GCP-ADP Practice Test 7 — 10 Questions

Question 1

You are designing a Dataflow pipeline that reads from Cloud Storage and writes to BigQuery. The input files are in Avro format and contain a field with a timestamp that is often null. You need to ensure that records with null timestamps are not dropped but are assigned a default timestamp of the pipeline start time. Which approach should you use?

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

Accepted Answer

Use a ParDo transform to check for null timestamps and replace them with the pipeline start time using a side input.. To replace null timestamps with the pipeline start time, a ParDo transform with a side input is appropriate. The side input can be a singleton PCollection containing the start time, which is computed once and available to all workers. This ensures each record with a null timestamp is assigned the default without dropping data.

Answer

Use a Combine transform to calculate the minimum timestamp and replace nulls with that value.

Answer

Use a GroupByKey transform to group records by timestamp and filter out nulls.

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Use a CoGroupByKey transform to join records with a default timestamp PCollection.

Question 2

Which TWO are valid methods to trigger a Dataflow job?

Accepted Answer

Cloud Console. Option A (Cloud Console) is correct because Google Cloud Dataflow jobs can be created and launched directly from the Cloud Console UI, where you select a template or pipeline and configure parameters before clicking Run. Option C (Cloud Scheduler) is correct because Cloud Scheduler can send an HTTP request to the Dataflow REST API (or trigger a Cloud Function that calls it) to start a Dataflow job on a cron schedule, making it a valid programmatic trigger method. Option B (Direct keyboard input) is not a valid trigger mechanism for Dataflow jobs, as Dataflow has no such input channel. Option D (GCS bucket browsing) is not a trigger method; browsing objects in Cloud Storage does not launch a Dataflow job. Option E (Cloud DNS lookup) is unrelated to Dataflow, as Cloud DNS resolves domain names and cannot start data processing jobs.

Answer

Direct keyboard input

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GCS bucket browsing

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Cloud DNS lookup

Question 3

You are using Dataflow to read from Pub/Sub. The pipeline is failing due to malformed messages. How can you handle these without crashing?

Accepted Answer

Use a Dead Letter Queue (DLQ). A Dead Letter Queue pattern in Dataflow captures messages that fail transformation or parsing so the pipeline can continue processing valid records. In Apache Beam you implement this by branching the pipeline: successful records go to the main sink, and failed records (caught via try/except or a tagged output) are written to a separate Pub/Sub topic or BigQuery table for later inspection. This prevents a single malformed message from killing the streaming job.

Answer

Ignore errors

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Increase memory

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Restart the pipeline

Question 4

You are cleaning data using Dataprep. You want to save the final dataset in BigQuery. What do you do?

Accepted Answer

Use the Export feature. In Dataprep (now Dataprep by Trifacta), after cleaning and transforming a dataset in the flow, you publish results using the Export (or 'Run job'/output) feature, where you specify BigQuery as the output destination and configure the target dataset and table. This writes the transformed data into BigQuery. Copying files or running SQL manually is not how Dataprep delivers results.

Answer

Copy the file from GCS

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Use a SQL query

Answer

Wait for Dataflow to finish

Question 5

Which TWO of the following are true about Dataplex assets? (Choose two)

Accepted Answer

Assets inherit the location of the zone. Option B is correct because a Dataplex asset is always created inside a zone, and it automatically inherits that zone's location (region), so you cannot place an asset in a different location from its parent zone. Option C is correct because an asset is a child resource of exactly one zone in the Dataplex hierarchy (lake > zone > asset); it cannot be attached to multiple zones simultaneously. Option A is wrong because an asset must reference an underlying storage resource, such as a Cloud Storage bucket, BigQuery dataset, or BigLake table, so it cannot exist without one. Option D is wrong because assets can represent Cloud Storage buckets/objects, BigQuery datasets/tables, and BigLake tables, not only BigQuery tables. Option E is wrong because Dataplex assets are metadata representations that point to the existing physical data; they do not copy or duplicate the underlying data.

Answer

Assets can be created without a storage resource

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Assets can only be BigQuery tables

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Assets duplicate the physical data

Question 6

You are implementing a security strategy for your data lake. Which THREE of the following are supported ways to control access to data within a Dataplex Lake? (Choose three)

Accepted Answer

Grant Dataplex-specific roles to users. Option A is correct because Dataplex Lake access is governed through IAM by granting Dataplex-specific roles (such as roles/dataplex.viewer, roles/dataplex.editor, or roles/dataplex.admin) to users, groups, or service accounts, which controls who can view or manage the lake and its zones/assets. Option B is correct because Dataplex assets point to underlying storage such as Cloud Storage buckets or BigQuery datasets, and IAM roles applied directly to those underlying resources (for example roles/storage.objectViewer or roles/bigquery.dataViewer) are honored and provide an additional supported layer of data access control. Option C is correct because Dataplex integrates with BigQuery and Data Catalog Policy Tags (via the Dataplex/Data Catalog taxonomy and policy tag framework) to enforce column-level security, restricting access to specific columns of sensitive data. Option D is not correct because IP-based firewalls are a network-level control (e.g., VPC firewall rules) and are not a supported mechanism for controlling access to data within a Dataplex Lake. Option E is not correct because IAM roles are not assigned to individual files; permissions are granted at the resource hierarchy level (project, bucket, dataset, table, or Dataplex entity), not per file.

Answer

Use IP-based firewalls for all datasets

Answer

Assign roles to individual files

Question 7

A colleague must load a 200 GB uncompressed CSV file from a Cloud Storage bucket into a BigQuery table once, with the lowest possible load cost and no transformation. They want the simplest option that BigQuery handles natively. What should they use?

Accepted Answer

bq load with the CSV file specified as a Cloud Storage URI. Loading a file straight from Cloud Storage with the bq load command is BigQuery's native bulk ingest path and does not require a separate compute service, so it is both the simplest and the least expensive option for an untransformed one-time CSV load. Dataflow, Cloud Data Fusion, and Dataproc all add their own compute charges and pipeline complexity that the scenario does not need.

Answer

A Cloud Data Fusion pipeline with a Cloud Storage source and BigQuery sink

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A Dataflow pipeline using the TextIO source and BigQueryIO sink

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A Dataproc job running Spark to read the CSV and write to BigQuery

Question 8

You want to dynamically generate tasks in an Airflow DAG based on the number of files in a GCS bucket. What is the standard way to do this?

Accepted Answer

Use a Python loop inside the DAG file to instantiate tasks.. The standard way to dynamically generate tasks in Airflow based on external conditions (like the number of files in a GCS bucket) is to use a Python loop inside the DAG file. At DAG parse time, the loop runs and creates task instances dynamically based on the current state (e.g., listing files via a GCS hook). This leverages Airflow's core design: DAGs are Python scripts that are executed to build the task graph. The loop can call an operator (e.g., PythonOperator, GCSToBigQueryOperator) for each file, generating tasks with unique task_ids. This pattern is widely documented and recommended for dynamic task generation, as long as the number of tasks is not excessively large and the DAG is idempotent.

Answer

Use the 'SubDagOperator'.

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Configure an external metadata service to inject tasks.

Answer

Use the 'DynamicTaskGroup' operator in the Airflow library.

Question 9

Which GCS storage class is most cost-effective for data accessed only once per year?

Accepted Answer

Archive. Archive is the most cost-effective GCS storage class for data accessed only once per year because it is designed for long-term storage of data that is accessed less than once a year. Archive storage has the lowest storage cost among GCS classes, but it also has the highest retrieval cost and a minimum storage duration of 365 days. For data accessed only once per year, Archive provides the best cost savings.

Answer

Standard

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Coldline

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Nearline

Question 10

Which THREE of the following are necessary for a production-ready Dataflow pipeline?

Accepted Answer

Error handling. Error handling (B) is necessary because a production Dataflow pipeline must gracefully manage failures such as malformed input records, transient network issues, or failed side inputs, typically via try/except blocks, dead-letter queues, or retry policies, so that a single bad element does not halt the entire streaming or batch job. Logging (C) is required to capture runtime events, warnings, and errors through tools like Cloud Logging (Stackdriver), enabling operators to diagnose issues, audit data flow, and trace pipeline behavior across workers. Monitoring (E) is essential to observe pipeline health and performance metrics such as system lag, throughput, watermark progression, and worker CPU/memory in Cloud Monitoring, allowing alerting and timely intervention before SLA breaches. The unmarked options do not belong: desktop-based processing (A) is irrelevant because Dataflow is a fully managed, cloud-based service that runs on Google Cloud infrastructure, not on a local desktop, and manual refreshes (D) contradict the automation and continuous operation expected of a production pipeline, which should run on schedules or streaming triggers without human intervention.

Answer

Desktop-based processing

Answer

Manual refreshes