PDE Practice Test 13 — 15 Questions

Question 1

A team deploys a new version of a Cloud Function. After deployment, error rates increase significantly. What is the most efficient way to diagnose the cause?

Accepted Answer

Check Cloud Logging for error stacks and exceptions.. Cloud Logging automatically captures error stacks and exceptions from Cloud Functions without requiring code changes. Checking these logs is the most efficient first step because it provides immediate visibility into the root cause of errors, such as unhandled exceptions, timeouts, or dependency failures, without incurring additional deployment overhead.

Answer

Deploy a debug version with additional logging.

Answer

Increase the function timeout and retry settings.

Answer

Immediately rollback to the previous version.

Question 2

A data engineer needs to transfer 500 TB of on-premises data to Google Cloud Storage. The data is stored on NAS devices and the network bandwidth is limited to 100 Mbps. What is the most cost-effective and timely transfer method?

Accepted Answer

Use Transfer Appliance. Transfer Appliance is Google's physical data transfer service that ships a rackable appliance to the customer site, where data is copied locally and the appliance is shipped back to Google for upload to Cloud Storage. For 500 TB over a 100 Mbps link, the network transfer time would be roughly 1.5 years, making online transfer impractical. Transfer Appliance is the most cost-effective and timely option for this volume and bandwidth.

Answer

Use Storage Transfer Service over the internet

Answer

Use a VPN connection and rsync

Answer

Use gsutil cp in parallel

Question 3

A developer wants to create a BigQuery table that automatically expires data older than 30 days to reduce storage costs. Which table design feature should be used?

Accepted Answer

Partitioned table with partition expiration. BigQuery partitioned tables support partition expiration, which automatically deletes partitions older than a specified number of days. Setting a partition expiration of 30 days on a time-partitioned table will drop data older than 30 days, reducing storage costs. This is the native, cost-effective way to enforce data retention.

Answer

Authorized view

Answer

Clustered table

Answer

Materialized view

Question 4

A company deploys a machine learning model on Vertex AI for online predictions. The model experiences intermittent spikes in traffic, causing latency increases. Which strategy should the company use to ensure consistent low latency during traffic spikes?

Accepted Answer

Enable autoscaling on the Vertex AI endpoint with appropriate min and max nodes.. Vertex AI endpoints support autoscaling, which dynamically adjusts the number of prediction nodes based on incoming traffic. By setting appropriate min and max nodes, the endpoint can scale up during traffic spikes to maintain low latency and scale down during low traffic to reduce costs. This ensures consistent performance without manual intervention.

Answer

Manually scale the deployed model to a larger machine type during peak hours.

Answer

Reduce the number of prediction nodes to minimize overhead.

Answer

Switch to batch prediction to handle all requests asynchronously.

Question 5

You need to process a large Spark ML training job on a Dataproc cluster. The job is fault-tolerant and can handle occasional node failures. To reduce costs, which type of worker nodes should you use?

Accepted Answer

Preemptible worker nodes. Preemptible worker nodes in Dataproc are significantly cheaper than standard VMs but can be reclaimed by Compute Engine at any time. Since the Spark ML job is fault-tolerant and can handle occasional node failures, preemptible workers are the ideal cost-saving choice. Dataproc automatically replaces preempted workers, and Spark's lineage-based recomputation allows the job to continue without manual intervention.

Answer

Standard worker nodes

Answer

High-memory worker nodes

Answer

Sole-tenant nodes

Question 6

You want to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook environment. Which service should you use?

Accepted Answer

Vertex AI Workbench. Vertex AI Workbench is Google Cloud's managed Jupyter notebook environment integrated with Vertex AI, allowing you to develop and train custom TensorFlow models directly. It provides pre-built containers, GPU support, and seamless integration with Vertex AI Training and Pipelines.

Answer

Cloud Datalab

Answer

Vertex AI Training

Answer

AI Platform Notebooks

Question 7

A data engineer needs to monitor model performance over time for drift detection. What tool is specifically designed for this?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is specifically designed to detect prediction drift and feature skew in deployed machine learning models. It continuously analyzes serving data against training data distributions and alerts when statistical metrics (e.g., Jensen-Shannon divergence, L-infinity distance) exceed configured thresholds, making it the correct tool for drift detection in the context of operationalizing ML models.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

BigQuery ML

Question 8

A data team uses Cloud Dataproc to run nightly Spark jobs. The job volume has increased, and the cluster is often underutilized during the day. They want to reduce costs while ensuring jobs can scale when needed. Which strategy should they adopt?

Accepted Answer

Use a cluster with a small number of primary workers and a large pool of preemptible workers, and enable autoscaling.. It combines a small number of primary (non-preemptible) workers for reliability with a large pool of preemptible workers for cost-effective scaling, and enables autoscaling to dynamically adjust the cluster size based on workload. This minimizes cost during idle periods (preemptible instances are ~80% cheaper) while ensuring jobs can scale up quickly when needed, as autoscaling adds preemptible workers automatically. Preemptible workers are ideal for fault-tolerant Spark jobs that can handle node preemptions.

Answer

Use preemptible workers for both primary and secondary nodes to minimize cost.

Answer

Manually scale the cluster up before nightly jobs and down after.

Answer

Use custom machine types with local SSDs for primary workers to improve I/O.

Question 9

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 10

Which BigQuery feature allows you to read data directly from Cloud Storage without loading it into BigQuery storage?

Accepted Answer

External tables. External tables in BigQuery allow you to query data directly from Cloud Storage (e.g., CSV, JSON, Avro, Parquet) without loading it into BigQuery's native storage. This is achieved by creating a table that references the external data source, enabling federated queries and on-demand access. The data remains in Cloud Storage, and BigQuery reads it at query time, which is ideal for one-time analysis or when data is frequently updated externally.

Answer

BI Engine

Answer

Federated queries

Answer

Authorized views

Question 11

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 12

You are designing a Dataflow pipeline that needs to exactly-once process events from Pub/Sub and write to BigQuery using the Storage Write API. The pipeline may restart and could reprocess some messages. What setting ensures exactly-once semantics for the output?

Accepted Answer

Use the Storage Write API in committed mode and enable exactly-once semantic in Dataflow. The BigQuery Storage Write API in committed mode, combined with Dataflow's exactly-once processing, provides exactly-once semantics for writes to BigQuery. Committed mode uses a stream-based protocol where records are written and committed atomically, and Dataflow's exactly-once mode ensures that on pipeline restart, records are not duplicated. This is the recommended configuration for exactly-once streaming into BigQuery.

Answer

Use the legacy streaming inserts with insertId for deduplication

Answer

Use at-least-once delivery on Pub/Sub and idempotent writes to BigQuery

Answer

Use the Storage Write API in buffered mode with deduplication logic

Question 13

A startup is building a real-time dashboard that shows aggregated metrics from social media feeds. They expect up to 10,000 events per second. The data must be near-real-time (< 30 seconds latency) and stored in BigQuery for historical analysis. They have limited experience managing infrastructure. The CTO suggests using Apache Kafka on Compute Engine for ingestion. However, the data engineer recommends a fully managed solution. Which approach should the team adopt?

Accepted Answer

Use Cloud Pub/Sub for ingestion and Cloud Dataflow for streaming into BigQuery. Cloud Pub/Sub provides a fully managed, scalable ingestion service that can handle 10,000+ events per second without infrastructure management, and Cloud Dataflow offers exactly-once, auto-scaling streaming into BigQuery with sub-30-second latency. This combination meets the near-real-time requirement while eliminating operational overhead, aligning with the data engineer's recommendation for a fully managed solution.

Answer

Use Cloud Functions to ingest events directly into BigQuery

Answer

Use Apache Kafka on Compute Engine for ingestion, then use Dataflow to write to BigQuery

Answer

Use App Engine to receive events and write to BigQuery

Question 14

A company processes large volumes of GPS sensor data stored in Cloud Storage. Each hour, they run an Apache Spark job that aggregates the data by geohash region. The job must be cost-effective and scale automatically. Currently, they are using a Dataproc cluster with preemptible workers. Which improvement would best reduce costs while maintaining performance?

Accepted Answer

Use Dataproc Serverless Spark. Dataproc Serverless Spark (Option D) eliminates the need to manage a cluster, automatically scaling resources to match job demand and charging only for the resources consumed during execution. This removes the overhead of preemptible worker management and idle cluster costs, directly reducing expenses while maintaining performance for the hourly aggregation job.

Answer

Use a larger Dataproc cluster with standard workers

Answer

Migrate the job to BigQuery scheduled queries

Answer

Switch to Dataflow batch pipeline with Apache Beam

Question 15

Which Google Cloud service provides a visual interface for building ETL pipelines using a drag-and-drop design and includes pre-built transforms from a marketplace?

Accepted Answer

Cloud Data Fusion. Cloud Data Fusion is Google Cloud's fully managed, code-free ETL/ELT service built on the open-source CDAP project, offering a graphical drag-and-drop pipeline designer and a marketplace of pre-built plugins and transformations. It lets data engineers build batch and streaming pipelines visually and deploy them to ephemeral Dataproc clusters. This matches the requirement for a visual interface with marketplace transforms.

Answer

Dataproc

Answer

Dataprep

Answer

BigQuery