PDE Practice Test 7 — 10 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