PDE Practice Test 46 — 15 Questions

Question 1

Your team uses dbt to transform data in BigQuery. You need to schedule dbt runs to refresh materialized tables and views every hour. The transformations include both full refreshes and incremental models. What is the most efficient way to orchestrate these dbt runs on Google Cloud?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use Cloud Composer (Airflow) to schedule and run dbt commands.. Cloud Composer (Airflow) is the recommended orchestration tool for complex workflows like dbt runs, supporting dependencies, retries, and scheduling. Cloud Scheduler alone cannot run dbt directly; it can trigger a Cloud Function to run dbt, but that is less maintainable. Cloud Build is CI/CD, not scheduling. Using a cron job on Compute Engine is possible but not managed.

Answer

Use Cloud Build with a trigger to run dbt every hour.

Answer

Use Cloud Scheduler to trigger a Cloud Function that runs dbt.

Answer

Set up a cron job on a Compute Engine instance to run dbt.

Question 2

You are loading 10 GB of daily CSV files from a GCS bucket into a BigQuery table. The files contain some malformed rows that you want to skip. Which BigQuery load configuration should you use?

Accepted Answer

Use the 'max_bad_records' option set to a value like 10.. The 'max_bad_records' option allows you to specify the maximum number of bad records that BigQuery can ignore during a load job. Setting it to a value like 10 means that up to 10 malformed rows will be skipped; if more than 10 bad records are encountered, the job fails. This is the correct configuration to skip a limited number of malformed rows while ensuring data quality.

Answer

Use the 'skip_leading_rows' option.

Answer

Use the 'ignore_unknown_values' option.

Answer

Use the 'allow_jagged_rows' option.

Question 3

A company uses Cloud Pub/Sub to ingest events from multiple sources. They need to guarantee that each event is processed exactly once by downstream consumers. However, Pub/Sub guarantees at-least-once delivery. Which additional steps should they implement to achieve exactly-once processing?

Accepted Answer

Store each message's unique ID in a database and ignore duplicates.. Pub/Sub's at-least-once delivery means a message can be redelivered if the ack isn't received in time or if the subscriber crashes after processing but before acking. To achieve exactly-once processing, the consumer must implement idempotency by tracking processed message IDs and discarding duplicates. Storing each message's unique ID in a database and ignoring duplicates ensures that even if the same message is delivered multiple times, it is only processed once. This is the standard pattern for exactly-once semantics on top of at-least-once infrastructure.

Answer

Set the subscription's acknowledgment deadline to 0.

Answer

Enable message deduplication on the subscription.

Answer

Use a dead letter topic to capture duplicates.

Question 4

You have deployed a classification model on Vertex AI Endpoints. The model's training data had a balanced class distribution, but over time, the production data has shifted such that one class appears 90% of the time. The model's overall accuracy remains high, but the recall for the minority class has dropped significantly. What is the best approach to detect and address this issue?

Accepted Answer

Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data. Vertex AI Model Monitoring is specifically designed to detect skew and drift between training and serving data. In this scenario, the production data has shifted to 90% of one class, which is a clear case of data drift. By setting up monitoring, you can be alerted to this drift and then retrain the model using a sliding window of recent data, which adapts to the new distribution without requiring full retraining on the entire historical dataset. This approach directly addresses the root cause—the shift in class distribution—rather than just treating symptoms.

Answer

Retrain the model daily on the entire historical dataset

Answer

Increase the number of replicas on the endpoint to reduce latency

Answer

Adjust the decision threshold to improve minority class recall

Question 5

A data engineer needs to transfer 500 TB of archival data from an on-premises NAS to Cloud Storage. The on-premises network has limited bandwidth (100 Mbps). Which transfer method should they recommend?

Accepted Answer

Transfer Appliance. The Transfer Appliance is a physical device designed for large-scale data transfers (up to petabytes) when network bandwidth is insufficient. With 500 TB of data and only 100 Mbps bandwidth, the theoretical transfer time would be over 500 days, making any online transfer method impractical. The Transfer Appliance bypasses network constraints entirely by shipping the data physically to Google Cloud.

Answer

Storage Transfer Service for on-premises

Answer

gsutil rsync

Answer

Dataflow pipeline reading from NAS

Question 6

A marketing team needs to run ad-hoc SQL queries on terabytes of clickstream data stored in Parquet files in Cloud Storage. They want a serverless solution with no cluster management and the ability to query external data without loading. Which service should they use?

Accepted Answer

BigQuery with external tables. BigQuery with external tables allows querying data stored in Cloud Storage (including Parquet files) without loading it into BigQuery storage, providing a serverless, fully managed solution with no cluster management. This matches the requirement for ad-hoc SQL queries on terabytes of clickstream data in Parquet format, as BigQuery automatically scales compute and storage.

Answer

Cloud SQL

Answer

AlloyDB

Answer

Dataproc with Spark SQL

Question 7

A company has a model that requires GPU for inference and has strict latency requirements. They deployed on Vertex AI Endpoint with autoscaling but observe cold start latency when scaling up. What is the best solution?

Accepted Answer

Set a higher min_replica_count to keep instances warm. Setting a higher min_replica_count ensures that a baseline number of GPU instances are always running and ready to serve inference requests, eliminating cold start latency because new instances do not need to be provisioned and loaded from scratch when traffic spikes. This directly addresses the autoscaling-induced cold start issue by maintaining a warm pool of replicas.

Answer

Pre-compile the model with TensorRT

Answer

Use a larger GPU instance

Answer

Switch to batch prediction

Question 8

An organization wants to enforce that data in a Cloud Storage bucket cannot be deleted or overwritten for 7 years due to regulatory compliance. Which Cloud Storage feature should they use?

Accepted Answer

Retention Policy with Bucket Lock. Retention Policy with a retention period ensures objects cannot be deleted or overwritten during that period. Bucket Lock makes the policy permanent. Object holds are per-object. Lifecycle management automates transitions/deletions, opposite of retention.

Answer

IAM conditions

Answer

Object Lifecycle Management

Answer

Object holds

Question 9

A company needs to process streaming sensor data from millions of devices with sub-second latency, apply transformations, and write results to BigQuery for real-time dashboards. The data volume varies, and they want to avoid managing servers. Which service should they use?

Accepted Answer

Dataflow. Dataflow is Google Cloud's fully managed, serverless data processing service built on Apache Beam, designed for both batch and streaming pipelines with exactly-once processing semantics. It natively supports streaming ingestion from Pub/Sub or Kafka, applies transformations via Beam's windowing/triggers, and can write directly to BigQuery using the BigQueryIO connector with streaming inserts or Storage Write API. Its autoscaling (Horizontal Autoscaling and Streaming Engine) handles variable data volumes without server management, making it the only option that meets sub-second latency, serverless, and BigQuery streaming requirements simultaneously.

Answer

Cloud Data Fusion

Answer

Dataproc

Answer

Dataprep

Question 10

Refer to the exhibit. A developer sees this log entry when trying to get a prediction. What is the most likely cause?

Accepted Answer

The model version is not deployed. The log entry indicates that the model version specified in the request is not currently deployed to the serving infrastructure. In Google Cloud's Vertex AI, a model version must be explicitly deployed to an endpoint before it can serve predictions; attempting to predict against a non-deployed version returns an error. This is the most likely cause because the error message directly references the model version's deployment status.

Answer

The model ID is incorrect

Answer

The endpoint does not exist

Answer

The project ID is wrong

Question 11

After deploying a model, the team notices that predictions are significantly different from training data distribution. What should they do?

Accepted Answer

Set up Vertex AI Model Monitoring for skew detection. Vertex AI Model Monitoring is specifically designed to detect skew between training data and serving data, including prediction drift. When predictions differ significantly from the training distribution, this indicates a skew or drift issue that Model Monitoring can alert on, enabling proactive investigation. Updating the endpoint or retraining without diagnosis would not address the root cause, and reviewing the pipeline alone does not provide ongoing detection.

Answer

Update the model endpoint

Answer

Review the training data pipeline

Answer

Retrain the model with new data

Question 12

Refer to the exhibit. A data scientist notices that the evaluation component rarely passes the threshold, causing the pipeline to fail often. What should they do to improve efficiency?

Accepted Answer

Add a conditional component that only runs evaluation if training metrics are above a certain level. Adding a conditional component that only runs evaluation when training metrics exceed a certain threshold prevents unnecessary evaluation runs on poorly performing models. This reduces pipeline failures by ensuring that evaluation, which may be resource-intensive or prone to failure with low-quality inputs, is only triggered when the model has demonstrated sufficient training performance. This approach optimizes resource usage and pipeline reliability without sacrificing the evaluation step entirely.

Answer

Reduce the training dataset size

Answer

Remove the evaluation component

Answer

Increase the threshold value

Question 13

A team wants to transfer data from an on-premises Hadoop cluster to Cloud Storage for processing. The cluster is located in a remote area with limited bandwidth. They need to transfer 500 TB of data. Which service should they use?

Accepted Answer

Transfer Appliance. Transfer Appliance is designed for petabyte-scale offline transfers when bandwidth is limited.

Answer

BigQuery Data Transfer Service

Answer

Storage Transfer Service

Answer

Dataproc with gsutil

Question 14

A company needs to process high-throughput streaming data with low latency. They are considering Cloud Pub/Sub for ingestion and Cloud Dataflow for processing. However, they are concerned about cost. Which alternative to Cloud Pub/Sub would reduce costs while still meeting the throughput requirements?

Accepted Answer

Cloud Pub/Sub Lite. Cloud Pub/Sub Lite is a zonal, cost-optimized messaging service designed for high-throughput streaming with predictable, low latency. Unlike standard Pub/Sub, which replicates data across zones and charges for data transfer and storage, Pub/Sub Lite lets you choose a capacity (in MiB/s) and charges a flat hourly rate, significantly reducing costs for steady, high-volume workloads. It integrates with Cloud Dataflow via the Pub/Sub Lite I/O connector, so the processing pipeline remains viable. Thus, it meets the throughput and latency requirements while addressing cost concerns.

Answer

Cloud Pub/Sub with pull subscriptions

Answer

Cloud Tasks

Answer

Cloud Pub/Sub with push subscriptions

Question 15

A company wants to ingest data from an on-premises Oracle database into BigQuery in near real-time with minimal latency. The database has a high volume of inserts and updates. Which service should they use?

Accepted Answer

Datastream. Datastream is correct because it is a serverless change data capture (CDC) service that replicates data from Oracle to BigQuery in near real-time with minimal latency. It reads the Oracle redo logs (via LogMiner or binary log reader) to capture inserts, updates, and deletes and streams them to BigQuery.

Answer

BigQuery Data Transfer Service

Answer

Pub/Sub

Answer

Storage Transfer Service