PDE Practice Exam 4 — 20 Questions

Question 1

A media company uses Cloud Data Loss Prevention (DLP) API to inspect and de-identify sensitive data before loading into BigQuery. They want to reduce costs by sampling the data during inspection. Which configuration should they use?

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

Accepted Answer

Set the sample method to 'RANDOM' with a percentage.. The Cloud DLP API supports a 'sample_method' of 'RANDOM' with a 'sampling_percentage' to inspect only a random subset of rows. This directly reduces the volume of data scanned, lowering costs while still providing statistically representative coverage for sensitive data discovery.

Answer

Use the 'ROWS' limit in the inspection job.

Answer

Use a hybrid inspection with a BigQuery sample table.

Answer

Use the 'BYTES_LIMIT' parameter.

Question 2

A data engineer needs to create a Dataflow pipeline template that can be reused across multiple environments (dev, staging, prod) with different parameters (e.g., input Pub/Sub topic, output BigQuery table). Which template type should they use?

Accepted Answer

Flex Template. Flex Templates (B) are the correct choice because they package a Dataflow pipeline as a Docker image, allowing environment-specific parameters (e.g., Pub/Sub topic, BigQuery table) to be passed at runtime via the --parameters flag. This enables true reusability across dev, staging, and prod without modifying the template code, unlike Classic Templates which require compile-time parameterization.

Answer

Dataflow Prime

Answer

Classic Template

Answer

Cloud Composer workflow template

Question 3

A company uses BigQuery for analytics on petabyte-scale data. They want to improve query performance by denormalizing schemas and reducing joins. Which TWO BigQuery features should they use? (Choose 2)

Accepted Answer

Clustering on frequently filtered columns. Option E is correct because BigQuery's native support for nested and repeated fields (ARRAY>) lets you store parent-child relationships inside a single table, which is the standard way to denormalize schemas and eliminate joins while preserving relational structure. Option A is correct because clustering on frequently filtered columns physically co-locates related rows within partitions, so BigQuery scans far less data and returns results faster for filtered queries on the denormalized table. Option B is not a BigQuery feature for denormalization; subqueries still execute as separate query blocks and do not remove join costs. Option C is wrong because external tables read data from Cloud Storage and generally offer worse performance than native tables, not better denormalized performance. Option D is wrong because partitioning by date improves pruning on time filters but does not denormalize schemas or reduce joins.

Answer

Using subqueries instead of JOINs

Answer

External tables reading from Cloud Storage

Answer

Table partitioning by date

Question 4

A company uses Cloud Dataproc to run Spark ML training jobs. They want to persist the trained models and metadata in a Hive-compatible metastore. Which Dataproc feature should they use?

Accepted Answer

Dataproc Metastore. Dataproc Metastore is a fully managed, Hive-compatible metastore service on Google Cloud that integrates natively with Dataproc clusters, allowing Spark and Hive jobs to persist and share table metadata and schemas. It provides a serverless, scalable alternative to running a self-managed Hive metastore on a cluster, and it supports the Hive Metastore Thrift API so existing Spark ML workflows can store model metadata without code changes. This directly meets the requirement for a Hive-compatible metastore for trained models and metadata.

Answer

Cloud Hive Metastore (self-managed)

Answer

Cloud Bigtable

Answer

Cloud Data Catalog

Question 5

Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from BigQuery, feature engineering using Dataflow, and model training using a custom container on Vertex AI Training. Recently, the pipeline has been failing intermittently at the Dataflow step with a 'The job encountered a transient error. Please retry.' message. You have enabled pipeline retries with 3 attempts. However, the pipeline still fails after 3 retries. You check the logs and find that the Dataflow job requires more resources than the default worker configuration provides. Which change should you make to reduce the failure rate?

Accepted Answer

Increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration. The pipeline fails due to insufficient resources (memory and CPU) in the default Dataflow worker configuration. By increasing the worker machine type (e.g., using a custom machine type with more vCPUs and memory), the Dataflow job can handle the feature engineering workload without hitting resource limits, reducing transient failures. This directly addresses the root cause identified in the logs, unlike retries or parallelism changes.

Answer

Increase the number of Dataflow workers to improve parallelism

Answer

Increase the number of retries in the pipeline to 5

Answer

Replace Dataflow with Dataproc to run the feature engineering step

Question 6

A startup wants to build a data lake on Google Cloud using Cloud Storage. They need to store raw data in its original format for future analysis. Which storage class should they use to optimize for cost given that data will be accessed occasionally after the first month?

Accepted Answer

Nearline storage class. Nearline storage class is the optimal choice because it offers low-cost storage for data accessed less than once a month, with a 30-day minimum storage duration. Since the data is accessed occasionally after the first month, Nearline provides significant cost savings over Standard while still offering low-latency access (milliseconds) suitable for analytics. Coldline and Archive have lower storage costs but impose higher retrieval fees and minimum storage durations (90 and 365 days respectively), making them more expensive for data that is accessed occasionally within the first year.

Answer

Coldline storage class

Answer

Standard storage class

Answer

Archive storage class

Question 7

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 8

You are optimizing a BigQuery query that runs on a large table (hundreds of TB). The table is partitioned by date and frequently queried with filters on a specific customer_id column and date range. Queries are slow even after partitioning. Which optimization should you apply?

Accepted Answer

Columnar clustering on customer_id. Clustering on customer_id within the partition improves query performance because BigQuery can prune blocks based on clustered columns. Partitioning alone doesn't help with non-date filters. Materialized views may help pre-aggregated queries but not ad-hoc customer_id filters. Denormalization is not an optimization. Increasing slots is expensive and doesn't address data structure.

Answer

Increase the number of BigQuery slots

Answer

Create materialized views for each customer

Answer

Denormalize the table to reduce joins

Question 9

You are building a Dataflow pipeline in Python that reads messages from Pub/Sub, enriches them with data from a BigQuery table, and writes the results to BigQuery. The enrichment lookup table is large and changes infrequently. Which approach minimizes cost and latency?

Accepted Answer

Use a side input that reads the BigQuery table periodically and caches it.. Using a side input that periodically reads the BigQuery table and caches it avoids querying BigQuery for every incoming message, which would be prohibitively expensive and high-latency. The side input is refreshed at a configurable interval (e.g., every 10 minutes) via a pipeline option, and the cached data is broadcast to all workers, enabling fast, in-memory lookups without per-element I/O. This approach minimizes cost by reducing BigQuery API calls and minimizes latency by avoiding synchronous queries for each message.

Answer

Use a CoGroupByKey transform to join the incoming stream with a stream from BigQuery.

Answer

Use BigQuery IO to query the table for every incoming message.

Answer

Use a stateful DoFn and store the lookup in state per key.

Question 10

You need to store and query a large dataset of customer profiles. The data is semi-structured and frequently updated. The application requires offline support for mobile users. Which database is MOST appropriate?

Accepted Answer

Firestore. Firestore is the most appropriate choice because it is a NoSQL document database designed for semi-structured data, real-time synchronization, and offline support. It provides built-in offline persistence for mobile clients, allowing users to read and write data even without network connectivity, and automatically syncs changes when the connection is restored. This directly meets the requirements of semi-structured data, frequent updates, and offline mobile support.

Answer

BigQuery

Answer

Cloud Bigtable

Answer

Cloud SQL

Question 11

A data engineer wants to create a data lake on Google Cloud for storing raw streaming data, then transform it into curated and processed zones for analytics. The data is in Avro format and will be queried by BigQuery. Which two services are MOST suitable as the primary storage and query interface?

Accepted Answer

Cloud Storage and BigQuery. Cloud Storage is the most suitable primary storage for a data lake because it provides scalable, durable, and cost-effective object storage for raw Avro data. BigQuery is the ideal query interface because it can directly query Avro files stored in Cloud Storage using external tables, and it supports serverless analytics without needing to manage infrastructure.

Answer

Cloud Storage and Dataproc

Answer

Cloud Storage and Cloud SQL

Answer

Cloud Storage and Firestore

Question 12

Which BigQuery feature allows you to write data with exactly-once semantics, high throughput, and the ability to buffer data before making it available for queries?

Accepted Answer

Storage Write API with buffered mode. The Storage Write API with buffered mode (option D) is correct because it provides exactly-once semantics for data ingestion, high throughput via gRPC streaming, and the ability to buffer data in memory before making it available for queries. This mode allows you to commit rows in a stream, ensuring no duplicates, while the buffering stage gives you control over when data becomes visible in BigQuery.

Answer

BigQuery load jobs

Answer

BigQuery Data Transfer Service

Answer

Legacy streaming inserts

Question 13

A company uses Pub/Sub with push subscriptions to deliver events to a Cloud Run service. Recently, the service has been returning HTTP 429 (Too Many Requests), causing messages to be retried and eventually sent to the dead letter topic. What is the MOST likely cause?

Accepted Answer

The Cloud Run service needs more instances to handle the incoming request rate. Push subscriptions can be rate limited by the receiving service. Increasing ackDeadline gives more time but doesn't reduce rate. Using pull subscriptions shifts the rate control to the subscriber. Adjusting max delivery attempts only affects how many retries before dead letter, not rate limiting.

Answer

The subscription ackDeadline is set too low, causing messages to be redelivered

Answer

The push endpoint is not acknowledging messages quickly enough, causing a backlog

Answer

The dead letter topic is misconfigured, causing messages to be sent to it prematurely

Question 14

A healthcare organization stores patient data in BigQuery. They need to encrypt a specific column (e.g., SSN) using a key they manage, and decrypt it only for authorized queries via a user-defined function. Which approach should they use?

Accepted Answer

Use BigQuery AEAD encryption functions with a Cloud KMS key. BigQuery AEAD encryption functions (e.g., `AEAD.ENCRYPT` and `AEAD.DECRYPT`) allow you to encrypt a specific column using a customer-managed key stored in Cloud KMS, and then decrypt it only within a user-defined function (UDF) that enforces access controls. This meets the requirement of per-column encryption with key management and authorized decryption via a UDF.

Answer

Use BigQuery column-level access controls

Answer

Use Cloud Key Management Service (Cloud KMS) with CMEK for the BigQuery dataset

Answer

Use Cloud Data Loss Prevention (DLP) to de-identify the column

Question 15

You are migrating a large on-premises data warehouse to BigQuery. The data includes sensitive PII columns that must be masked for certain users. Which BigQuery feature can automatically redact PII in query results based on user roles?

Accepted Answer

Column-level security with data masking. BigQuery column-level security with data masking (also called dynamic data masking) lets you attach policy tags to specific columns and define masking rules that automatically redact values in query results based on the querying user's IAM roles. Users without the fine-grained reader role see masked values (e.g., hashed or null), while authorized users see the real data — all without changing the query. This is the native BigQuery feature designed for exactly this PII-redaction use case.

Answer

IAM conditions on tables

Answer

Authorized views

Answer

Cloud DLP API

Question 16

Which Google Cloud service provides a fully managed, serverless Spark environment without requiring cluster provisioning?

Accepted Answer

Dataproc Serverless. Dataproc Serverless is a fully managed, serverless Spark environment on Google Cloud that eliminates the need to provision or manage clusters. It automatically scales resources and charges only for the duration of the workload, making it ideal for running Spark jobs without infrastructure overhead.

Answer

Dataproc on GKE

Answer

Dataflow

Answer

Cloud Data Fusion

Question 17

A company uses Cloud Monitoring to track application latency. They notice a spike in latency every 30 minutes. What is the best initial step to diagnose the issue?

Accepted Answer

Check if scheduled jobs or cron tasks overlap.. Recurring spikes at regular intervals often indicate a scheduled process (e.g., cron job, batch job) that runs every 30 minutes. Checking for overlapping scheduled jobs is the most efficient first step before scaling or other actions.

Answer

Increase the number of instances to handle the load.

Answer

Enable Cloud Trace for all requests.

Answer

Change the alert threshold to ignore the spikes.

Question 18

Which TWO are common causes of prediction bias in a deployed machine learning model in production?

Accepted Answer

Data drift between training and serving data distributions.. Option B is correct because data drift occurs when the statistical distribution of the input features or target changes between the training environment and the live serving environment, causing the model's learned mappings to become stale and its predictions to be systematically biased. Option E is correct because training-serving skew arises when feature engineering logic differs between the training pipeline and the production inference path (for example, different imputation, normalization, or aggregation code), so the model receives inputs at serving time that do not match what it learned from, producing biased predictions. Option A is not a cause of bias — high accuracy is generally desirable and does not by itself indicate or produce prediction bias. Option C is not correct in this context because overfitting primarily harms generalization and variance rather than being a canonical cause of prediction bias in production, and it is a training-time issue rather than the drift/skew mechanisms described. Option D is not correct because low latency is a performance characteristic of the serving system and has no direct causal relationship with prediction bias.

Answer

Model accuracy is too high.

Answer

Model is overfitted to training data.

Answer

Low latency predictions.

Question 19

Your company uses Cloud Composer to orchestrate a data pipeline that includes Dataproc Spark jobs and BigQuery load operations. You need to pass the output file path from the Spark job to the next BigQuery task in the DAG. Which two mechanisms can you use to share data between tasks? (Choose TWO.)

Accepted Answer

Write the output path to a Cloud Storage object and read it in the next task.. Option E is correct because Airflow XComs are the native, built-in mechanism for passing small pieces of data such as a file path between tasks in a DAG: the Spark task calls xcom_push (or returns a value) and the BigQuery task uses xcom_pull with the task_id/key to retrieve it. Option C is correct because writing the output path to a Cloud Storage object and reading it in the next task is a durable, decoupled way to share data between tasks, especially when the value is larger or must persist beyond the DAG run; Cloud Composer tasks can use the GCS operators/sensors or the google-cloud-storage client to write and read the object. Option A is not appropriate because Cloud Composer (Airflow) Variables are global key-value configuration stores, not per-run task-to-task data channels, so using them for a run-specific output path risks race conditions and stale values. Option B is not appropriate because Pub/Sub is an asynchronous messaging service, not a synchronous task-to-task handoff mechanism, and it adds unnecessary complexity and latency for passing a single path within one DAG run. Option D is not appropriate because using BigQuery as an intermediary to store a transient output path is heavyweight and semantically wrong; BigQuery is for analytical data, not for orchestrating task metadata.

Answer

Store the output path as a Cloud Composer variable.

Answer

Publish the output path to a Pub/Sub topic and subscribe in the next task.

Answer

Use BigQuery as an intermediary to store the output path.

Question 20

You need to orchestrate a simple, linear workflow that calls several Cloud Functions and API endpoints sequentially with conditional logic. The workflow should be defined as code and have minimal overhead. Which GCP service should you use?

Accepted Answer

Workflows. Workflows is a serverless orchestration service that uses YAML/JSON to define workflows. It is ideal for simpler, linear or conditional orchestrations without the need for full Airflow infrastructure.

Answer

Cloud Tasks

Answer

Dataflow

Answer

Cloud Composer