per file latency from Cloud Storage and increases input throughput so the two GPUs are kept busy without any change to the model.

Using interleave with parallel calls in tf.data lets the pipeline open multiple TFRecord files at once and mix their records as they are produced. With an appropriate cycle length and num parallel calls setting the input stage can overlap file I O and parsing so that step time is no longer dominated by single file reads. This technique is a standard remedy when you have many shards and the profiler shows the accelerators waiting on input because it converts a largely serial file access pattern into a parallel one while remaining within the training job and not adding external systems.

Provision a machine with more vCPUs to increase data preparation throughput is not the best fix when the bottleneck is file I O from Cloud Storage. Adding CPUs can help parsing but if reads are serialized across files then the GPUs will continue to stall because input latency has not been reduced.

Insert a cache transformation in the pipeline after parsing so later epochs reuse data does not help the first epoch which is when the GPUs are already waiting. It can also be impractical if the dataset does not fit in memory or local disk and it still does not parallelize the initial file reads that dominate step time.

Move parsing and shuffling to Cloud Dataflow and write data to a single large file before training adds complexity and can reduce read parallelism during training because a single large object is typically consumed sequentially. The training job would still wait on I O and you would lose the benefit of many shards that can be read concurrently within the tf.data pipeline.

Question 12

The correct option is Hold out the most recent 21 days for time based validation to reflect current behavior. This focuses evaluation on the latest patterns and preserves temporal order, which makes it the strongest indicator of how the model will perform immediately after deployment.

This approach mirrors real world usage because the model will forecast the near future and should therefore be validated on the most recent window. It avoids information leakage from the future into the past and it captures any recent shifts in demand that older data may not reflect. Evaluating on a contiguous and recent time slice provides a faithful estimate of generalization under current conditions.

Apply k fold cross validation across the entire history is not appropriate for time series because it breaks temporal ordering or mixes future and past when creating folds. Even time series aware folds do not directly evaluate on the most recent contiguous period, so they are not the best choice when you want to measure current behavior before production.

Vertex AI Model Monitoring is for monitoring models after deployment and detecting drift or anomalies in production. It does not validate a model offline before release and therefore does not answer the question about pre production evaluation on recent behavior.

Create a random 25 percent holdout across all records regardless of date introduces leakage from future records into the training process and fails to reflect the sequential nature of time series. It also does not focus evaluation on the latest period, so it underestimates shifts in recent demand.

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