PDE Practice Test 10 — 10 Questions

Question 1

A company is building a data lake on Cloud Storage with data from multiple sources. They need to apply schema-on-read and support ad-hoc SQL queries. Which architecture is most suitable?

Accepted Answer

Ingest to Cloud Storage, create BigQuery external tables.. BigQuery external tables allow schema-on-read by defining the schema at query time over data stored in Cloud Storage, enabling ad-hoc SQL queries without loading data into a separate system. This architecture directly supports the requirement for schema-on-read and SQL-based analysis, as BigQuery provides a serverless, scalable SQL engine.

Answer

Ingest to Cloud Spanner, query directly.

Answer

Ingest to Cloud SQL, then export to Cloud Storage for queries.

Answer

Ingest to Cloud Storage, load into Dataproc for queries.

Question 2

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.. 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 3

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 4

A company is migrating ML workflows to Vertex AI Pipelines. They want to ensure best practices for pipeline reproducibility and debugging. Which THREE actions should they take? (Choose three.)

Accepted Answer

Set a random seed for all training components. Setting a random seed for all training components ensures deterministic behavior, meaning that the same inputs will produce the same outputs across multiple runs. This is critical for debugging and reproducibility in Vertex AI Pipelines, as it eliminates stochastic variability that can mask bugs or make results irreproducible. Without a fixed seed, even identical code and data can yield different model weights or metrics, complicating root cause analysis.

Answer

Use dynamic pipeline parameters for each run

Answer

Use conditional execution based on previous component outputs

Question 5

A data pipeline uses Cloud Composer to orchestrate Dataflow and BigQuery jobs. The pipeline fails intermittently with dependency errors. Which design change can improve reliability?

Accepted Answer

Use retries with exponential backoff. Cloud Composer (Apache Airflow) tasks can fail due to transient issues like API rate limits or resource contention. Implementing retries with exponential backoff allows the DAG to automatically re-attempt failed tasks with increasing delays, reducing the impact of intermittent failures without manual intervention. This is a standard Airflow pattern for improving reliability in orchestrated pipelines.

Answer

Switch to Cloud Functions for orchestration

Answer

Increase worker count in Dataflow

Answer

Use a simpler DAG with fewer dependencies

Question 6

A data engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)

Accepted Answer

Set autoscaling policies based on YARN memory.. Autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.

Answer

Always use persistent disks for all nodes.

Answer

Store intermediate data in HDFS.

Answer

Use the largest machine types for master nodes.

Question 7

A data engineer needs to build a feature engineering pipeline using Vertex AI Pipelines. The pipeline should preprocess data, train a model, and deploy it. Which two components are required to define the pipeline? (Choose 2)

Accepted Answer

Kubeflow Pipelines SDK. Vertex AI Pipelines uses the Kubeflow Pipelines SDK (or TFX) to define components and compile them into a pipeline. The pipeline is then run on Vertex AI Pipelines.

Answer

Vertex AI Feature Store

Answer

Dataflow

Answer

Cloud Composer

Question 8

A data pipeline using Cloud Pub/Sub and Cloud Dataflow is experiencing duplicate messages. The source system publishes messages at least once. What Dataflow technique ensures exactly-once processing?

Accepted Answer

Use idempotent sinks. Idempotent sinks ensure that even if Cloud Pub/Sub delivers the same message multiple times (due to its at-least-once delivery semantics), the Dataflow pipeline can deduplicate or safely reapply the same data without causing duplicates in the output. This is achieved by designing the sink (e.g., BigQuery with insertId, Cloud Storage with unique filenames) to recognize and ignore repeated writes, effectively providing exactly-once processing semantics downstream.

Answer

Use GlobalWindows

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Set watermark threshold

Answer

Enable streaming engine

Question 9

A data science team deploys a TensorFlow image classification model to Vertex AI Prediction. The model performs well in offline evaluation but shows a 15% drop in accuracy in production. The production data distribution has shifted compared to the training data. The team needs to continuously monitor and retrain the model. Which solution is most appropriate for detecting drift and triggering retraining?

Accepted Answer

Enable Vertex AI Model Monitoring for feature drift; configure alerts to trigger a Vertex AI Pipelines retraining run.. Vertex AI Model Monitoring is purpose-built for detecting feature drift in production ML models by comparing live inference data against a baseline distribution. When drift is detected, it can directly trigger a Vertex AI Pipelines retraining run, creating an automated, end-to-end MLOps loop that addresses the production accuracy drop without manual intervention.

Answer

Export production predictions to Cloud Logging, then use Log Analytics to compare distributions.

Answer

Store predictions in BigQuery and run scheduled SQL queries to detect drift; trigger retraining via Cloud Functions.

Answer

Use Cloud Monitoring to track prediction latency and error rates; manually retrain when errors increase.

Question 10

A company is deploying a large-scale streaming application on Google Kubernetes Engine. They need to ensure the application can handle sudden traffic spikes without dropping data. Which architectural pattern is most appropriate?

Accepted Answer

Use a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog.. Pub/Sub provides a durable, scalable, and asynchronous message buffer that decouples the producer from the consumer. By autoscaling consumer pods based on the Pub/Sub subscription backlog (e.g., using the 'pubsub.googleapis.com/subscription/num_undelivered_messages' custom metric with Horizontal Pod Autoscaler), the application can elastically handle traffic spikes without data loss, as messages are persisted until acknowledged.

Answer

Implement custom retry logic with exponential backoff in the application.

Answer

Use Cloud SQL as a temporary buffer and process from there.

Answer

Pre-provision 3x the expected peak capacity to handle spikes.