PDE Practice Test 50 — 15 Questions

Question 1

What is the primary purpose of Vertex AI Feature Store?

Accepted Answer

To store and serve features for machine learning models at scale. Vertex AI Feature Store is a managed service for storing, serving, and sharing ML features. It supports both online (low-latency serving for prediction) and offline (batch serving for training) serving. It is not for model training, data transformation, or experiment tracking.

Answer

To manage and track ML experiments

Answer

To train machine learning models using AutoML

Answer

To transform raw data into features using SQL

Question 2

In the Vertex AI Pipeline component YAML exhibit, the component is designed to evaluate a model and produce metrics. If the threshold_accuracy is set to 0.85, what is the expected behavior of this component?

Accepted Answer

It will output the evaluation metrics, and the pipeline can use them for conditional decisions. In Vertex AI Pipelines, a component's YAML definition specifies inputs, outputs, and implementation. Setting `threshold_accuracy` to 0.85 defines a parameter that the component can use internally, but by itself it does not trigger deployment or cause failure. The component's expected behavior is to output evaluation metrics, and the pipeline can then use those metrics in conditional logic (e.g., via `Condition` or `if/else` tasks) to decide subsequent steps, such as model deployment or retraining.

Answer

It will deploy the model if the accuracy meets the threshold

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It will ignore the threshold_accuracy input if not provided

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It will fail if the model accuracy is below 0.85

Question 3

Which THREE components are typically part of a Vertex AI Pipeline for automated model retraining and deployment?

Accepted Answer

Training component (e.g., CustomContainerTrainingJob). A training component, such as a `CustomContainerTrainingJob`, is the core step in a Vertex AI Pipeline that executes the model training logic. It defines the container image, machine configuration, and hyperparameters, enabling automated retraining when triggered by a schedule or event.

Answer

Cloud Monitoring alerting component

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Cloud Storage artifact storage component

Question 4

Which THREE best practices should be followed when designing a Dataflow pipeline for real-time data processing?

Accepted Answer

Set up monitoring alerts for system lag and data freshness.. Monitoring alerts for system lag and data freshness are essential for maintaining operational visibility in real-time Dataflow pipelines. System lag (the time between data ingestion and processing) and data freshness (how current the processed output is) directly impact the pipeline's ability to meet latency SLAs. Without these alerts, issues like worker backpressure or Pub/Sub subscription backlog can go unnoticed, leading to stale or lost data.

Answer

Use static side inputs that are loaded once at pipeline start.

Answer

Use global windows with early triggers for low latency.

Question 5

Which TWO statements about designing a data processing pipeline on Google Cloud are correct? (Choose 2.)

Accepted Answer

Cloud Data Fusion allows you to build and manage data pipelines visually without writing code.. Cloud Data Fusion provides a visual, no-code interface for building and managing data pipelines, enabling users to design ETL/ELT workflows through a drag-and-drop UI. It abstracts the underlying complexity of Apache Spark and Cloud Dataproc, making it suitable for users who prefer a graphical approach over writing code.

Answer

Pub/Sub guarantees message ordering across all subscribers globally.

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Cloud Bigtable is ideal for data warehousing and SQL analytics.

Answer

Dataproc is the best choice for fully managed data warehousing and analytics.

Question 6

You are responsible for deploying a PyTorch model for real-time inference. The model requires GPU acceleration. You want to minimize infrastructure management overhead. Which serving option should you choose?

Accepted Answer

Deploy the model on Vertex AI Endpoints using a custom container with GPU support. Vertex AI Endpoints with a custom container and GPU support is the correct choice because it is purpose-built for serving ML models at scale, fully managed, and supports GPU acceleration for low-latency inference. It minimizes infrastructure overhead by handling auto-scaling, health checks, and model versioning, unlike the other options that lack GPU support or are designed for training rather than serving.

Answer

Deploy the model as a Cloud Function with a GPU backend

Answer

Use Cloud Run with GPU enabled

Answer

Use AI Platform Training to host the model as a prediction service

Question 7

The exhibit shows a Cloud Logging query result. A data engineer sees this log for a streaming Dataflow job. What is the most likely cause?

Accepted Answer

The job has reached the maximum number of workers allowed by the project quota.. The log shows that the Dataflow job is not scaling up despite pending work. This typically occurs when the job has reached the maximum number of workers allowed by the project quota. Dataflow uses the Compute Engine default worker quota, and if the job attempts to exceed that limit, it will stop scaling and log messages indicating that it cannot add more workers.

Answer

The job is experiencing network latency.

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The job is using too much memory per worker.

Answer

The job has insufficient permissions to scale.

Question 8

A company wants to train a machine learning model to predict customer churn using BigQuery ML. The dataset has a severe class imbalance (only 2% churn). Which approach should the data engineer take to handle this imbalance within BigQuery ML?

Accepted Answer

Set the CLASS_WEIGHTS option to 'balanced' in the CREATE MODEL statement. BigQuery ML supports class weights via the CLASS_WEIGHTS option in CREATE MODEL, which adjusts the loss function to penalize misclassifications of the minority class more heavily.

Answer

Use SMOTE directly in BigQuery SQL before training

Answer

Create a custom Vertex AI model using TensorFlow and use the class_weight parameter

Answer

Oversample the minority class using a SQL query that duplicates rows

Question 9

A company is migrating its on-premises Apache Spark jobs to Dataproc. The jobs read from and write to Cloud Storage. After migration, the jobs are slower than expected. The Dataproc cluster uses standard worker machines with local SSDs. What is the most likely cause of the performance degradation?

Accepted Answer

The jobs use the Cloud Storage connector instead of HDFS, causing network latency.. D is correct because the performance degradation is most likely due to network latency when using the Cloud Storage connector instead of HDFS. Cloud Storage is an object store accessed over the network, while HDFS leverages local SSDs for data locality and faster I/O. In Dataproc, jobs that read/write to Cloud Storage incur higher latency compared to using HDFS on local SSDs, especially for shuffle-heavy Spark workloads.

Answer

The Spark shuffle service is not enabled on the cluster.

Answer

The local SSDs are not mounted or are misconfigured.

Answer

The Cloud Storage connector is not using the gRPC protocol.

Question 10

A data engineer needs to create a BigQuery ML model for predicting customer churn using a dataset with 10 million rows and 50 features. The dataset is highly imbalanced (5% churn). Which approach should the engineer use to handle class imbalance during model training?

Accepted Answer

Use the CREATE MODEL statement with CLASS_WEIGHTS = {'0': 0.2, '1': 0.8}. BigQuery ML supports class weights for imbalanced datasets via the CLASS_WEIGHTS option in CREATE MODEL. This assigns higher weight to the minority class without generating synthetic data. SMOTE is not available in BigQuery ML. Undersampling the majority class would lose data, and oversampling with duplication could introduce bias.

Answer

Undersample the majority class before training

Answer

Use SMOTE via TRANSFORM clause in BigQuery ML

Answer

Oversample the minority class by duplicating rows

Question 11

You are migrating an existing Kafka cluster to Google Cloud using Dataproc. The cluster handles high-throughput streaming data with strict ordering requirements per partition. Which choice of Dataproc configuration is most appropriate?

Accepted Answer

Use Dataproc with non-preemptible workers and persistent SSD storage for brokers.. Kafka brokers in a Dataproc cluster require persistent, non-preemptible workers to maintain data durability and strict ordering per partition. Preemptible workers can be terminated at any time, causing data loss or rebalancing that violates ordering guarantees. Persistent SSD storage provides the low-latency I/O needed for high-throughput Kafka workloads, while non-preemptible instances ensure broker stability and consistent replication.

Answer

Use Dataflow with Kafka IO instead of Dataproc.

Answer

Use Dataproc with local SSDs for better performance, and enable autoscaling.

Answer

Use Dataproc with preemptible workers to reduce cost, and attach standard persistent disks.

Question 12

A company wants to use BigQuery ML to build a recommendation system for movies. The data includes user IDs, movie IDs, and ratings. Which BigQuery ML model types are suitable for this? (Select TWO)

Accepted Answer

Boosted tree classifier. Matrix factorization (via implicit or explicit feedback) is specifically designed for recommendation systems. Boosted tree classifiers can also be used for predicting ratings as a classification problem. AutoML Tables is not a model type in BigQuery ML. ARIMA_PLUS is for time-series. k-means is for clustering, not recommendations directly.

Answer

ARIMA_PLUS

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k-means

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AutoML Tables

Question 13

A large retail company processes point-of-sale transactions from thousands of stores daily. The current batch pipeline runs on Cloud Dataproc using Spark and takes 3 hours to complete. The business wants to reduce processing time to under 30 minutes. The pipeline reads from Cloud Storage, joins with inventory data from BigQuery, performs aggregations, and writes to Cloud SQL for reporting. What is the most effective optimization?

Accepted Answer

Read inventory data from BigQuery and pre-join in BigQuery, then export to Cloud Storage as ORC files. It offloads the join operation to BigQuery, which is optimized for large-scale analytics and can process the join much faster than Spark. By pre-joining and exporting the result as ORC files (a columnar format optimized for Spark), the pipeline avoids the expensive shuffle and data transfer between Cloud Storage and BigQuery, significantly reducing the overall processing time to meet the 30-minute target.

Answer

Migrate the pipeline to Cloud Dataflow with Apache Beam for auto-scaling

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Write intermediate results to Cloud SQL instead of BigQuery for faster access

Answer

Increase the number of worker nodes in the Dataproc cluster

Question 14

A company needs to process real-time clickstream data and store it in a data warehouse for SQL-based analytics. The data volume is moderate. Which combination of Google Cloud services is most cost-effective?

Accepted Answer

Cloud Pub/Sub, Cloud Dataflow, BigQuery. Cloud Pub/Sub ingests real-time clickstream data, Cloud Dataflow processes it with low latency, and BigQuery provides a serverless, SQL-based data warehouse that is cost-effective for moderate data volumes due to its pay-per-query pricing and automatic scaling. This combination avoids the overhead of managing clusters (Dataproc) or expensive storage (Cloud Spanner) while directly supporting SQL analytics.

Answer

Cloud Pub/Sub, Cloud Dataproc, Cloud Storage

Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Spanner

Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Storage

Question 15

Your company is building a real-time fraud detection system using Google Cloud. Transactions are streamed into Pub/Sub, and you need to process them with low latency (under 100ms per event) and aggregate data over sliding windows. Which Google Cloud service is best suited for this processing logic?

Accepted Answer

Dataflow. Dataflow is the best choice because it provides a unified stream and batch processing model with native support for Pub/Sub, exactly-once semantics, and low-latency sliding window aggregations. Its autoscaling and millisecond-level checkpointing enable sub-100ms per event processing, which is critical for real-time fraud detection.

Answer

BigQuery streaming inserts with scheduled queries

Answer

Dataproc with Spark Streaming

Answer

Cloud Functions