PDE Timed Practice Test 5 — 10 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.