PDE Practice Test 17 — 15 Questions

Question 1

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 2

A data warehouse in BigQuery is experiencing performance issues. Which THREE techniques can improve performance without moving data to a different storage system?

Accepted Answer

Partition by date. Partitioning by date in BigQuery allows the query engine to prune entire partitions that do not match the query's date filter, significantly reducing the amount of data scanned and improving performance. This technique works without moving data to a different storage system because it is a metadata-level reorganization of the existing table.

Answer

Use streaming buffer

Answer

Use BigQuery slots

Question 3

An online retailer uses BigQuery for analytics. They have a time-series table with 5 billion rows and new data arrives every day. They want to optimize query performance and reduce costs by ensuring that queries scan only the partitions they need. Which table design should they use?

Accepted Answer

Use a table partitioned on the timestamp column.. Partitioning on the timestamp column allows BigQuery to perform partition pruning, so queries with filters on that column only scan the relevant partitions. This directly reduces the amount of data read, lowering both query cost (pay-per-byte) and improving performance. For a 5-billion-row table with daily data arrival, time-unit partitioning is the standard design to meet the stated goals.

Answer

Use a table clustered on the timestamp column.

Answer

Use a table with no partitioning but use LIMIT in queries.

Answer

Use a table partitioned by ingestion time with a partition expiration.

Question 4

A Dataflow pipeline reads events from Pub/Sub and transforms them. Some events contain invalid product IDs that should be filtered out. The list of valid product IDs is stored in a frequently updated BigQuery table. What is the best approach to filter out invalid events?

Accepted Answer

Read the BigQuery table as a side input and refresh it periodically using a global window with a periodic trigger. Option A is correct because reading the BigQuery table as a side input with a global window and periodic trigger allows the pipeline to refresh the list of valid product IDs at a configurable interval without reprocessing the entire stream. This pattern is idiomatic for Beam/Dataflow when the reference data changes frequently and must be kept reasonably current while maintaining low latency for streaming events.

Answer

Use a Combine.PerKey to group by product ID and then filter

Answer

Use a custom pipeline option to read the valid IDs at startup and cache them

Answer

Use a ParDo with a side input that is a MapSideInput of valid IDs, and refresh it on each element

Question 5

A company is migrating their on-premises Apache Spark jobs to Dataproc. They want to minimize code changes and take advantage of serverless infrastructure. Which Dataproc feature should they use?

Accepted Answer

Dataproc Serverless Spark. Dataproc Serverless Spark is the correct choice because it allows the company to run Spark workloads without provisioning or managing clusters, minimizing code changes by using the same Spark APIs and libraries. This serverless infrastructure automatically scales resources and handles failures, aligning with the goal of reducing operational overhead while maintaining compatibility with existing Spark jobs.

Answer

Dataproc clusters with preemptible VMs

Answer

Dataproc Workflow Templates

Answer

Dataproc Jobs API with custom machine types

Question 6

A Dataflow streaming pipeline reads from Pub/Sub, applies a ParDo that uses a side input from a BigQuery table (refreshed hourly), and writes to BigQuery. The side input is large and causes increased latency and worker OOM errors. Which design change solves this?

Accepted Answer

Use a stateful ParDo and store the lookup data in an external cache like Cloud Bigtable, performing lookups per element.. Option A is correct because moving the large lookup data to an external cache like Cloud Bigtable offloads memory pressure from workers, eliminating OOM errors. The side input broadcast approach keeps the entire dataset in each worker's memory, which causes OOM when the data is large. Using an external cache allows per-element lookups without storing the entire dataset in memory, reducing latency by avoiding broadcast overhead.

Answer

Increase the side input broadcast frequency to update more often.

Answer

Split the pipeline into two: one to load the side input, the other to process main input.

Answer

Use smaller worker machine types to distribute memory across more workers.

Question 7

A data engineering team is operationalizing a machine learning model for real-time inference. They need to monitor the model's performance in production. Which THREE types of monitoring should they implement? (Choose three.)

Accepted Answer

Model accuracy decay. Model accuracy decay (A) is critical because in production, the model's predictive performance can degrade over time due to changes in the underlying data distribution or business logic. Monitoring accuracy decay allows the team to detect when the model no longer meets its performance baseline, triggering retraining or rollback. This is a standard practice in MLOps for maintaining model reliability.

Answer

Model re-training frequency

Answer

Training pipeline failures

Question 8

A data engineering team needs to process a large volume of CSV files stored in Cloud Storage using Dataproc. The files are generated hourly and each contains millions of rows. They want to minimize the number of Dataproc cluster nodes to reduce cost while processing within an hour. Which configuration should they recommend?

Accepted Answer

Use a cluster with many small worker nodes and use Spark dynamic allocation.. Option D is correct because using many small worker nodes with Spark dynamic allocation allows the cluster to scale resources precisely to the workload, minimizing idle capacity and cost. Dynamic allocation enables executors to be added or removed based on the processing demands of the hourly CSV files, ensuring the job completes within the hour without over-provisioning nodes.

Answer

Use a cluster with preemptible worker nodes only.

Answer

Use a cluster with local SSDs for temporary storage.

Answer

Use a cluster with a few large worker nodes and use Spark static allocation.

Question 9

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 10

A team is using Kubeflow Pipelines on Google Kubernetes Engine to orchestrate ML workflows. They need to track parameters, metrics, and artifacts for each run. Which tool should they integrate?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is purpose-built for tracking parameters, metrics, and artifacts in ML workflows, and it integrates natively with Kubeflow Pipelines on Google Kubernetes Engine. It stores metadata for each pipeline run, enabling lineage tracking, comparison, and reproducibility of experiments.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

BigQuery

Question 11

A healthcare organization is deploying a model that processes protected health information (PHI). They need to ensure that the inference data is encrypted in transit and at rest, and access is audited. Which combination of services meets these requirements?

Accepted Answer

Vertex AI Endpoints with VPC-SC, CMEK, and Cloud Audit Logs. Option C is correct because VPC Service Controls (VPC-SC) provides data exfiltration protection and ensures inference data remains within a defined security perimeter, Customer-Managed Encryption Keys (CMEK) encrypt data at rest with keys controlled by the organization, and Cloud Audit Logs capture all access events for auditing. This combination directly addresses encryption in transit (via VPC-SC perimeter enforcement) and at rest (via CMEK), plus access auditing via Cloud Audit Logs.

Answer

Cloud Run with VPC connector and Cloud KMS

Answer

Vertex AI Endpoints with IAM and Cloud Monitoring

Answer

AI Platform Prediction with Cloud Armor

Question 12

A company is migrating ML workflows to Vertex AI Pipelines. They want to ensure best practices for pipeline reproducibility and debugging. Which THREE actions should they take? (Choose three.)

Accepted Answer

Set a random seed for all training components. Setting a random seed for all training components ensures deterministic behavior, meaning that the same inputs will produce the same outputs across multiple runs. This is critical for debugging and reproducibility in Vertex AI Pipelines, as it eliminates stochastic variability that can mask bugs or make results irreproducible. Without a fixed seed, even identical code and data can yield different model weights or metrics, complicating root cause analysis.

Answer

Use dynamic pipeline parameters for each run

Answer

Use conditional execution based on previous component outputs

Question 13

Refer to the exhibit. An auditor sees the following output from `gcloud ai models list`. What can they conclude about versioning?

Accepted Answer

The model has two versions with v2 being the latest. The `gcloud ai models list` output shows two model versions (v1 and v2) under the same model resource. The default traffic split or the listed order indicates v2 is the latest version. This directly confirms that the model has two versions, with v2 being the latest, making option B correct.

Answer

The model is deployed on a single endpoint

Answer

Only the latest version is available

Answer

The model is automatically scaled

Question 14

Refer to the exhibit. A team is trying to run a custom prediction container on Vertex AI Endpoint. They get this error when the container starts. What is the most likely cause?

Accepted Answer

The entry point is missing or incorrect. The error occurs when the container starts, which typically happens during the initial health check or readiness probe. Vertex AI Endpoints require a valid entry point (e.g., CMD or ENTRYPOINT in the Dockerfile) to start the prediction server. If the entry point is missing or incorrect, the container fails to launch, resulting in the observed error.

Answer

The container image is too large

Answer

The container is built for a different CPU architecture

Answer

The model file is missing from the container

Question 15

Refer to the exhibit. An ML engineer sees this error when invoking a Vertex AI endpoint. What is the most likely cause?

Accepted Answer

The model expects 128x128 images but raw input is 256x256. The error indicates a mismatch between the input dimensions expected by the model and the dimensions of the data being sent to the Vertex AI endpoint. ResNet50 models are commonly trained on 128x128 images, and if the raw input is 256x256, the endpoint will reject the request because the model's input tensor shape does not match. This is a typical input validation error in Vertex AI, where the serving infrastructure checks the shape of the prediction request against the model's signature.

Answer

The input format should be JSON

Answer

The model has a bug in the ResNet50 architecture

Answer

The endpoint is overloaded