PDE Practice Questions

Question 1

You are using Cloud Workflows to orchestrate a series of API calls. You need to handle errors and retries. Which THREE features of Cloud Workflows can you use? (Choose THREE.)

Accepted Answer

Use try/except blocks to catch and handle errors.. Cloud Workflows supports steps with retry policies, try/except blocks for error handling, and conditional (if-else) logic for branching. It does not have built-in built-in step-level logging (logging is done via Cloud Logging), and there is no built-in load balancer integration.

Answer

Integrate with Cloud Load Balancing for high availability.

Answer

Enable automatic logging for each step.

Question 2

A manufacturing company wants to detect anomalies in sensor data from thousands of IoT devices in real time. The data is streaming into Pub/Sub. The best solution should use a machine learning model served from AI Platform that scores sensor readings aggregated over 5-minute windows. Which pipeline design meets these requirements?

Accepted Answer

Use Cloud Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window. Cloud Dataflow's sliding windows natively handle the 5-minute aggregation requirement for streaming data, and its ability to call external services via a DoFn allows integration with AI Platform Prediction for real-time model scoring. This design aligns with the need for low-latency, scalable processing of Pub/Sub streams without managing infrastructure.

Answer

Use Cloud Dataproc with Spark Streaming to aggregate data, and use a Spark ML model embedded in the pipeline

Answer

Use BigQuery streaming inserts and run scheduled queries that call the ML model

Answer

Use Cloud Functions triggered by Pub/Sub to process each sensor reading individually

Question 3

You have a BigQuery table with sales data and want to pivot product categories into columns. Which SQL clause should you use?

Accepted Answer

PIVOT. PIVOT is the standard SQL clause to rotate rows into columns. UNPIVOT rotates columns to rows. ARRAY_AGG and STRUCT are not used for pivoting.

Answer

UNPIVOT

Answer

ARRAY_AGG with CROSS JOIN

Answer

STRUCT

Question 4

Which TWO are best practices for monitoring a deployed machine learning model in production on Vertex AI?

Accepted Answer

Enable Vertex AI Model Monitoring to track feature drift and skew. Vertex AI Model Monitoring automatically tracks feature drift and skew by comparing the serving data distribution against the training data distribution using statistical tests like the Kolmogorov-Smirnov test. This is a best practice for detecting data quality issues that can degrade model performance in production.

Answer

Set up a weekly retraining pipeline triggered by calendar schedule

Answer

Monitor the training job duration to detect anomalies

Answer

Monitor the model's file size to ensure it hasn't changed

Question 5

You are using Vertex AI Feature Store to serve features for online predictions. Your model requires features from multiple sources with low latency (<10ms). Which type of serving should you use?

Accepted Answer

Online serving with Bigtable. Online serving (with Bigtable as backing store) provides low-latency feature retrieval. Offline serving is for batch predictions. Feature Store supports both; online is for real-time.

Answer

Online serving with Cloud SQL

Answer

Offline serving with BigQuery

Answer

Offline serving with Cloud Storage

Question 6

Your team is using Vertex AI Pipelines to orchestrate a model retraining workflow. The pipeline includes a data validation step, a training step, and a model evaluation step. You want to ensure that if the evaluation step fails due to low model performance, the pipeline stops and does not deploy the model. Which approach should you use?

Accepted Answer

Use a Conditional in the pipeline to check evaluation metrics and only run the deployment step if metrics pass thresholds. Vertex AI Pipelines supports conditional execution via the `Condition` component, which allows you to evaluate model performance metrics (e.g., accuracy, RMSE) and gate subsequent steps. By placing the deployment step inside a conditional branch that only executes when evaluation metrics meet predefined thresholds, the pipeline automatically stops and avoids deploying a poor-performing model. This approach aligns with MLOps best practices for automated gating in production pipelines.

Answer

Run the evaluation step after deployment and roll back if performance is low

Answer

Configure the evaluation step to retry up to 3 times on failure

Answer

Create a separate pipeline for deployment and trigger it manually after review

Question 7

Your company stores sensitive customer data in Cloud Storage. You need to inspect the data for personally identifiable information (PII) and de-identify it before sharing with a third party. Which Google Cloud service should you use?

Accepted Answer

Cloud Data Loss Prevention (DLP). Cloud Data Loss Prevention (DLP) is the correct service because it is specifically designed to inspect, classify, and de-identify sensitive data such as PII in Cloud Storage. It provides built-in infoType detectors for over 150 types of PII and supports de-identification techniques like masking, tokenization, and encryption. This directly matches the requirement to inspect and de-identify data before sharing with a third party.

Answer

Security Command Center

Answer

Dataplex

Answer

Cloud KMS

Question 8

A company runs a batch data processing workload using Dataproc clusters that are auto-scaled based on YARN memory utilization. During peak times, jobs take much longer than expected. Analysis shows the cluster is not scaling up despite high YARN memory utilization. What is the most likely cause?

Accepted Answer

The cluster is using primary workers only; auto-scaling only adds secondary workers. Dataproc clusters have two types of workers: primary workers (which run both HDFS and compute) and secondary workers (compute-only). The autoscaler can only add or remove secondary workers; it cannot scale primary workers. If the cluster uses only primary workers, the autoscaler has no secondary workers to add, so it cannot scale up even under high YARN memory utilization. This explains why the cluster remains static during peak times.

Answer

Spark dynamic allocation is disabled, preventing executors from using added workers

Answer

The cluster autoscaler is misconfigured to scale based on CPU, not memory

Answer

The autoscaler is set to scale down secondary workers, not up

Question 9

A model deployed on Vertex AI Endpoint is making predictions with high accuracy but the business team suspects bias against a certain demographic group. You need to analyze the model's predictions for fairness. What is the most effective approach?

Accepted Answer

Use Vertex AI Explainable AI to generate feature attributions for each prediction and analyze whether the demographic feature has disproportionate impact.. Vertex AI Explainable AI provides per-instance feature attributions, which allow you to examine how the model uses each feature—including sensitive demographic attributes—to arrive at a prediction. By analyzing these attributions across demographic groups, you can detect whether the model disproportionately relies on the demographic feature, indicating potential bias. This approach is more granular than aggregate metrics and directly addresses the business team's concern about bias in individual predictions.

Answer

Compute overall fairness metrics by comparing prediction rates across demographic groups.

Answer

Collect more data for the under-represented group and retrain the model.

Answer

Use Vertex AI Model Monitoring to check for training-serving skew on the demographic feature.

Question 10

You have a Dataflow pipeline that processes streaming data with high throughput. You notice that the pipeline is experiencing high latency and the workers are underutilized. Which Dataflow feature can automatically optimize resource allocation?

Accepted Answer

Dataflow Prime. Dataflow Prime (also known as right-fitting) provides vertical autoscaling and resource optimization based on actual usage. Horizontal autoscaling is standard but may not address underutilization. Streaming engine is for scaling the streaming writes but not worker tuning. Flex templates are for deployment, not runtime optimization.

Answer

Flex Templates

Answer

Horizontal autoscaling

Answer

Streaming Engine

Question 11

A company runs large batch prediction jobs on Vertex AI every day. They want to minimize costs while ensuring the jobs complete within a 4-hour window. The model requires significant memory. What is the most cost-effective approach?

Accepted Answer

Use preemptible VMs with a machine type that meets memory requirements. Preemptible VMs (now called Spot VMs) are significantly cheaper than standard VMs (up to 60-80% discount) and are ideal for fault-tolerant batch prediction jobs that can handle interruptions. Since the job has a 4-hour window and the model requires significant memory, using preemptible VMs with a machine type that meets the memory requirements minimizes cost while allowing the job to complete if restarted within the time limit.

Answer

Use Cloud TPUs to accelerate predictions

Answer

Use a smaller machine type (e.g., n1-standard-4) to reduce cost

Answer

Use standard VMs and reduce parallelization

Question 12

A data scientist has iterated on a model and produced a new version. The organization requires the ability to roll back to the previous version quickly if the new version performs poorly in production. Which approach should be used?

Accepted Answer

Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed.. Vertex AI Model Registry allows you to deploy multiple model versions and use endpoint traffic splitting to gradually shift traffic or instantly route 100% to a specific version. This enables immediate rollback by setting the traffic split to 100% for the previous model version without redeploying or changing infrastructure.

Answer

Store each model version in a separate Cloud Storage bucket.

Answer

Keep the previous model in a container image and redeploy via Cloud Run.

Answer

Use Cloud Source Repositories to tag model versions.

Question 13

A financial company needs to process batch trades data daily and ensure that if a transformation step fails, the entire daily run is retried from the beginning. Which design pattern is appropriate?

Accepted Answer

Use an orchestrator like Cloud Composer with retry logic. The requirement states that if any transformation step fails, the entire daily run must be retried from the beginning. An orchestrator like Cloud Composer (Apache Airflow) provides native DAG-level retry logic that can be configured to restart the entire workflow on failure, ensuring atomicity of the batch run. This pattern is essential for maintaining data consistency when partial processing cannot be tolerated.

Answer

Use idempotent writes with checkpointing

Answer

Retry the failed step only

Answer

Use a transactional staging area

Question 14

A data engineer needs to design a Bigtable row key for a time-series IoT application where each device sends data every second. The query pattern is to retrieve all data for a specific device over a time range. Which row key design minimizes hotspots?

Accepted Answer

hash(device_id)#timestamp (e.g., a3f2#2024-03-15-10:30:00). To avoid hotspots (where all writes hit a single tablet server), the row key should start with a hash of the device ID to distribute writes across the cluster, then append the timestamp for range scans.

Answer

device_id#timestamp (e.g., device123#2024-03-15-10:30:00)

Answer

timestamp#device_id (e.g., 2024-03-15-10:30:00#device123)

Answer

device_type#device_id#timestamp

Question 15

A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into BigQuery. Which BigQuery feature should they use?

Accepted Answer

BigQuery external tables. BigQuery external tables allow querying data stored in Cloud Storage (including Parquet files) directly without loading it into BigQuery storage. This feature uses a federated query engine that reads the data on the fly, supporting formats like Parquet, Avro, ORC, CSV, and JSON. Option C is correct because it directly addresses the requirement to query Parquet files in Cloud Storage without ingestion.

Answer

BigQuery Omni

Answer

BigQuery ML

Answer

BigQuery BI Engine

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