PDE Practice Test 8 — 10 Questions

Question 1

What is the most likely cause of this error?

Accepted Answer

Each JSONL line contains a single image tensor without a batch dimension.. The error occurs because each line in a JSONL file is expected to be a self-contained JSON object representing a single inference request. When the line contains a raw image tensor without a batch dimension, the model's serving framework (e.g., TensorFlow Serving or TorchServe) cannot perform batched inference, as it expects input tensors to have shape (batch_size, channels, height, width) or (batch_size, height, width, channels). The missing batch dimension causes a shape mismatch error during model execution.

Answer

The JSONL file is missing some required fields.

Answer

The input images have a different number of channels than expected.

Answer

The instances are in JSON format instead of JSONL.

Question 2

Which TWO statements about designing a data processing pipeline on Google Cloud are correct? (Choose 2.)

Accepted Answer

Cloud Data Fusion allows you to build and manage data pipelines visually without writing code.. Cloud Data Fusion provides a visual, no-code interface for building and managing data pipelines, enabling users to design ETL/ELT workflows through a drag-and-drop UI. It abstracts the underlying complexity of Apache Spark and Cloud Dataproc, making it suitable for users who prefer a graphical approach over writing code.

Answer

Pub/Sub guarantees message ordering across all subscribers globally.

Answer

Cloud Bigtable is ideal for data warehousing and SQL analytics.

Answer

Dataproc is the best choice for fully managed data warehousing and analytics.

Question 3

A data team uses Cloud Composer to orchestrate Airflow DAGs. They need to ensure that a downstream task runs only if at least two out of three upstream sensor tasks succeed. Which TWO configurations should they combine?

Accepted Answer

Set trigger_rule to 'none_failed_or_skipped' and use a condition.. Option A is correct because the 'none_failed_or_skipped' trigger rule triggers the downstream task when all upstream tasks have succeeded or been skipped. Combined with a condition (e.g., using a PythonOperator or BranchPythonOperator) that checks whether at least two of the three sensor tasks succeeded, this ensures the downstream task runs only when the required threshold is met. This approach leverages Airflow's built-in trigger rules and conditional logic to implement a quorum-based dependency.

Answer

Set trigger_rule to 'one_success'.

Answer

Set trigger_rule to 'all_done'.

Answer

Set trigger_rule to 'none_failed'.

Question 4

You are responsible for deploying a PyTorch model for real-time inference. The model requires GPU acceleration. You want to minimize infrastructure management overhead. Which serving option should you choose?

Accepted Answer

Deploy the model on Vertex AI Endpoints using a custom container with GPU support. Vertex AI Endpoints with a custom container and GPU support is the correct choice because it is purpose-built for serving ML models at scale, fully managed, and supports GPU acceleration for low-latency inference. It minimizes infrastructure overhead by handling auto-scaling, health checks, and model versioning, unlike the other options that lack GPU support or are designed for training rather than serving.

Answer

Deploy the model as a Cloud Function with a GPU backend

Answer

Use Cloud Run with GPU enabled

Answer

Use AI Platform Training to host the model as a prediction service

Question 5

Which THREE factors should be considered when designing a Vertex AI Pipeline for continuous training?

Accepted Answer

Cost of training and infrastructure. Cost of training and infrastructure (A) is correct because Vertex AI Pipelines incur compute costs for each pipeline run, including training, data processing, and orchestration. Continuous training amplifies these costs, so you must consider budget constraints, resource optimization (e.g., using preemptible VMs), and cost monitoring to avoid unexpected bills.

Answer

Debugging tools like Cloud Debugger

Answer

Number of model versions to keep

Question 6

You are using AI Platform Prediction (now Vertex AI) for online predictions. You notice that some requests are failing with a 503 status code. Which is the most likely cause?

Accepted Answer

The model is experiencing high traffic and the underlying nodes are still scaling up. A 503 status code in Vertex AI (formerly AI Platform Prediction) indicates that the prediction service is temporarily unavailable, most commonly due to autoscaling latency. When a model receives a sudden spike in traffic, the underlying nodes (compute instances) may still be provisioning and initializing, causing requests to be rejected until the new nodes are ready to serve. This is a transient condition that resolves once scaling completes.

Answer

The input data format does not match the model's expected schema

Answer

The project has exceeded its prediction requests quota

Answer

The service account used for prediction does not have the required permissions

Question 7

You configured a model deployment monitor on your Vertex AI endpoint as shown. What will happen when the feature 'age' has a skew of 0.4?

Accepted Answer

An alert will be sent to admin@example.com. Option A is correct because the monitoring configuration shows an alert threshold of 0.2 for the feature 'age', and a skew of 0.4 exceeds that threshold. Vertex AI Model Monitoring will trigger the configured alert action, which in this case is sending an email to admin@example.com. The alert is based on the specific feature's threshold, not on any other feature's threshold.

Answer

The endpoint will automatically roll back to a previous model version

Answer

No alert will be sent because the skew threshold is 0.2 for income

Answer

An alert will be sent only if both features exceed their thresholds

Question 8

Your MLOps pipeline uses Vertex AI Pipelines. You want to ensure that model training uses a consistent environment with specific Python package versions. Which approach best achieves this?

Accepted Answer

Build a custom container image with all dependencies and use it in the training step. Option D is correct because building a custom container image with all dependencies ensures a fully deterministic and reproducible environment for model training. Vertex AI Pipelines executes each step as a container, so by pre-installing specific Python package versions into a custom image, you eliminate any risk of version drift or network issues during package installation at runtime. This approach aligns with MLOps best practices for environment consistency and is the most reliable method when exact package versions are critical.

Answer

Include a requirements.txt file in the pipeline step and let Vertex AI install them

Answer

Use a pre-built deep learning container from Deep Learning Containers and install packages at runtime

Answer

Specify the Python version and package versions in the training job configuration

Question 9

Which THREE considerations are important when designing a batch prediction pipeline for a large dataset on Vertex AI?

Accepted Answer

Choosing the appropriate machine type (e.g., n1-standard-16) balances cost and throughput. Option C is correct because selecting the appropriate machine type, such as n1-standard-16, directly impacts the cost-performance trade-off in batch prediction. Vertex AI batch prediction jobs run on Compute Engine instances, and choosing a machine type with more vCPUs and memory can increase throughput for large datasets, but also raises cost. The key is to match the machine type to the model's computational needs and the data volume, avoiding over-provisioning while ensuring the job completes within acceptable time.

Answer

Batch prediction automatically uses GPUs if the model framework requires them

Answer

Batch prediction requires a dedicated real-time endpoint

Question 10

A company is running a Cloud Dataflow streaming pipeline that aggregates events in 1-minute windows. They notice that the watermark is lagging significantly behind real-time. What is the most likely cause?

Accepted Answer

A hot key is causing data skew.. A hot key causes data skew, which means a disproportionate amount of data is assigned to a single key. In Cloud Dataflow, this leads to a single worker processing the bulk of the events, creating a processing bottleneck. The watermark, which tracks the progress of event-time processing, cannot advance until all data for a given window is processed, so the skewed key delays watermark progression significantly behind real-time.

Answer

The window duration is too short.

Answer

The pipeline was recently updated.

Answer

The allowed lateness is set too high.

PDE Practice Test 8 — 10 Questions

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PDE Practice Test 8 — 10 Questions

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