PDE Practice Test 48 — 15 Questions

Question 1

Which THREE Google Cloud services are typically used together in a production ML pipeline?

Accepted Answer

Cloud Storage. Cloud Storage is correct because it serves as the central artifact repository in a production ML pipeline on Google Cloud. It stores training data, model artifacts, and prediction inputs/outputs, enabling seamless integration with Vertex AI Training for model training and Vertex AI Prediction for serving. Without Cloud Storage, there is no durable, scalable, and cost-effective way to manage the large datasets and model binaries required for production ML workflows.

Answer

Cloud Functions

Answer

BigQuery

Question 2

Which TWO actions are recommended to improve the reliability of a Cloud Dataflow streaming pipeline that processes event data from Pub/Sub?

Accepted Answer

Enable Dataflow Streaming Engine.. Enabling Dataflow Streaming Engine moves state and computation from worker VMs to the backend service, reducing the impact of worker scaling and preemption. This improves reliability by providing consistent performance and fault tolerance for streaming pipelines, especially those with high throughput or stateful processing.

Answer

Use a pull subscription with a 10-second acknowledgment deadline.

Answer

Disable autoscaling to prevent worker churn.

Answer

Use micro-batch processing with a small batch size.

Question 3

A data engineer needs to ingest daily Salesforce reports into BigQuery without writing custom code. The reports are exported to an Amazon S3 bucket on a schedule. Which service should they use to automate the transfer?

Accepted Answer

BigQuery Data Transfer Service. The BigQuery Data Transfer Service (BQDTS) is the correct choice because it supports scheduled, automatic ingestion of Salesforce reports exported to Amazon S3 into BigQuery without requiring custom code. It can connect to S3 as a source and load the data into BigQuery tables on a schedule, handling schema detection and incremental updates. This meets the requirement of no-code automation from an S3 bucket.

Answer

Cloud Dataproc

Answer

Cloud Composer

Answer

Cloud Storage Transfer Service

Question 4

A company deploys a TensorFlow model on Vertex AI for online predictions. They want to monitor model performance in production to detect degradation. Which TWO practices should they implement? (Choose 2.)

Accepted Answer

Log prediction requests and responses to Cloud Logging and analyze distribution metrics.. Logging prediction requests and responses to Cloud Logging allows you to analyze distribution metrics (e.g., mean, variance, quantiles) over time. This enables detection of data drift or performance degradation by comparing live distributions against baseline distributions, which is a standard monitoring practice for production ML models.

Answer

Use a separate endpoint for shadow testing new model versions.

Answer

Set up Cloud Monitoring alerts for high prediction latency.

Answer

Schedule daily retraining of the model regardless of monitoring alerts.

Question 5

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 6

You are operating a streaming data pipeline that uses Cloud Pub/Sub and Dataflow. The data source sometimes emits events that are delayed by several minutes due to network issues. Your pipeline must produce accurate aggregations (e.g., counts per minute) even for late data, but you also need to avoid waiting for a long time before emitting results. Which approach should you use?

Accepted Answer

Use event-time processing with allowed lateness and a trigger that fires early to provide speculative results.. It uses event-time processing to handle late data via allowed lateness, combined with early triggers to emit speculative results before the window closes. This balances accuracy for delayed events with low latency for downstream consumers, which is a common requirement in streaming pipelines using Cloud Pub/Sub and Dataflow.

Answer

Use processing-time windows and ignore the event timestamps entirely.

Answer

Use global windows and hold all data for 24 hours before processing to ensure completeness.

Answer

Use event-time processing and discard any data that arrives after the window ends.

Question 7

A company uses Vertex AI to serve a model that requires GPU for inference. They want to minimize cost while handling variable traffic. Which strategy should they use?

Accepted Answer

Use a Vertex AI Endpoint with GPU and configure auto-scaling to zero when idle. Vertex AI Endpoints support GPU-accelerated inference with autoscaling, including the ability to scale down to zero replicas when there is no traffic. This minimizes cost by only incurring GPU charges during active inference, while still handling variable traffic through dynamic scaling.

Answer

Deploy the model to Cloud Functions with GPU

Answer

Use Vertex AI Batch Prediction with GPU

Answer

Use a Vertex AI Endpoint with GPU with a fixed number of replicas

Question 8

A data team runs regular analytical queries on a BigQuery table that stores 2 years of sales data (approximately 10 TB). Queries frequently filter on a `sale_date` column and also group by `product_id`. To optimize cost and performance, which design approach is most effective?

Accepted Answer

Partition the table by `sale_date` and cluster by `product_id`.. Partitioning by `sale_date` allows BigQuery to perform partition pruning, eliminating scans of irrelevant date ranges, while clustering by `product_id` physically co-locates rows with the same product ID within each partition. This combination minimizes the data scanned for queries that filter on `sale_date` and group by `product_id`, directly reducing both cost (bytes billed) and query latency.

Answer

Do not partition; only cluster by `sale_date`.

Answer

Partition by `sale_date` and set a table expiration of 90 days.

Answer

Partition by `product_id` and cluster by `sale_date`.

Question 9

A company wants to use Cloud Data Fusion for ETL pipelines. They need to integrate with custom transformations not available in the marketplace. What should they do?

Accepted Answer

Write a custom plugin using the CDAP SDK and deploy it.. Cloud Data Fusion supports custom plugins using the CDAP SDK.

Answer

Switch to Dataproc and write a Spark job.

Answer

Use the Data Fusion Hub to download a custom plugin.

Answer

Use Dataprep to create the transformation.

Question 10

Drag and drop the steps to create a Cloud Storage bucket with uniform bucket-level access into the correct order.

Accepted Answer

Navigate to Cloud Storage, click Create Bucket, enter bucket name, select Uniform under access control, click Create. Uniform bucket-level access simplifies permissions by using IAM policies at the bucket level instead of ACLs.

Answer

Click Create Bucket, navigate to Cloud Storage, enter bucket name, select Uniform, click Create

Answer

Navigate to Cloud Storage, click Create Bucket, select Uniform, enter bucket name, click Create

Answer

Navigate to Cloud Storage, click Create Bucket, enter bucket name, click Create, select Uniform

Question 11

A company is designing a data lake on Cloud Storage for analytics. They need to store data in various formats (Avro, Parquet, CSV) and enable efficient querying with BigQuery and Dataproc. Which THREE practices should they follow?

Accepted Answer

Use BigLake to create BigQuery tables that reference Cloud Storage data.. BigLake allows you to create BigQuery tables that reference data stored in Cloud Storage, enabling unified governance and fine-grained access control without moving data. This is essential for a data lake architecture where BigQuery and Dataproc need to query the same underlying data in various formats like Avro, Parquet, and CSV.

Answer

Disable encryption on the bucket to improve read performance.

Answer

Store all data in CSV format for simplicity.

Question 12

You need to ingest Google Ads performance data into BigQuery on a daily basis for reporting. Which service should you use?

Accepted Answer

BigQuery Data Transfer Service for Google Ads. The BigQuery Data Transfer Service for Google Ads is the correct choice because it provides a fully managed, scheduled connector that automatically ingests Google Ads performance data into BigQuery on a daily basis without requiring any custom code. It handles authentication, schema mapping, and incremental loads, making it the simplest and most reliable solution for this specific use case.

Answer

Cloud Scheduler to call Google Ads API and load to BigQuery

Answer

Pub/Sub with a Google Ads subscriber

Answer

Storage Transfer Service for Google Ads

Question 13

A company uses Cloud Functions to process events from Cloud Storage. They notice that occasionally functions are not triggered. What should they check first to ensure solution quality?

Accepted Answer

Verify that the Cloud Storage bucket has notifications configured for the correct event type.. The most common reason for Cloud Functions not being triggered by Cloud Storage events is that the bucket's notification configuration is missing or misconfigured. Cloud Functions relies on Pub/Sub notifications from the bucket to invoke the function; if the notification is not set for the correct event type (e.g., `OBJECT_FINALIZE`), the function will never be triggered. Therefore, verifying the notification configuration is the first and most direct diagnostic step.

Answer

Check the logs for function execution.

Answer

Increase the function memory allocation.

Answer

Increase the function timeout.

Question 14

An organization wants to automate their batch data processing pipeline using Cloud Composer. The pipeline consists of multiple tasks: extract from Cloud Storage, transform with Dataflow, and load into BigQuery. Which Airflow operator should be used to run Dataflow jobs?

Accepted Answer

DataflowCreatePythonJobOperator. B is correct because the DataflowCreatePythonJobOperator is specifically designed to submit and manage Apache Beam pipelines written in Python as Dataflow jobs in Google Cloud. This operator handles the creation of a Dataflow job from a Python file, which aligns with the requirement to run Dataflow transformations within a Cloud Composer DAG.

Answer

BigQueryInsertJobOperator

Answer

GCSToBigQueryOperator

Answer

DataprocSubmitJobOperator

Question 15

A company runs a data pipeline that ingests clickstream events from multiple websites into Cloud Pub/Sub, then processed by Dataflow to generate user sessions, and written to BigQuery for analytics. The pipeline runs 24/7. Recently, the team noticed that some sessions are incomplete due to missing events, and data quality checks reveal that about 2% of sessions have gaps of more than 30 minutes. The pipeline uses fixed 30-minute windows for sessionization, with allowed lateness set to 10 minutes. They have Cloud Monitoring dashboards tracking system throughput and pipeline lag but do not have custom metrics tracking per-element delays or watermark progress. The team suspects two possible causes: (a) the Pub/Sub subscription accumulates backlog and some messages are delivered after the window end; (b) the Dataflow job has insufficient workers causing checkpoint failures. The team needs to determine the root cause and improve data quality. What is the best first course of action?

Accepted Answer

Set up a Dataflow monitoring dashboard that tracks the watermark delay and create an alert when it exceeds the allowed lateness.. To determine whether late-arriving messages are the issue, the team should monitor the Dataflow watermark delay, which indicates how far behind the pipeline is compared to the event time. Setting up a metric and alert on watermark delay > allowed lateness will confirm if late data is being dropped.

Answer

Change the Pub/Sub subscription to pull mode with more aggressive flow control settings.

Answer

Increase the number of Dataflow workers and set autoscaling to the maximum allowed.

Answer

Modify the Dataflow pipeline to use session windows instead of fixed windows, and increase allowed lateness to 60 minutes.