PDE Practice Test 39 — 15 Questions

Question 1

A data engineer is designing a BigQuery table for time-series data that will be queried frequently by time range and also by a customer_id. Which TWO design decisions will improve query performance and manage costs? (Choose two.)

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

Accepted Answer

Partition the table by day on the timestamp column. Partitioning the table by day on the timestamp column allows BigQuery to prune partitions when queries filter by a time range, scanning only the relevant partitions instead of the entire table. This directly reduces the amount of data read, improving query performance and lowering costs.

Answer

Disable automatic reclustering to save costs

Answer

Set partition expiration to 1 year

Answer

Use nested repeated fields for customer data

Question 2

You are building a forecasting model to predict daily sales for the next 90 days using historical sales data with clear seasonality and trend. You want to use BigQuery ML with minimal manual tuning. Which model type should you choose?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is specifically designed for time-series forecasting and automatically handles seasonality, trend, and holiday effects without manual tuning. ARIMA is less automated; linear regression would require manual feature engineering for time components.

Answer

ARIMA

Answer

Boosted tree (XGBoost)

Answer

Linear regression

Question 3

A company runs a Dataflow pipeline that processes a high-volume data stream. They notice that the pipeline's worker CPU utilisation is near 100% and the system lag is increasing. Which three actions can improve performance? (Choose three.)

Accepted Answer

Increase the number of workers.. Increasing the number of workers distributes the processing load across more parallel workers, reducing CPU utilization per worker and allowing the pipeline to keep up with the incoming data stream. This directly addresses both high CPU usage and increasing system lag by scaling out horizontally.

Answer

Increase the worker disk size.

Answer

Use batch processing instead of streaming.

Question 4

What is the most likely cause of this error?

Accepted Answer

The schema of the BigQuery table expects a TIMESTAMP but the pipeline is sending a STRING. The error message indicates a type mismatch: BigQuery expects a TIMESTAMP column, but the pipeline is sending a STRING. Dataflow's BigQuery sink performs automatic schema validation, and if the source data type (STRING) does not match the target column type (TIMESTAMP), the write operation fails with a mismatch error. This is a common issue when pipeline code or source data formats timestamps as strings without explicit conversion.

Answer

The BigQuery table is not partitioned

Answer

The Dataflow worker does not have the correct time zone

Answer

The pipeline is using a fixed window but the data is out of order

Question 5

A company is building a data lake on Cloud Storage. They need to organise data into zones for raw, curated, and processed layers. Which TWO practices should they follow? (Choose 2.)

Accepted Answer

Use separate Cloud Storage buckets for each zone (raw, curated, processed).. A data lake typically uses separate buckets or prefixes for raw (immutable), curated (cleaned/transformed), and processed (aggregated/reporting) data. Using prefixes within a single bucket is common, but separate buckets provide better isolation. Lifecycle rules can be applied per prefix or bucket.

Answer

Use the same storage class for all zones to simplify management.

Answer

Set retention policies on the raw zone to make data immutable.

Answer

Enable object versioning on all buckets to prevent data loss.

Question 6

A company wants to use BigQuery for analytics. They need to meet compliance requirements by encrypting data at rest with a key they control. Which TWO actions should they take? (Choose 2.)

Accepted Answer

Set the Cloud KMS key as the default encryption key for the BigQuery dataset.. BigQuery supports Customer-Managed Encryption Keys (CMEK) for encrypting data at rest. You need to create a Cloud KMS key and then set it as the default encryption key for a BigQuery dataset. All tables in that dataset will be encrypted with that key.

Answer

Create a Cloud Storage bucket and load data there.

Answer

Use VPC Service Controls to restrict access to the dataset.

Answer

Enable BigQuery column-level encryption using AEAD functions.

Question 7

A data engineer needs to enforce that all datasets in a project expire after 90 days to reduce storage costs. They want to automate this without manual intervention. Which approach should they use?

Accepted Answer

Set a default table expiration on each BigQuery dataset to 90 days. BigQuery datasets support a default table expiration setting that automatically deletes tables after a specified number of days. This enforces a 90-day lifecycle for all tables in the dataset without requiring manual intervention or external automation, directly addressing the cost reduction goal.

Answer

Use Cloud Storage lifecycle rules to delete tables after 90 days

Answer

Create an IAM policy that revokes access after 90 days

Answer

Use BigQuery scheduled queries to delete tables older than 90 days

Question 8

Your organization deploys multiple versions of the same model to Vertex AI Endpoint for A/B testing. You have a production model (v1) serving 90% of traffic and a candidate model (v2) serving 10%. After one week, you observe that v2 has a slightly lower AUC but significantly higher business metrics like click-through rate. The product team wants to gradually increase v2's traffic. However, you need to ensure that the overall prediction latency remains under 200 ms. Currently, the endpoint has 10 replicas for v1 and 2 replicas for v2. What is the best approach to roll out v2 while maintaining latency SLO?

Accepted Answer

Increase v2's traffic split by 10% each day while also adding replicas for v2 based on CPU utilization.

Answer

Merge v2's model into v1 by retraining v1 with v2's architecture and deploy as a single model.

Answer

Immediately set v2 to serve 100% traffic and monitor latency; if it exceeds 200 ms, roll back.

Answer

Use a separate endpoint for v2 and route traffic at the load balancer level.

Question 9

A company uses Dataplex to manage data quality across multiple BigQuery datasets. They want to define a data quality rule that checks if a column 'email' contains a valid email format. Which Dataplex feature should they use?

Accepted Answer

Create a custom Data Quality rule using the 'regex' type.. Dataplex Data Quality allows predefined rule types including a 'regex' rule for pattern matching. There is no built-in 'email' rule, so a regex check is appropriate.

Answer

Use Cloud DLP to classify and validate emails.

Answer

Use the built-in 'email' rule type in Dataplex.

Answer

Create a Dataflow pipeline to validate emails and write results to a separate table.

Question 10

A company is migrating their on-premises Hadoop cluster to Google Cloud. The existing cluster runs HDFS, Hive, and Spark jobs. The migration must minimize changes to existing job code and configuration. The data volume is 50 TB and growing. The team expects to run both batch and interactive SQL queries. Which architecture should they use?

Accepted Answer

Use Cloud Dataproc with ephemeral clusters and Cloud Storage (instead of HDFS) for data storage. Run Spark jobs directly, and use Cloud Dataproc's built-in Hive on Cloud Dataproc for SQL queries.. It uses Cloud Storage as the underlying storage layer, which is HDFS-compatible and allows existing Spark jobs to run without code changes. Ephemeral Dataproc clusters reduce costs and provide native Hive support for interactive SQL queries, meeting both batch and interactive requirements without altering job configurations.

Answer

Keep HDFS on persistent Cloud Dataproc clusters and use BigQuery for SQL queries.

Answer

Use Cloud Dataflow for all batch processing and BigQuery for storage and querying.

Answer

Migrate HDFS to Cloud Storage, create a Cloud Dataproc cluster for Spark jobs, and use BigQuery for interactive SQL queries via a Hive metastore linked to BigQuery.

Question 11

You train a BigQuery ML linear regression model to predict house prices. The model has high bias during evaluation. Which action BEST reduces bias?

Accepted Answer

Add more features like number of bedrooms and square footage. High bias indicates underfitting, meaning the model is too simple. Adding more relevant features (option B) increases model complexity and reduces bias. Decreasing the learning rate (A) does not address model complexity; it only affects convergence speed. Removing features with low correlation (C) further reduces complexity, increasing bias. Increasing L2 regularization (D) penalizes large coefficients, which increases bias.

Answer

Decrease the learning rate in the training options

Answer

Remove features that have low correlation with the label

Answer

Increase L2 regularization

Question 12

Which Google Cloud service would you use to create a unified data catalog that automatically captures lineage from BigQuery, Cloud Storage, and other sources?

Accepted Answer

Dataplex. Dataplex provides a unified data catalog (Universal Catalog) with automated lineage, discovery, and governance across GCP. Data Catalog is the older standalone service; Dataplex is the recommended unified solution. Cloud Composer and Dataflow are orchestration/processing tools.

Answer

Cloud Composer

Answer

Dataflow

Answer

Data Catalog

Question 13

You want to quickly estimate the number of distinct visitors to your website from a large BigQuery table. Which function provides an approximate count with low latency?

Accepted Answer

APPROX_COUNT_DISTINCT. APPROX_COUNT_DISTINCT provides an approximate distinct count with low latency. COUNT(DISTINCT) is exact but slower for large data. APPROX_QUANTILES estimates quantiles. HyperLogLog is not a BQ function.

Answer

HyperLogLog++

Answer

COUNT(DISTINCT)

Answer

APPROX_QUANTILES

Question 14

You need to stream real-time user click events from your application into BigQuery for immediate analysis. The events must be available for query within seconds. Which approach is recommended?

Accepted Answer

Use Pub/Sub to Dataflow to BigQuery with the Storage Write API for high-throughput streaming.. Pub/Sub to Dataflow to BigQuery using the Storage Write API provides the highest throughput and reliability with near-real-time latency. Legacy streaming inserts are limited and have higher latency. Direct Pub/Sub to BigQuery subscription is not a native feature. Cloud Functions is not suitable for high-throughput streaming.

Answer

Use Cloud Data Fusion to ingest streaming data from Pub/Sub into BigQuery.

Answer

Use Cloud Functions to receive events from Pub/Sub and insert them into BigQuery using the legacy streaming API.

Answer

Use Pub/Sub with a BigQuery subscription to directly write events into BigQuery.

Question 15

You need to track data lineage from a BigQuery table through a series of transformations and into a Vertex AI model training pipeline. Which Google Cloud service provides automated data lineage tracking?

Accepted Answer

Dataplex. Dataplex includes data lineage tracking that captures metadata about data movement and transformations across BigQuery, Vertex AI, and other services. Data Catalog provides metadata but not automated lineage.

Answer

Dataflow

Answer

Data Catalog

Answer

Cloud Composer