PDE Practice Exam 3 — 20 Questions

Question 1

A team uses Vertex AI AutoML Tables to train a model. They need to deploy the model for real-time predictions with high availability. Which deployment configuration should they use?

Accepted Answer

Deploy to a Vertex AI Endpoint with multiple replicas and auto-scaling. For real-time predictions with high availability, you need a deployment that can handle traffic spikes and failover. Deploying to a Vertex AI Endpoint with multiple replicas and auto-scaling ensures that the model is served from multiple instances, providing redundancy and the ability to scale up or down based on demand. This configuration meets the high-availability requirement by distributing load and automatically recovering from instance failures.

Answer

Export as a Cloud Function

Answer

Deploy to a Vertex AI Endpoint with 1 replica

Answer

Use a Vertex AI Batch Prediction job

Question 2

Drag and drop the steps to create a Cloud Function triggered by Cloud Storage events into the correct order.

Question 3

A company runs a batch processing job on Dataproc that uses Apache Spark to process 500 GB of data daily. The job completes successfully but takes 4 hours. The team wants to reduce the runtime to under 2 hours without increasing cost. What should they do?

Accepted Answer

Use preemptible VMs for worker nodes and increase the number of workers.. Preemptible VMs cost significantly less than standard VMs (about 60-80% discount). By using preemptible VMs for worker nodes, you can increase the number of workers (and thus parallelism) without increasing cost. This directly reduces runtime by distributing the 500 GB workload across more executors, while the cost savings from preemptible VMs offset the additional nodes.

Answer

Increase the master node's machine type to n2-standard-8.

Answer

Increase the machine type of worker nodes to n2-highmem-8.

Answer

Migrate the job to Dataflow with autoscaling enabled.

Question 4

A Dataflow pipeline reads log files from Cloud Storage, parses them into LogEvent objects, and writes to BigQuery. The pipeline fails with the above errors. What is the most likely cause?

Accepted Answer

The LogEvent class does not have a no-argument constructor.. Apache Beam's SDK requires that custom types used as PCollection elements (like LogEvent) have a no-argument constructor so that the framework can deserialize objects during distributed processing, especially when using the Dataflow runner. Without it, the pipeline fails at runtime with a serialization error because Beam's default coder (e.g., SerializableCoder) cannot reconstruct the object.

Answer

The pipeline is missing required import statements for LogEvent.

Answer

The BigQuery table schema does not match the LogEvent fields.

Answer

The log files are not in the expected format, causing parsing failures.

Question 5

Which TWO statements are correct about designing a data pipeline using Cloud Dataflow for processing unbounded data?

Accepted Answer

Watermarks are used to measure the progress of event time.. Watermarks in Cloud Dataflow measure the progress of event time, indicating when all data up to a certain timestamp is expected to have arrived. This allows the pipeline to handle late-arriving data and determine when to close windows for unbounded data streams.

Answer

Triggers can only emit results at the end of a window.

Answer

Dataflow guarantees exactly-once processing for streaming pipelines.

Answer

Fixed windows are always based on processing time.

Question 6

A retail company processes real-time clickstream data using Cloud Pub/Sub and Dataflow. The pipeline aggregates events by user session and writes to Bigtable for low-latency queries. However, users report that session data is sometimes missing or duplicated. What is the most likely cause?

Accepted Answer

Pub/Sub provides at-least-once delivery, and Dataflow does not deduplicate by default.. D is correct because Pub/Sub offers at-least-once delivery, meaning the same message may be delivered multiple times. Dataflow does not automatically deduplicate messages unless explicitly configured (e.g., using idempotent sinks or custom deduplication logic). Without deduplication, the same session event can be processed more than once, leading to duplicate session data in Bigtable.

Answer

Session windowing is configured with too short a gap duration.

Answer

Bigtable schema design causes row key collisions.

Answer

Dataflow's default behavior discards late-arriving data.

Question 7

Which THREE considerations are important when designing a data lake on Google Cloud using Cloud Storage?

Accepted Answer

Choose the appropriate storage class based on access patterns.. Option C is correct because selecting the appropriate storage class (e.g., Standard, Nearline, Coldline, Archive) based on data access patterns directly optimizes cost and performance in Cloud Storage. For a data lake, where data may be accessed frequently initially and rarely later, matching the storage class to the access pattern avoids paying premium rates for infrequently accessed data.

Answer

Use Cloud Storage's eventual consistency model for cost savings.

Answer

Define a schema when writing data to enforce data quality.

Question 8

A company uses Cloud Dataproc to run Spark ML jobs. The jobs are memory-intensive and often fail with OutOfMemory errors. Which action would most effectively reduce memory pressure without changing the Spark code?

Accepted Answer

Increase the number of worker nodes and reduce the number of cores per worker.. Increasing the number of worker nodes while reducing the number of cores per worker reduces memory pressure by distributing the workload across more JVMs, each with a smaller heap. This lowers the per-executor memory requirement and reduces the risk of OutOfMemory errors without modifying Spark code. In Cloud Dataproc, this approach directly addresses memory contention by giving each executor fewer tasks to process concurrently.

Answer

Increase the master node's memory.

Answer

Increase the number of Spark partitions.

Answer

Use preemptible VMs for workers.

Question 9

Which THREE best practices should be followed when designing a Dataflow pipeline for real-time data processing?

Accepted Answer

Set up monitoring alerts for system lag and data freshness.. Option A is correct because monitoring alerts for system lag and data freshness are essential for maintaining operational visibility in real-time Dataflow pipelines. System lag (the time between data ingestion and processing) and data freshness (how current the processed output is) directly impact the pipeline's ability to meet latency SLAs. Without these alerts, issues like worker backpressure or Pub/Sub subscription backlog can go unnoticed, leading to stale or lost data.

Answer

Use static side inputs that are loaded once at pipeline start.

Answer

Use global windows with early triggers for low latency.

Question 10

A data engineer needs to process a large dataset (500 TB) stored in Cloud Storage using Dataproc. The processing job requires reading the entire dataset and writing results back to Cloud Storage. The job is expected to run for 6 hours. Which configuration minimizes cost?

Accepted Answer

Use a cluster with a mix of standard and preemptible VMs.. Option C is correct because preemptible VMs cost about 80% less than standard VMs, and mixing them with standard VMs provides fault tolerance for the job's 6-hour duration. Since the job reads and writes to Cloud Storage (not local HDFS), local SSDs are unnecessary, and a single-node cluster would lack the parallelism needed to process 500 TB efficiently within 6 hours. Using a mix of standard (for critical master/worker nodes) and preemptible VMs (for worker nodes) minimizes cost while ensuring job completion.

Answer

Use a single-node cluster with standard VMs.

Answer

Use a cluster with local SSDs for faster I/O.

Answer

Use a cluster with n1-highmem-32 instances and 1000 cores.

Question 11

Which TWO factors should be considered when choosing between Cloud Dataflow and Dataproc for a batch processing pipeline?

Accepted Answer

Dataflow is built for data processing patterns, while Dataproc is better for general-purpose compute.. Option B is correct because Dataflow is purpose-built for data processing patterns like batch and stream processing with unified programming models (Apache Beam), while Dataproc is optimized for general-purpose compute workloads such as running custom Spark, Hadoop, or ML jobs. Option D is correct because Dataflow provides automatic horizontal autoscaling based on pipeline throughput, whereas Dataproc requires manual cluster sizing or configuration of autoscaling policies, which are not as granular or reactive as Dataflow's.

Answer

Dataproc allows custom Docker containers, while Dataflow does not.

Answer

Dataproc supports Python, while Dataflow only supports Java.

Answer

Dataflow supports Java and Python, while Dataproc only supports Java.

Question 12

In the Vertex AI Pipeline component YAML exhibit, the component is designed to evaluate a model and produce metrics. If the threshold_accuracy is set to 0.85, what is the expected behavior of this component?

Accepted Answer

It will output the evaluation metrics, and the pipeline can use them for conditional decisions. In Vertex AI Pipelines, a component's YAML definition specifies inputs, outputs, and implementation. Setting `threshold_accuracy` to 0.85 defines a parameter that the component can use internally, but by itself it does not trigger deployment or cause failure. The component's expected behavior is to output evaluation metrics, and the pipeline can then use those metrics in conditional logic (e.g., via `Condition` or `if/else` tasks) to decide subsequent steps, such as model deployment or retraining.

Answer

It will deploy the model if the accuracy meets the threshold

Answer

It will ignore the threshold_accuracy input if not provided

Answer

It will fail if the model accuracy is below 0.85

Question 13

Which TWO actions can help reduce the latency of a Vertex AI endpoint serving a large neural network model?

Accepted Answer

Deploy the model on a machine type with GPU accelerators. Option D is correct because GPU accelerators are specifically designed to handle the parallel computations required by large neural networks, significantly reducing inference latency compared to CPU-only machines. Vertex AI endpoints with GPUs can process multiple predictions concurrently, which is critical for deep learning models where matrix operations dominate the workload.

Answer

Use a larger machine type with more CPU cores

Answer

Enable model compression with quantization

Answer

Increase the number of model versions deployed on the same endpoint

Question 14

You have deployed a TensorFlow model on Vertex AI Endpoints with autoscaling. The model receives high traffic during peak hours, but you notice that inference latency increases significantly during cold starts. Which strategy would best minimize cold-start latency without incurring unnecessary cost?

Accepted Answer

Set minNodes to a value that handles baseline traffic, and use traffic splitting to gradually shift traffic to new replicas. Setting minNodes to a value that handles baseline traffic ensures that a minimum number of replicas are always warm, eliminating cold starts for baseline requests. Traffic splitting gradually shifts new traffic to newly created replicas, allowing them to warm up before receiving full load, which minimizes latency spikes without over-provisioning resources.

Answer

Set minNodes to 0 and enable node auto-scaling

Answer

Increase maxNodes to allow more replicas during peak, and rely on Kubernetes Horizontal Pod Autoscaler

Answer

Use Cloud Functions with Cloud Run for the model inference to leverage serverless cold-start mitigation

Question 15

Which TWO actions can reduce the cost of running a Dataproc cluster for a nightly batch job?

Accepted Answer

Use preemptible VMs for worker nodes.. Preemptible VMs (Option C) are significantly cheaper than standard VMs because Compute Engine can terminate them at any time, making them ideal for fault-tolerant, stateless batch jobs like nightly data processing on Dataproc. Deleting the cluster after the job completes (Option E) eliminates ongoing compute costs for idle resources, which is a best practice for ephemeral workloads.

Answer

Increase the number of worker nodes for faster processing.

Answer

Use high-memory machine types for master node.

Answer

Attach local SSDs to all nodes.

Question 16

A company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?

Accepted Answer

Configure auto-scaling with higher min and max instances. Option A is correct because configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model architecture or inference logic, only the infrastructure capacity.

Answer

Reduce the number of input features

Answer

Switch from online to batch prediction

Answer

Use a larger machine type for the model

Question 17

A company trains a custom model using TensorFlow and wants to deploy it to Vertex AI for low-latency predictions. The model is large (2 GB). Which deployment option should they choose?

Accepted Answer

Deploy to Vertex AI Endpoint with a custom container. Option C is correct because deploying a large (2 GB) model to Vertex AI Endpoint with a custom container allows you to package the model, its dependencies, and a serving framework (e.g., TensorFlow Serving) into a Docker image. This approach supports low-latency predictions by keeping the model loaded in memory across requests, and it can scale to handle real-time inference traffic, unlike batch or serverless options that have cold-start or size limitations.

Answer

Use Vertex AI Batch Prediction job

Answer

Deploy as a Cloud Function

Answer

Deploy to Cloud Run with minimum instances

Question 18

A company wants to stream data from Cloud Pub/Sub into BigQuery with minimal latency. They have a small team and limited operational resources. Which approach is best?

Accepted Answer

Use a Dataflow pipeline with a BigQuery subscription.. Option D is correct because a Dataflow pipeline with a BigQuery subscription provides a fully managed, serverless streaming solution that directly ingests messages from Pub/Sub and writes them to BigQuery with minimal latency. Dataflow handles autoscaling, checkpointing, and exactly-once semantics, which aligns with the team's limited operational resources. The BigQuery subscription (via the Pub/Sub to BigQuery template) eliminates the need for custom code or cluster management, ensuring low-latency streaming without operational overhead.

Answer

Write a custom application on Compute Engine that polls Pub/Sub and writes to BigQuery.

Answer

Create a Dataproc cluster running a Spark Streaming job.

Answer

Create a Cloud Function that writes to BigQuery.

Question 19

A company runs a Cloud Dataflow streaming pipeline that reads from Cloud Pub/Sub, performs a fixed window of 10 seconds, joins with a slowly-changing dimension table stored in Cloud Bigtable, and writes results to BigQuery. The pipeline has been running for months but recently started exhibiting increasing latency and occasional data loss. The pipeline uses default settings with autoscaling enabled (min 2, max 20 workers). The Bigtable cluster has 3 nodes. The dimensions are updated infrequently. The latency has grown from seconds to minutes. Examining the Dataflow monitoring UI, you see that the 'System Lag' metric is increasing, and some windows are not being emitted. The CPU utilization on Bigtable nodes is below 50%. There are no errors in the logs. Which action is most likely to resolve the issue?

Accepted Answer

Set the pipeline option --maxNumWorkers to a value between 5 and 10.. The increasing system lag and unemitted windows in a streaming pipeline with autoscaling (2–20 workers) and a 3-node Bigtable cluster indicate that the pipeline is bottlenecked by the number of workers, not by Bigtable performance. With default autoscaling, Dataflow may not scale up aggressively enough to handle the sustained load, causing backlog and window expiration. Capping maxNumWorkers to 5–10 ensures sufficient parallelism without over-provisioning, allowing the pipeline to catch up and emit windows reliably.

Answer

Increase the window duration to 30 seconds to reduce the number of windows.

Answer

Redesign the pipeline to use a side input for the dimension table instead of a lookup.

Answer

Increase the number of Bigtable nodes to reduce lookup latency.

Question 20

A company has a batch ETL job that runs daily using Cloud Dataflow. The job reads from Cloud Storage, transforms data, and writes to BigQuery. Recently, the job started failing with 'Resources have been exhausted' errors. What is the most likely cause?

Accepted Answer

The project has reached its Dataflow API quota.. The 'Resources have been exhausted' error in Cloud Dataflow typically indicates that the project has reached its Dataflow API quota, such as the maximum number of concurrent jobs or API requests per minute. This is a common issue when multiple jobs run simultaneously or when the quota is set low by default. The error is distinct from resource exhaustion in the underlying compute or storage layers.

Answer

The Cloud Storage bucket has been deleted.

Answer

The input data volume has increased significantly.

Answer

The BigQuery output table schema has changed.