PDE Practice Test 5 — 25 Questions

Question 1

A large e-commerce company is migrating its on-premise Hadoop cluster to Google Cloud using Dataproc for batch processing. The cluster processes daily sales data from multiple sources, generates aggregated reports, and performs ad-hoc analysis. The migration is complete, but users report that jobs are running 30% slower than on-premise. The data is stored in Cloud Storage as Parquet files partitioned by date. The Dataproc cluster uses preemptible VMs for worker nodes, and the master node uses a standard VM. The jobs heavily rely on shuffling data between stages. The cluster's autoscaling is enabled with a minimum of 10 and a maximum of 50 workers. During job execution, CPU utilization on workers is low, but disk I/O is high, especially on local SSDs. The network utilization is moderate. The team suspects that the shuffle operation is causing the slowdown. Which action should the team take to improve job performance?

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

Accepted Answer

Enable Cloud Storage as a shuffle destination by setting the property `dataproc:dataproc.shuffle.direct` to `true` and ensure the cluster has appropriate IAM permissions.. B is correct because the high disk I/O on local SSDs during shuffling indicates that the shuffle data is being written to local disk, which is a bottleneck. By enabling Cloud Storage as a shuffle destination via `dataproc:dataproc.shuffle.direct`, shuffle data is written directly to Cloud Storage, bypassing local disks and leveraging Google Cloud's high-throughput object storage. This reduces disk I/O contention and improves shuffle performance, especially when preemptible VMs are used, as shuffle data is not lost on VM preemption.

Answer

Attach additional local SSDs to each worker to increase local disk capacity and I/O throughput.

Answer

Change all worker VMs from preemptible to standard VMs to avoid preemption and improve reliability.

Answer

Increase the maximum number of preemptible workers to 100 to provide more parallelism.

Question 2

A company uses Dataflow to process streaming data from Pub/Sub. They notice increased processing latency. What is the most likely cause?

Accepted Answer

Insufficient workers. In Dataflow, processing latency increases most commonly due to insufficient workers, as the streaming pipeline cannot keep up with the incoming data rate when the number of Compute Engine instances is too low. This causes backpressure from Pub/Sub, leading to growing unacknowledged messages and higher end-to-end latency. Autoscaling may be delayed or limited by max worker count settings, making manual or configuration-based worker scaling the primary corrective action.

Answer

Pub/Sub subscription issue

Answer

Too many shards

Answer

Wrong machine type

Question 3

A company uses Looker to define business logic in LookML. They need to create a new measure that calculates the average order value, defined as total revenue divided by number of orders. Which LookML syntax should they use?

Accepted Answer

measure: avg_order_value { type: average; sql: ${revenue} / ${order_count} ;; }. Measures in LookML are defined with type and sql expression. The correct syntax for a calculated measure is: measure: avg_order_value { type: average; sql: ${revenue} / ${order_count} ;; }

Answer

measure: avg_order_value { type: sum; sql: ${revenue} / ${order_count} ;; }

Answer

dimension: avg_order_value { type: number; sql: ${revenue} / ${order_count} ;; }

Answer

dimension: avg_order_value { type: average; sql: ${revenue} / ${order_count} ;; }

Question 4

A BigQuery query fails with the error shown in the exhibit. What is the most likely cause?

Accepted Answer

The query scans too many partitions or data without efficient pruning. The error indicates that the query attempted to scan too many partitions or a large amount of data without effective partition pruning. BigQuery charges based on the amount of data processed, and queries that scan all partitions of a large table can hit limits or incur high costs. The most likely cause is that the query's WHERE clause does not filter on the partitioning column, forcing a full table scan across all partitions.

Answer

The table has too many partitions

Answer

The user does not have permission to query the table

Answer

Insufficient slot capacity in the project

Question 5

A data engineering team is building a CI/CD pipeline for machine learning models using Cloud Build and AI Platform. Which TWO practices are essential for ensuring reproducible and safe model deployments?

Accepted Answer

Tag each model version with the Git commit hash of the training code.. Tagging each model version with the Git commit hash of the training code (Option B) ensures full traceability from code to deployed model. This practice allows the team to exactly reproduce the training environment and code state, which is critical for debugging, auditing, and rolling back to a known-good version. Without this link, the model becomes a black box, and any attempt to recreate it relies on undocumented assumptions.

Answer

Use Cloud Functions to trigger retraining on new data arrival.

Answer

Use the same environment for training and serving, possibly via custom containers.

Answer

Directly deploy from the development environment using gcloud commands.

Question 6

Drag and drop the steps to create a Cloud Bigtable instance and table using the CLI into the correct order.

Accepted Answer

Create Cloud Bigtable instance → Create table inside the instance → Add column families to the table. Bigtable instances contain clusters; tables are created within instances and must have column families.

Answer

Create table → Create Cloud Bigtable instance → Add column families to the table

Answer

Create Cloud Bigtable instance → Add column families → Create table inside the instance

Answer

Add column families → Create Cloud Bigtable instance → Create table inside the instance

Question 7

A company stores raw data files in Cloud Storage in a bucket named 'raw-data'. After processing, the files are moved to a 'processed' bucket. To reduce costs, they want to automatically delete raw data older than 30 days. What should they do?

Accepted Answer

Configure a lifecycle rule on the 'raw-data' bucket to delete objects older than 30 days.. Cloud Storage lifecycle management allows you to set a rule that automatically deletes objects after a specified number of days from their creation time. By configuring a lifecycle rule on the 'raw-data' bucket to delete objects older than 30 days, the company can achieve cost reduction without manual intervention. This directly addresses the requirement to remove raw data files that have been processed and are no longer needed.

Answer

Enable object versioning on the 'raw-data' bucket and configure a lifecycle rule to delete noncurrent versions.

Answer

Set a retention policy on the 'raw-data' bucket to expire objects after 30 days.

Answer

Use a bucket policy that denies read access to objects older than 30 days.

Question 8

A company needs to store and analyze large amounts of unstructured data (images, videos) and structured data (CSV logs) in a cost-effective manner. The data should be accessible for analytics with BigQuery. Which two services should they use? (Choose TWO.)

Accepted Answer

BigQuery. Cloud Storage is the best option for storing unstructured and structured files cost-effectively. BigQuery can analyze this data directly via external tables or after loading, making it a powerful analytics platform.

Answer

Cloud SQL

Answer

Cloud Spanner

Answer

Firestore

Question 9

Your team is processing a large dataset with Apache Beam on Dataflow. The pipeline sometimes fails due to transient errors when writing to a BigQuery sink. You need to ensure that failed records are not lost and can be reprocessed later without blocking the pipeline. What is the best approach?

Accepted Answer

Use a side output (e.g., via TupleTag) to write failed records to a dead letter sink (e.g., GCS or Pub/Sub) and continue processing the main output.. Using a dead letter pattern with a side output to write failed records to a GCS bucket (or Pub/Sub) allows the pipeline to continue processing healthy records while failed records are stored for later analysis and reprocessing.

Answer

Configure the pipeline to use at-least-once semantics and rely on Dataflow to retry the entire bundle.

Answer

Increase the number of workers to reduce the chance of transient errors.

Answer

Use a try-catch block in the DoFn and log the error; continue processing other elements.

Question 10

A marketing team needs to run ad-hoc SQL queries on terabytes of clickstream data stored in Parquet files in Cloud Storage. They want a serverless solution with no cluster management and the ability to query external data without loading. Which service should they use?

Accepted Answer

BigQuery with external tables. BigQuery with external tables allows querying data stored in Cloud Storage (including Parquet files) without loading it into BigQuery storage, providing a serverless, fully managed solution with no cluster management. This matches the requirement for ad-hoc SQL queries on terabytes of clickstream data in Parquet format, as BigQuery automatically scales compute and storage.

Answer

Cloud SQL

Answer

AlloyDB

Answer

Dataproc with Spark SQL

Question 11

An organization wants to enforce that data in a Cloud Storage bucket cannot be deleted or overwritten for 7 years due to regulatory compliance. Which Cloud Storage feature should they use?

Accepted Answer

Retention Policy with Bucket Lock. Retention Policy with a retention period ensures objects cannot be deleted or overwritten during that period. Bucket Lock makes the policy permanent. Object holds are per-object. Lifecycle management automates transitions/deletions, opposite of retention.

Answer

IAM conditions

Answer

Object Lifecycle Management

Answer

Object holds

Question 12

A data engineer needs to design a stream processing pipeline that reads events from Pub/Sub, enriches them with data from a Cloud Storage file, and writes aggregated results to BigQuery. The pipeline must handle late-arriving events up to 1 hour. Which Dataflow feature should be used to manage late data?

Accepted Answer

Watermarks. Watermarks track event time progress and allow specifying allowed lateness. Triggers control when results are emitted, but watermarks handle late data.

Answer

Triggers

Answer

Side inputs

Answer

Windowing

Question 13

A data analyst wants to compute the rank of sales per region and also the difference in sales between consecutive months for each region. Which BigQuery analytic functions should they use? (Select TWO)

Accepted Answer

RANK(). RANK() computes the rank of rows within a partition. LAG() accesses data from a previous row in the same result set, which can be used to compute differences. ROW_NUMBER() assigns unique sequential integers, not rank. NTILE() distributes rows into buckets. LEAD() accesses next row, not previous.

Answer

ROW_NUMBER()

Answer

LEAD()

Answer

NTILE()

Question 14

A company uses Looker Studio to create dashboards from BigQuery data. They notice that dashboard queries take several seconds to load. They want to improve performance without changing the underlying data or creating materialized views. Which option should they use?

Accepted Answer

Enable BigQuery BI Engine for the project. BigQuery BI Engine is an in-memory analysis service that accelerates queries from Looker Studio (and other BI tools) by caching data in memory, significantly reducing latency. Replicating data to Cloud SQL would add complexity and may not handle the volume. Using Looker instead of Looker Studio doesn't inherently speed up queries. Increasing BigQuery slots would help but is more expensive and not as targeted for BI tools.

Answer

Increase the number of BigQuery slots

Answer

Switch to Looker instead of Looker Studio

Answer

Replicate the data to Cloud SQL for faster queries

Question 15

A company uses Cloud Storage as a data lake with raw, curated, and processed zones. Data in the raw zone should be automatically moved to a cheaper storage class after 30 days, and deleted after 1 year. What is the most efficient way to implement this?

Accepted Answer

Use Object Lifecycle Management with rules to transition to Coldline after 30 days and delete after 365 days.. Object Lifecycle Management in Cloud Storage allows you to set rules based on object age. You can transition objects to a lower-cost storage class (e.g., Nearline or Coldline) after 30 days and delete after 365 days.

Answer

Write a Cloud Function that runs daily, checks object ages, and moves/deletes them.

Answer

Use Cloud Scheduler to run a script that changes storage class and deletes objects.

Answer

Set a retention policy on the raw zone to prevent deletion and manually clean up.

Question 16

You are designing a Cloud Spanner schema for a global e-commerce application. The database will include a Customers table and an Orders table. To optimise performance for queries that join Customers with their Orders, which THREE design choices are recommended? (Choose 3.)

Accepted Answer

Use interleaved tables: make Orders an interleaved table under Customers.. Spanner interleaved tables store child rows physically close to parent rows, improving join performance. Including the parent's primary key as the first part of the child's primary key is required for interleaving. Secondary indexes on non-key columns are also needed for efficient lookups.

Answer

Use a single table for both Customers and Orders and filter by CustomerId.

Answer

Denormalise the schema by embedding order details into the Customers table as repeated fields.

Question 17

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 18

A company needs to predict whether a product image contains a specific defect. They have 10,000 labeled images and want to build a model quickly without writing custom code or training from scratch. Which GCP service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is designed for custom image classification tasks with minimal ML expertise. It uses transfer learning and supports up to millions of images. AutoML Tables handles tabular data, not images. Vertex AI custom training would require more effort. AutoML NLP is for text data.

Answer

AutoML Tables

Answer

Vertex AI custom training

Answer

AutoML Natural Language

Question 19

A company uses BigQuery ML to create a classification model. The model is used for batch prediction on a weekly basis. After six months, the data distribution shifts, and model accuracy drops. Which approach should the company take to maintain model performance?

Accepted Answer

Schedule automatic retraining of the model using the most recent three months of data.. The model's accuracy drop is due to data distribution shift (concept drift). Scheduling automatic retraining using the most recent three months of data ensures the model adapts to the new patterns without manual intervention. BigQuery ML supports scheduled queries and automatic model retraining via the `CREATE OR REPLACE MODEL` statement, making this approach both practical and aligned with MLOps best practices for batch prediction pipelines.

Answer

Use Cloud Dataflow to preprocess the data and then update the model with new features.

Answer

Perform hyperparameter tuning on the original training data.

Answer

Apply model quantization to reduce model size and improve inference speed.

Question 20

A data engineer needs to query data from BigQuery and another cloud provider's storage (AWS S3) using a single SQL query. The data must not be moved or copied to GCP. Which Google Cloud service should they use?

Accepted Answer

BigQuery Omni. BigQuery Omni allows querying data across multiple clouds (AWS S3, Azure Blob Storage) using BigQuery's interface without moving data. BigQuery Omni runs compute in the other cloud's region. BigQuery Transfer Service moves data into BigQuery. Dataplex is for data management, not cross-cloud queries. Cloud Storage Transfer Service is for moving data between clouds.

Answer

Cloud Storage Transfer Service

Answer

Dataplex

Answer

BigQuery Data Transfer Service