PDE Maintaining and Automating Data Workloads Practice Test 6 — 15 Questions

Question 1

You need to estimate the cost of a BigQuery query before running it. Which command or feature should you use?

Accepted Answer

Use the bq command with the --dry_run flag.. The `bq` command with the `--dry_run` flag allows you to estimate the amount of data a BigQuery query will process before actually executing it. This dry run does not read any data or incur charges; it simply returns the estimated bytes to be processed, which you can use to calculate the cost based on BigQuery's pricing model.

Answer

Check the BigQuery jobs list for similar queries.

Answer

Use the BigQuery cache to estimate if the query is cached.

Answer

Run EXPLAIN on the query to see the query plan.

Question 2

You are designing a data quality pipeline that must inspect PII in BigQuery tables and de-identify sensitive columns before sharing with analysts. Which GCP service should you use?

Accepted Answer

Cloud DLP. Cloud DLP (Data Loss Prevention) is the correct choice because it is purpose-built for inspecting, classifying, and de-identifying sensitive data such as PII. It integrates natively with BigQuery via inspection jobs and de-identification templates, allowing you to scan tables for over 150 built-in infoTypes (e.g., email, SSN) and apply transformations like masking, tokenization, or encryption before sharing data with analysts.

Answer

Dataplex

Answer

Cloud Data Catalog

Answer

Dataflow

Question 3

You need to schedule a Dataproc Spark job to run at 2 AM every day, and upon completion, trigger a BigQuery load job. Which Cloud Composer operator should you use to run the Spark job?

Accepted Answer

DataprocSubmitJobOperator. The DataprocSubmitJobOperator is specifically designed to submit a job (e.g., a Spark job) to an existing Dataproc cluster. In this scenario, you need to run a Spark job on a scheduled basis, and Cloud Composer (Airflow) provides this operator to submit the job to Dataproc. After the Spark job completes, you can chain a BigQuery load operator to trigger the load, matching the requirement exactly.

Answer

DataflowPythonOperator

Answer

BigQueryOperator

Answer

DataprocClusterCreateOperator

Question 4

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 5

You are using Cloud Workflows to orchestrate a series of API calls. You need to handle errors and retries. Which THREE features of Cloud Workflows can you use? (Choose THREE.)

Accepted Answer

Use try/except blocks to catch and handle errors.. Option A is correct because Cloud Workflows supports try/except blocks, allowing you to catch errors raised by a step and execute fallback logic or handle the failure gracefully. Option C is correct because Cloud Workflows supports conditional branching with if/else constructs, letting you evaluate step results and choose different execution paths based on those outcomes. Option D is correct because Cloud Workflows lets you attach a retry policy to a step, specifying parameters such as max_retries, initial_delay, max_delay, and multiplier to automatically retry failed calls. Option B is not correct because Cloud Load Balancing is a traffic-distribution service for workloads, not a Cloud Workflows error-handling or retry feature. Option E is not correct because logging is handled by Cloud Logging integration and is not a configurable per-step error-handling or retry feature of Cloud Workflows.

Answer

Integrate with Cloud Load Balancing for high availability.

Answer

Enable automatic logging for each step.

Question 6

You are designing a Cloud Composer workflow that loads data from Cloud Storage into BigQuery, runs a Dataflow job to transform the data, and then triggers a Dataproc Spark job. After each step, you need to conditionally branch based on success or failure. Which Airflow feature allows you to pass messages between tasks to enable dynamic branching?

Accepted Answer

XComs. XComs (cross-communications) are Airflow's built-in mechanism for passing small pieces of data between tasks. A task can push a value via xcom_push() or by returning it, and downstream tasks pull it via xcom_pull(), enabling dynamic decisions such as choosing a branch based on a prior task's output. This is exactly what conditional branching in a Composer DAG requires.

Answer

Sensors

Answer

TaskFlow API

Answer

DAG dependencies

Question 7

You have a BigQuery table that is used by multiple teams. To save costs, you want to provide a consistent view of the data as of a specific point in time without creating full copies. Which BigQuery feature should you use?

Accepted Answer

Table snapshot. A BigQuery table snapshot creates a lightweight, read-only copy of a table at a specific point in time without duplicating the underlying data, and it is the correct feature for providing a consistent point-in-time view cheaply. Snapshots preserve the table state at creation and can be queried or restored, making them ideal for this requirement.

Answer

Authorized views

Answer

Materialized views

Answer

Table clone

Question 8

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

Question 9

Your organization has a BigQuery flat-rate reservation with 500 slots. During peak hours, queries are queued and you need additional capacity temporarily. You want to add slots for a burst of activity without committing to a long-term purchase. What should you do?

Accepted Answer

Use flex slots. Flex slots are a BigQuery feature that allows you to purchase slots for a short, fixed duration (e.g., 1 hour, 1 day, 1 week) without a long-term commitment. They are designed exactly for temporary bursts of query activity, such as peak-hour spikes, and can be added to an existing flat-rate reservation. By using flex slots, you temporarily increase the slot capacity of your reservation, reducing queuing, and then the slots automatically expire after the chosen period, avoiding ongoing costs.

Answer

Switch to on-demand pricing

Answer

Create a secondary reservation with autoscaling

Answer

Purchase additional committed use reservations

Question 10

A data engineer wants to quickly estimate the cost of running a BigQuery query before executing it. Which command-line tool or command should they use?

Accepted Answer

bq query --dry_run. The bq query --dry_run flag validates a query and returns the amount of data it would process without actually executing it, which is the standard way to estimate BigQuery on-demand query cost. Since BigQuery on-demand pricing is based on bytes scanned (currently $6.25 per TiB), the dry-run's reported bytes give a direct cost estimate. It also validates syntax and permissions without incurring charges.

Answer

gcloud logging read

Answer

bq query --use_cache=false

Answer

gcloud bigtable queries run

Question 11

You need to inspect a BigQuery table for sensitive data such as credit card numbers and apply masking. Which GCP service should you use to identify and de-identify the data?

Accepted Answer

Cloud Data Loss Prevention (DLP). Cloud Data Loss Prevention (DLP) is Google Cloud's service for discovering, classifying, and de-identifying sensitive data such as credit card numbers, SSNs, and emails. It provides infoType detectors (including CREDIT_CARD_NUMBER) and de-identification transforms like masking, tokenization, and format-preserving encryption. It integrates directly with BigQuery for scanning tables and applying masking at query or storage time.

Answer

IAM Recommender

Answer

Cloud KMS

Answer

Dataplex

Question 12

You are setting up Dataplex data quality rules for a BigQuery table. You want to define rules that check for non-null values in key columns and also validate that a column's values fall within a certain range. Which TWO rule types must you use? (Choose 2)

Accepted Answer

Row rule (e.g., not null). Dataplex data quality rules include row rules (for null checks) and column rules (for range or value checks). Table rules apply to the entire table; custom SQL can be used but row and column rules are the standard.

Answer

Table rule (e.g., row count)

Answer

Partition rule

Answer

Custom SQL rule

Question 13

You want to optimize BigQuery costs for a large dataset that is frequently queried by time range. You also need to ensure that predictable workloads have dedicated slot capacity. Which TWO strategies should you combine? (Choose 2)

Accepted Answer

Partition the table by date. Option B is correct because partitioning the table by date lets BigQuery prune partitions and scan only the date ranges a query touches, which directly reduces bytes processed and therefore cost for a dataset that is frequently queried by time range. Option C is correct because committed use reservations (capacity commitments) provide dedicated, predictable slot capacity at a discounted rate for steady baseline workloads, satisfying the requirement that predictable workloads have dedicated slots. Option A is not appropriate as a primary cost strategy here because query caching only helps when identical queries are repeated and results are still cached, which does not address time-range pruning or dedicated capacity. Option D is wrong because a materialized view over the entire table would be expensive to maintain and does not target the time-range access pattern. Option E is wrong because autoscaling slots add flexible, on-demand capacity rather than the dedicated, predictable capacity the scenario requires.

Answer

Use query caching

Answer

Create a materialized view for the entire table

Answer

Enable autoscaling slots

Question 14

You need to deploy a reusable Dataflow pipeline that can be executed with different parameters from Cloud Composer. Which TWO components should you use? (Choose 2)

Accepted Answer

Dataflow Flex Template. Option B (Dataflow Flex Template) is correct because Flex Templates package the pipeline as a Docker image plus a template spec file, allowing the same pipeline to be reused and launched with different runtime parameters (such as input/output locations) via the templates launch API. Option C (Cloud Composer with DataflowStartFlexTemplateOperator) is correct because this operator is purpose-built to submit a Flex Template job from an Airflow/Composer DAG, passing the required parameters and letting Composer orchestrate the pipeline execution. Option A (Direct runner) is incorrect because it runs the pipeline locally for testing rather than deploying a reusable job on Dataflow. Option D (Dataflow Classic Template) is not the best fit here since Classic Templates are less flexible for parameterization and dependency packaging compared with Flex Templates. Option E (Cloud Scheduler) is incorrect because it only triggers jobs on a schedule and does not deploy or parameterize a Dataflow pipeline.

Answer

Direct runner

Answer

Dataflow Classic Template

Answer

Cloud Scheduler

Question 15

You are building a data pipeline that ingests data from on-premises into Cloud Storage, then processes it with Dataproc, and finally loads into BigQuery. You need to schedule the pipeline to run daily. The pipeline must handle occasional failures gracefully. Which THREE Google Cloud services should you use together to achieve this? (Choose 3)

Accepted Answer

Cloud Storage. Cloud Storage (A) is correct because the scenario explicitly requires ingesting on-premises data into Cloud Storage as the landing/staging layer before processing. Dataproc (B) is correct because the pipeline is specified to process the data with Dataproc, Google Cloud's managed Hadoop/Spark service. Cloud Composer (C) is correct because it is the managed Apache Airflow service that provides daily scheduling, dependency orchestration, and retry/alerting logic so occasional failures are handled gracefully. Dataflow (D) is not needed since the processing engine in this pipeline is Dataproc, not a Beam-based Dataflow job. Pub/Sub (E) is not required because the scenario describes batch ingestion into Cloud Storage rather than real-time streaming messaging.

Answer

Dataflow

Answer

Pub/Sub