PDE Practice Test 34 — 15 Questions

Question 1

A team is implementing CI/CD for their ML models using Google Cloud. They want to automatically retrain and deploy a new model version when new training data arrives in Cloud Storage. Which combination of services should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cloud Storage triggers, Cloud Functions, and Vertex AI Pipelines. Cloud Storage triggers fire an event when new data arrives, which invokes a Cloud Function that can start a Vertex AI Pipeline for retraining and deploying the model. This combination provides a fully managed, event-driven CI/CD pipeline for ML models without manual intervention.

Answer

Cloud Scheduler and Vertex AI Training

Answer

Cloud Pub/Sub and Cloud Composer

Answer

Cloud Storage notifications and Cloud Build

Question 2

A data engineer needs to create a Dataflow pipeline template that can be reused across multiple environments (dev, staging, prod) with different parameters (e.g., input Pub/Sub topic, output BigQuery table). Which template type should they use?

Accepted Answer

Flex Template. Flex Templates (B) are the correct choice because they package a Dataflow pipeline as a Docker image, allowing environment-specific parameters (e.g., Pub/Sub topic, BigQuery table) to be passed at runtime via the --parameters flag. This enables true reusability across dev, staging, and prod without modifying the template code, unlike Classic Templates which require compile-time parameterization.

Answer

Dataflow Prime

Answer

Classic Template

Answer

Cloud Composer workflow template

Question 3

A user named Charlie needs to deploy a model to a Vertex AI Endpoint and also create training jobs. Which role should be assigned to Charlie?

Accepted Answer

roles/aiplatform.user. Charlie needs to deploy a model to a Vertex AI Endpoint and create training jobs. The `roles/aiplatform.user` role grants the necessary permissions to use all Vertex AI resources, including creating and managing endpoints, training jobs, models, and predictions. This role is the minimum required for a user to interact with Vertex AI services without granting broader project-level permissions.

Answer

roles/owner

Answer

roles/aiplatform.modelUser

Answer

roles/editor

Question 4

An MLOps team wants to implement continuous deployment of ML models using Cloud Build and Vertex AI. They have a GitHub repository with training code. What should they use?

Accepted Answer

Cloud Build trigger with a custom step to run Vertex AI Training job and deploy. It directly addresses the requirement for continuous deployment of ML models using Cloud Build and Vertex AI. A Cloud Build trigger can be configured to fire on GitHub commits, and a custom step in the Cloud Build pipeline can invoke a Vertex AI Training job, followed by deploying the trained model to Vertex AI Endpoints. This provides a fully automated CI/CD pipeline for ML models without additional orchestration overhead.

Answer

Deploy using Cloud Run

Answer

Vertex AI Pipelines integrated with Cloud Build

Answer

Cloud Functions to monitor GitHub

Question 5

An organization uses Cloud Storage to store backup files. They want to automatically delete files older than 90 days, and after deletion, move remaining files to Nearline storage if not accessed for 30 days. Which Cloud Storage feature should they configure?

Accepted Answer

Object Lifecycle Management. Object Lifecycle Management (D) is the correct feature because it allows you to define rules to automatically transition objects to colder storage classes (such as Nearline) after a specified period of inactivity and to delete objects after a set age. In this scenario, a lifecycle rule can be configured to delete objects older than 90 days and, for the remaining objects, move them to Nearline storage if they have not been accessed for 30 days. This fully automates the required data management without manual intervention.

Answer

Object Versioning

Answer

Retention Policies

Answer

Bucket Lock

Question 6

You are designing a system to serve predictions from a large language model (LLM) with a latency SLO of 500ms. The model does not fit on a single GPU and requires model parallelism. You are considering using Vertex AI Endpoints with a custom container. What additional setup is required to achieve the latency target?

Accepted Answer

Use a machine type with multiple GPUs and configure the container to use tensor parallelism.. The model does not fit on a single GPU and requires model parallelism. Using a machine type with multiple GPUs and configuring the container to use tensor parallelism allows the model to be split across GPUs within a single instance, enabling efficient parallel computation to meet the 500ms latency SLO. Tensor parallelism distributes individual tensor operations across GPUs, reducing communication overhead compared to pipeline parallelism and is a standard approach for large models on multi-GPU instances in Vertex AI.

Answer

Compile the model using TensorFlow XLA to optimize for single GPU execution.

Answer

Deploy the model across multiple endpoints and use a load balancer to send requests to different parts of the model.

Answer

Use Vertex AI Prediction as a service for LLMs, which automatically handles hardware selection.

Question 7

Which THREE steps are essential for implementing a continuous training pipeline with Vertex AI?

Accepted Answer

If the new model passes evaluation, deploy it to a production endpoint.. A continuous training pipeline aims to automate model updates. After a new model is trained and evaluated, deploying it to a production endpoint (e.g., using Vertex AI Endpoints) is the essential final step to serve predictions from the improved model. This completes the automation loop without manual intervention, assuming the evaluation passes predefined thresholds.

Answer

Manually approve each new model version before deployment.

Answer

Deploy the original model once and set it to auto-update.

Question 8

You are building a BigQuery table that contains nested and repeated fields (e.g., order with line items). You need to write a query that counts the number of line items per order. Which TWO SQL functions/techniques can you use?

Accepted Answer

UNNEST with COUNT. UNNEST flattens the repeated line items array into individual rows, allowing COUNT to aggregate the number of line items per order. Option D is correct because ARRAY_LENGTH directly returns the number of elements in the repeated field array, which corresponds to the line item count.

Answer

Window function ROW_NUMBER

Answer

STRUCT with aggregation

Answer

SELECT * EXCEPT

Question 9

Your company uses Kafka for event streaming. You want to run Kafka on Google Cloud with the ability to auto-scale clusters and use managed infrastructure. Which service should you choose?

Accepted Answer

Dataproc. Dataproc is the correct choice because it is a managed Spark and Hadoop service on Google Cloud that supports running Kafka clusters via initialization actions. It allows auto-scaling of worker nodes and integrates with GCP storage and networking, providing the managed infrastructure required for Kafka event streaming. Note that Confluent Cloud is a third-party managed Kafka service, not a GCP-native service, and Cloud Pub/Sub is a messaging service, not a Kafka replacement. Cloud Dataflow is for data processing pipelines, not for running Kafka itself.

Answer

Cloud Pub/Sub

Answer

Confluent Cloud on GCP

Answer

Cloud Dataflow

Question 10

A healthcare company deploys a model for diagnosing medical images on Vertex AI using a custom container with a TensorFlow model. The model uses a mixture of GPUs (NVIDIA T4) and CPUs. After deployment, you notice that prediction latency is highly variable: sometimes under 100ms, sometimes over 10 seconds. Investigation shows that the variability correlates with the number of concurrent requests. The endpoint has a min replicas of 1 and max replicas of 3, with target CPU utilization set to 80%. You also observe that GPU utilization remains low (<20%) even during high load. What is the most likely cause of the latency variability? A) The model is not fully utilizing GPUs due to inefficient data loading from CPU. B) The autoscaling metric (CPU utilization) is not appropriate for a GPU-bound workload; the endpoint does not scale based on GPU utilization. C) The GPU machine type is too small for the model. D) The container is not configured to use the GPU correctly.

Accepted Answer

The autoscaling metric (CPU utilization) is not appropriate for a GPU-bound workload; the endpoint does not scale based on GPU utilization.. Option A. Vertex AI's default autoscaling metric is CPU utilization. However, GPU-bound workloads often have low CPU utilization because most computation happens on the GPU. As a result, the autoscaling does not trigger to add more replicas when load increases, causing a single replica to be overwhelmed and latency to spike. This explains the observed variability: CPU utilization stays low even under high load, so autoscaling does not add replicas, leading to high latency. Options B, C, and D are less likely: B (inefficient data loading) could contribute but is not the primary cause; C (GPU not configured) would typically cause errors; D (GPU too small) would cause consistently high latency rather than variability.

Answer

The model is not fully utilizing GPUs due to inefficient data loading from CPU.

Answer

The container is not configured to use the GPU correctly.

Answer

The GPU machine type is too small for the model.

Question 11

An e-commerce company uses BigQuery to analyze customer behavior. They need to compute the number of distinct customers per day, approximate quantiles of purchase amounts, and assign a row number per customer partition by date. Which BigQuery SQL functions should they use? (Choose THREE)

Accepted Answer

APPROX_COUNT_DISTINCT(customer_id). APPROX_COUNT_DISTINCT for approximate distinct counts, APPROX_QUANTILES for approximate quantiles, ROW_NUMBER for row numbering within partitions.

Answer

NTILE(4) OVER (ORDER BY purchase_amount)

Answer

COUNT(DISTINCT customer_id)

Question 12

A company uses Cloud Datastream to replicate data from a MySQL database to BigQuery in near real-time. Which TWO BigQuery features are automatically used by Datastream for optimal performance and consistency? (Choose TWO.)

Accepted Answer

BigQuery Storage Write API. Datastream uses the BigQuery Storage Write API (option D) to stream change data capture (CDC) events into BigQuery with exactly-once semantics and high throughput. It also automatically creates a materialized view (option E) that merges the change stream into the final table, ensuring consistent, near real-time replication without manual merge logic.

Answer

BigQuery Data Transfer Service

Answer

BigQuery legacy streaming inserts

Answer

A Dataflow pipeline to transform the data

Question 13

A company uses BigQuery for analytics and needs to ensure that certain columns containing PII are encrypted at query time so that only authorized users can decrypt. What should they use?

Accepted Answer

BigQuery AEAD encryption functions. BigQuery AEAD encryption functions allow you to encrypt sensitive columns (e.g., PII) at query time using a user-managed key, so that only authorized users who possess the key can decrypt the data. This is the correct approach because it provides column-level, application-layer encryption that is transparent to the query engine and ensures that unauthorized users see only ciphertext.

Answer

VPC Service Controls

Answer

Customer-managed encryption keys (CMEK)

Answer

Fine-grained IAM roles

Question 14

You deployed a model on Vertex AI Endpoints using a custom container. The model serves predictions but the latency is higher than expected. You suspect the container is not making full use of the CPU resources. What should you do to reduce latency?

Accepted Answer

Modify the container to use multi-threading or increase the number of workers in the prediction server (e.g., Gunicorn workers).. High latency in a CPU-based custom container often stems from underutilizing available CPU cores. By increasing the number of workers (e.g., Gunicorn workers) or enabling multi-threading, you allow the prediction server to handle multiple requests concurrently, reducing queue time and improving throughput. This directly addresses the symptom of the container not making full use of CPU resources.

Answer

Enable response caching on the endpoint.

Answer

Change the machine type to a GPU-accelerated machine.

Answer

Increase the number of nodes by adjusting autoscaling limits.

Question 15

Which TWO roles are required to allow a service account to run a Dataflow job and write results to BigQuery? (Choose two.)

Accepted Answer

roles/dataflow.worker. The roles/dataflow.worker role grants the service account the necessary permissions to execute Dataflow worker tasks, such as reading from sources and writing to sinks. Option C is correct because roles/bigquery.dataEditor allows the service account to insert rows into BigQuery tables, which is required for the Dataflow job to write results.

Answer

roles/pubsub.subscriber

Answer

roles/storage.objectAdmin

Answer

roles/dataflow.admin