PDE Practice Exam 53 — 20 Questions

Question 1

Match each Google Cloud service to its data processing capability.

Accepted Answer

Dataflow: Unified stream and batch data processing. Dataflow is for stream/batch processing, Dataproc for managed clusters, and Pub/Sub for messaging. Distractors swap responsibilities with Cloud Composer and Dataflow.

Answer

Dataflow: Orchestrates multiple services and workflows

Answer

Pub/Sub: Data transformation and enrichment

Question 2

Match each machine learning term to its description.

Accepted Answer

Supervised Learning: Model trained on labeled data to predict output. In machine learning, supervised learning uses labeled data (inputs mapped to known outputs), unsupervised learning finds patterns in unlabeled data, features are input variables, and labels are the target outputs. Common confusions include mixing supervised/unsupervised definitions or swapping features and labels.

Answer

Supervised Learning: Model finds patterns without labeled data

Answer

Feature: The output variable the model predicts

Question 3

A team notices that the latency for online predictions from a Vertex AI endpoint has increased significantly over the past hour. The model is a large TensorFlow model deployed with automatic scaling (minReplicaCount=2, maxReplicaCount=10). The CPU utilization of the deployed instances is consistently above 85%. What is the most likely cause of the increased latency?

Accepted Answer

The model is CPU-bound, and the current replicas are saturated, causing queuing.. The consistently high CPU utilization (above 85%) indicates that the existing replicas are saturated, unable to process incoming requests quickly enough. When all replicas are busy, new requests are queued, which directly increases latency. Automatic scaling can add more replicas up to maxReplicaCount=10, but if the scaling is slow or the traffic spike is sudden, queuing occurs first, causing the observed latency increase.

Answer

The network latency between the client and the endpoint has increased due to regional issues.

Answer

The model is deployed with GPU acceleration, but the instances are using incorrect CUDA drivers.

Answer

The model is too large for the instance memory, causing disk swapping.

Question 4

A company wants to share a large BigQuery dataset with a partner for analysis. The partner needs read-only access to a specific snapshot of the data as of a certain point in time, and the company wants to avoid additional storage costs for the partner. What is the most cost-effective approach?

Accepted Answer

Create a BigQuery table snapshot at the desired point in time and share it.. BigQuery table snapshots are zero-copy, read-only, and share the underlying storage with the base table. They do not incur additional storage costs as long as the snapshot is within the time travel window (7 days default). Since the partner requires read-only access to a specific point-in-time snapshot and the company wants to avoid additional storage costs, a table snapshot is the most cost-effective approach. In contrast, table clones are writable and designed for development purposes, not read-only sharing; exporting to Cloud Storage incurs storage and egress costs; and authorized views do not provide a point-in-time snapshot and incur query costs.

Answer

Create a BigQuery table clone at the desired point in time and share it with the partner.

Answer

Export the table to Cloud Storage as Avro and share a signed URL.

Answer

Grant the partner access to the original table with an authorized view.

Question 5

A data engineer tries to grant a service account read access to a Cloud Storage bucket using the IAM policy above. The service account still cannot read objects. What is the most likely reason?

Accepted Answer

The condition prevents access because the request time is after 2023. The IAM condition explicitly restricts access to requests made before January 1, 2023. Since the current time is after that date, the condition evaluates to false, denying the service account's read access regardless of the role binding. IAM conditions are evaluated at request time, and if the condition is not met, the permission is not granted.

Answer

The role does not include the necessary permission

Answer

The service account is misspelled

Answer

The role should be roles/storage.admin

Question 6

A data engineer notices that BigQuery queries are slower than expected. They want to identify the most expensive stages in the query execution. Which tool or command should they use?

Accepted Answer

Use bq query --format=prettyjson and look at statistics. To identify expensive stages in BigQuery query execution, you should run the query with the bq command-line tool using the --format=prettyjson flag. This returns a detailed job object that includes a query plan with stages, each having cost estimates like 'avg execution time' and 'peak memory'. Analyzing these stages reveals the most resource-intensive parts.

Answer

Use bq show to view job statistics

Answer

Use Cloud Monitoring to view query execution graphs

Answer

Use EXPLAIN statement in BigQuery

Question 7

Your Dataflow pipeline reads from Pub/Sub, performs transformations, and writes to BigQuery. You notice that the pipeline's autoscaling is not keeping up with sudden spikes in traffic, causing increased lag. The pipeline uses Classic Templates. Which change would most effectively improve autoscaling responsiveness?

Accepted Answer

Enable Dataflow Streaming Engine on the pipeline.. Enabling Dataflow Streaming Engine reduces the overhead of checkpointing and state management by offloading them to the service side, which allows the pipeline to scale more quickly in response to sudden traffic spikes. This directly addresses the autoscaling lag because Streaming Engine decouples compute from state, enabling faster worker adjustments without the bottleneck of persistent disk-based shuffle.

Answer

Switch to Dataflow Prime with Vertical Autoscaling enabled.

Answer

Increase the initial number of workers to handle the spike.

Answer

Use Flex Templates instead of Classic Templates.

Question 8

Refer to the exhibit. What is the cause of this error?

Accepted Answer

The machine type flag is only used during model deployment, not endpoint creation. The error occurs because the `machine_type` flag is only valid during model deployment (when creating a deployment in Vertex AI), not during endpoint creation. When creating an endpoint, you specify the endpoint name and region, but the machine type is configured later when deploying a model to that endpoint. Attempting to set `machine_type` during endpoint creation causes a validation error because the API does not accept that parameter at that stage.

Answer

The endpoint name already exists

Answer

The user must specify a model name

Answer

The region is missing

Question 9

You need to automate retraining of a model when new training data becomes available every week. The training pipeline runs on Vertex AI Pipelines and is triggered by Cloud Composer. After retraining, you want to evaluate the new model against a golden dataset. If the model's accuracy improves by at least 1%, it should be automatically deployed to the staging endpoint. What is the best way to implement the decision logic?

Accepted Answer

Add a conditional step in the Vertex AI Pipeline to evaluate the model and deploy if the accuracy improvement threshold is met.. Vertex AI Pipelines supports conditional execution natively via the `Condition` component, allowing you to evaluate the new model's accuracy against the golden dataset within the same pipeline and deploy only if the improvement threshold (≥1%) is met. This approach keeps the entire retraining, evaluation, and deployment workflow automated, auditable, and tightly coupled within a single orchestrated pipeline, avoiding external triggers or manual steps.

Answer

Use Cloud Functions to compare metrics and call the endpoint if conditions are met.

Answer

After training, run a batch prediction job on the golden dataset and compare metrics manually.

Answer

Use Vertex AI Experiments to log metrics and set up an alert to manually deploy.

Question 10

A healthcare company stores patient records as JSON files in Cloud Storage for analysis. They want to design a data lake that enables querying the data with BigQuery while minimizing storage costs and maintaining data security. Which two actions should they take? (Choose two.)

Accepted Answer

Configure object lifecycle management to transition files older than 90 days to Nearline storage.. Options B and D are correct. Option B uses Cloud Storage object lifecycle management to transition older objects to Nearline storage, reducing storage costs while maintaining queryability via BigQuery external tables. Option D uses BigLake to create external tables with row-level security and access delegation, ensuring data security and enabling direct BigQuery querying without moving data, while minimizing storage costs by avoiding data duplication.

Answer

Partition the data by date and store in separate directories for each partition.

Answer

Convert all JSON files to CSV to reduce storage size.

Answer

Enable Cloud KMS to encrypt the data with customer-managed encryption keys.

Question 11

You are designing a data pipeline that must process sensitive customer data with strict access controls. The data is ingested via Cloud Pub/Sub, processed by Cloud Dataflow, and stored in BigQuery. The security team requires that data is encrypted at rest and in transit, and that access is limited to specific service accounts. Which implementation strategy meets all requirements?

Accepted Answer

Use CMEK for Pub/Sub, Dataflow, and BigQuery, and VPC-SC with per-service service accounts. It combines Customer-Managed Encryption Keys (CMEK) for all three services (Pub/Sub, Dataflow, BigQuery) to ensure data is encrypted at rest with keys controlled by the customer, and uses VPC Service Controls (VPC-SC) with per-service service accounts to enforce network perimeter security and least-privilege access. This meets the requirements for encryption at rest and in transit (CMEK also covers in-transit encryption via TLS) and strict access controls via service accounts and VPC-SC.

Answer

Use Cloud KMS for BigQuery only; leave Dataflow with default encryption

Answer

Use VPC Service Controls and Cloud Armor for network security

Answer

Use default Google-managed encryption keys and IAM roles only

Question 12

You have a BigQuery table 'orders' with columns order_id, customer_id, order_amount, and order_date. You need to rank customers by total spend per month, assigning the rank 1 to the highest spender. Which SQL function should you use in a window clause?

Accepted Answer

RANK(). RANK() assigns a rank with gaps for ties; for top-spender ranking, that is appropriate. DENSE_RANK() also works but without gaps; the stem does not specify. ROW_NUMBER() gives unique numbers even for ties. However, typical ranking with ties uses RANK().

Answer

NTILE()

Answer

DENSE_RANK()

Answer

ROW_NUMBER()

Question 13

A company uses Vertex AI Feature Store for serving features. They have a high-throughput online serving requirement. Which configuration should they use?

Accepted Answer

Vertex AI Feature Store with online serving enabled. Vertex AI Feature Store with online serving enabled is the correct choice because it is specifically designed for low-latency, high-throughput retrieval of feature values for online predictions. It uses a managed Bigtable backend optimized for real-time serving, ensuring consistent performance under high request loads without requiring manual infrastructure management.

Answer

Cloud Storage with high-memory instances

Answer

Bigtable as serving source

Answer

Firestore

Question 14

You run `gcloud ai models describe` and get the error above. The model was created successfully from a training job that completed without errors. The model ID is correct. What is the most likely cause?

Accepted Answer

The model was created in a different region (e.g., europe-west4) than the one specified in the command.. `gcloud ai models describe` defaults to the `us-central1` region unless overridden with the `--region` flag. If the model was created in a different region (e.g., `europe-west4`), the command will fail with a 'Model not found' error even though the model ID is correct. Vertex AI models are regional resources, so the region must match exactly.

Answer

The model was deleted or expired due to time-to-live settings.

Answer

The gcloud command is not authenticated to the correct project.

Answer

The model was created but not yet trained; training must complete before describe works.

Question 15

A company is migrating on-premises Apache Spark jobs to Google Cloud Dataproc. They want to reduce operational overhead and minimize costs. Which architecture is most appropriate?

Accepted Answer

Use Dataproc clusters with auto-scaling and preemptible VMs.. Dataproc clusters with auto-scaling and preemptible VMs directly address the need to reduce operational overhead and minimize costs for on-premises Spark migrations. Auto-scaling dynamically adjusts cluster size based on workload, while preemptible VMs (which cost 60-80% less than standard VMs) handle fault-tolerant tasks, making this the most cost-effective and operationally efficient architecture for Spark on Dataproc.

Answer

Use Cloud Dataproc Serverless for all Spark jobs.

Answer

Migrate jobs to Cloud Dataflow.

Answer

Run Spark on Compute Engine instances with startup scripts.

Question 16

An e-commerce company processes real-time clickstream data using Pub/Sub and Dataflow. They want to ensure that if a Dataflow worker fails, the pipeline can resume processing from the point of failure without data loss. Which feature should they enable?

Accepted Answer

Snapshot-based recovery. Snapshot-based recovery (Option C) is the correct feature because Dataflow snapshots capture the entire pipeline state, including the current position in each Pub/Sub subscription and the state of all transforms. If a worker fails, the pipeline can be resumed from the exact snapshot point, ensuring no data loss and exactly-once processing semantics for the recovered data.

Answer

At-least-once delivery mode

Answer

Exactly-once processing mode

Answer

Streaming engine

Question 17

You have a BigQuery table that is partitioned by ingestion time and clustered on user_id. The table stores event logs and is queried frequently by user_id to analyze user behavior over the last 30 days. Queries are still scanning too many partitions. Which optimization should you apply first?

Accepted Answer

Change the partition column to a DATE column based on event_timestamp and keep clustering on user_id. The query filter on user_id already uses clustering, which prunes blocks within a partition. But the query also filters on a date range, which should leverage partition pruning. If queries still scan many partitions, the most likely cause is that the partition filter is not applied effectively. Using a range filter on the partition column (_PARTITIONDATE) or a column used for partitioning will limit partitions scanned. But the question already says the table is partitioned by ingestion time. The best next step is to ensure the query uses a filter on the partition column. However, among the options, changing the partition type to a specific date column (e.g., event_timestamp) with clustering on user_id could improve if the ingestion time doesn't align with the query time range.

Answer

Create a materialized view that pre-aggregates data by user_id and date

Answer

Remove partitioning and rely solely on clustering

Answer

Add clustering on a second column like event_type

Question 18

A data engineer needs to ingest on-premises Oracle CDC data into BigQuery in near real-time with minimal operational overhead. Which service should they use?

Accepted Answer

Datastream. Datastream is purpose-built for streaming change data capture (CDC) from Oracle and other sources into BigQuery with near-real-time latency and minimal operational overhead. It handles schema propagation, checkpointing, and automatic retries, eliminating the need to manage custom ingestion pipelines.

Answer

Pub/Sub + Dataflow

Answer

Storage Transfer Service

Answer

Transfer Appliance

Question 19

Refer to the exhibit. A BigQuery dataset is shared with the group 'analysts@example.com' using the IAM policy shown. A user who is a member of this group reports that they cannot run queries on the dataset, though they can see the tables. What is the most likely reason?

Accepted Answer

The group needs the 'roles/bigquery.jobUser' role at the project level.. The IAM policy grants the 'roles/bigquery.dataViewer' role at the dataset level, which allows the user to see tables but not run queries. To run queries, the user also needs the 'roles/bigquery.jobUser' role at the project level, because BigQuery query jobs are project-scoped resources. Without this role, the user lacks permission to create query jobs, even though they can view dataset metadata.

Answer

The user is using an incorrect client library version.

Answer

The user's account is not activated in the group membership.

Answer

The dataset has an organization policy that denies query access.

Question 20

A data pipeline uses Cloud Pub/Sub to ingest events, then a Dataflow job writes to Cloud Storage in Avro format. The Dataflow job uses Global windows with a 10-minute trigger. The data is later loaded into BigQuery. They notice duplicate rows in BigQuery because the trigger produced multiple panes. What should the Dataflow pipeline change to eliminate duplicates?

Accepted Answer

Enable exactly-once sink to BigQuery via Dataflow. Enabling exactly-once sinks in Dataflow ensures that each record is written to the sink only once, even if the pipeline produces multiple panes due to triggers. In this scenario, the 10-minute trigger on a global window causes multiple output panes, leading to duplicate rows in BigQuery. Exactly-once sinks use idempotent writes and deduplication mechanisms to prevent duplicates, directly addressing the issue without changing the windowing or trigger logic.

Answer

Use a sharded output to Cloud Storage with unique filenames

Answer

Write to a staging table and use a MERGE statement in BigQuery

Answer

Use a session window instead of global window