GCP-ADP Practice Test 3 — 25 Questions

Question 1

You have a dashboard in Looker Studio and you want to allow users to dynamically change the date range without editing the report. What should you add?

Accepted Answer

Date range control.. A Date range control in Looker Studio is a native chart component that renders an interactive date picker on the report canvas, letting viewers change the time window of all charts bound to that date dimension without editing the report. It is the standard, purpose-built UI element for dynamic date filtering.

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Parameter.

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Calculated field.

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Chart interaction filter.

Question 2

Which THREE of the following are Dataflow windowing types?

Accepted Answer

Fixed. In Google Cloud Dataflow (Apache Beam), the supported windowing types are fixed windows, sliding windows, and session windows, so options A, B, and D are correct. Fixed windows (A) divide a PCollection into non-overlapping, contiguous time intervals of a set duration, such as every 60 seconds. Session windows (B) group elements that arrive close together in time, separated by a gap duration of inactivity, which is useful for user-activity streams. Sliding windows (D) are defined by a window duration and a slide period, allowing windows to overlap, such as a 60-second window that slides every 30 seconds. Binary (C) and Random (E) are not Beam/Dataflow windowing types; no such windowing functions exist in the Dataflow model.

Answer

Binary

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Random

Question 3

You are writing a complex BigQuery query and need to define a temporary table to use later in the same query. What is the most efficient syntax?

Accepted Answer

WITH clause (CTE).. The WITH clause, also known as a Common Table Expression (CTE), defines a temporary named result set that can be referenced later in the same query. It is the most efficient and readable syntax for complex queries because it avoids repeating subqueries and allows modular composition without persisting data.

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Nested subqueries.

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Variable declaration.

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CREATE TABLE AS SELECT.

Question 4

Which THREE steps are involved in an effective Dataflow pipeline development?

Accepted Answer

Deployment. The three correct steps are Deployment (A), Testing (B), and Development (D). Development (D) is where you author the pipeline code using the Apache Beam SDK, defining PTransforms, sources, and sinks before anything can be run. Testing (B) is essential to validate pipeline logic and correctness, typically via unit tests with TestPipeline and DoFnTester, and integration tests against representative data before production. Deployment (A) is the step of running the pipeline on a runner such as Dataflow, whether via the gcloud command, the Dataflow template, or a CI/CD job, to execute it at scale. Automated email alerts (C) are an operational monitoring concern, not a core pipeline development step, and manual data entry (E) is not part of building a Dataflow pipeline at all.

Answer

Automated email alerts

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Manual data entry

Question 5

You are a data analyst at a marketing firm. You need to query a large dataset stored in BigQuery, but you want to minimize query costs. The dataset contains a column named event_time of type TIMESTAMP. Most of your queries filter on event_time for a specific day. What should you do to reduce the amount of data scanned by your queries?

Accepted Answer

Create a partitioned table on the event_time column.. Partitioning the table on event_time enables BigQuery to prune partitions that do not match the filter. When querying for a specific day, only that day's partition is scanned, reducing the amount of data processed and lowering cost. Other methods like LIMIT, wildcard tables, or clustering do not achieve the same cost reduction for time-filtered queries.

Answer

Add a clustered column on event_time to the table.

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Use the LIMIT clause to restrict the number of rows returned.

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Use a wildcard table query to select only the relevant shards.

Question 6

Which TWO of the following strategies help reduce Cloud Composer costs?

Accepted Answer

Delete unused environments. Option C (Delete unused environments) is correct because Cloud Composer charges for the underlying GKE nodes, Cloud SQL database, and web server that run continuously per environment, so removing environments that are no longer needed eliminates those recurring infrastructure costs. Option E (Enable auto-scaling) is correct because Composer's autoscaling dynamically adjusts the number of worker nodes based on the workload's queue demand, allowing the environment to scale down during idle periods and avoid paying for excess capacity. Option A (Keep all historical logs forever) is wrong because retaining logs indefinitely increases Cloud Logging storage costs rather than reducing them. Option B (Use the largest environment size) is wrong because larger environments provision more workers and thus cost more, not less. Option D (Increase scheduler frequency) is wrong because more frequent scheduler runs increase resource consumption and can raise costs instead of lowering them.

Answer

Keep all historical logs forever

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Use the largest environment size

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Increase scheduler frequency

Question 7

You have a Dataplex Lake containing data from two different regions. How does this affect data access?

Accepted Answer

Users must have access to both regional storage resources. A Dataplex Lake can reference assets (Cloud Storage buckets or BigQuery datasets) that reside in different regions, but Dataplex does not move or replicate the data. Access to each asset is governed by the underlying regional storage resource, so users must have permissions and network access to both regions to query data across them. Thus, users must have access to both regional storage resources.

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Users cannot access data across regions

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Data access is automatically merged

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The Lake automatically replicates data

Question 8

You are using Vertex AI Workbench and want to ensure your notebook environment persists after the instance is stopped. Where should you save your work?

Accepted Answer

In a persistent disk attached to the Workbench instance.. Vertex AI Workbench instances use a persistent disk as the boot disk and for user data, so files saved outside the container image persist across stop/start cycles. Saving work in the persistent disk ensures notebooks, datasets, and environment customizations survive instance shutdown. The root directory of the container image and /tmp are ephemeral and are lost when the instance stops.

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In the root directory of the container image.

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In the local /tmp folder.

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In the system memory.

Question 9

You are building a streaming pipeline using Dataflow to process sensor data arriving via Pub/Sub. You need to handle out-of-order data by allowing events to arrive late. Which Dataflow concept must you configure?

Accepted Answer

Watermarks. Watermarks are Dataflow's mechanism for tracking event-time progress in a streaming pipeline. They represent the point in event time up to which the system believes all data has arrived, so configuring an allowed lateness window relative to the watermark lets late-arriving sensor events still be processed correctly. Without watermarks, Dataflow cannot reason about when a window is 'complete' or how long to wait for stragglers.

Answer

Side Inputs

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Partitioning

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GroupByKey

Question 10

You are loading a daily CSV file from Cloud Storage into a BigQuery table. The file contains a header row, uses comma delimiters, and has some rows with extra trailing columns. You want the load to tolerate these malformed rows by skipping them and recording how many were skipped, without failing the whole job. Which load configuration should you use?

Accepted Answer

Set `skipLeadingRows` to 1 and set `maxBadRecords` to a positive value.. `maxBadRecords` is the setting that lets a load job complete while skipping rows that cannot be parsed, and BigQuery reports how many were skipped. Pairing it with `skipLeadingRows` handles the header so the header is not counted as a bad row, giving a load that tolerates the malformed data and still succeeds.

Answer

Set `skipLeadingRows` to 1 and set `allowJaggedRows` to false.

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Set `skipLeadingRows` to 1 and set `ignoreUnknownValues` to true.

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Set `skipLeadingRows` to 1 and enable `allowQuotedNewlines`.

Question 11

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?

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.

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

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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 12

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 13

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 14

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

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Wait for Dataflow to finish

Question 15

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 16

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 17

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 18

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.

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Use the 'DynamicTaskGroup' operator in the Airflow library.

Question 19

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 20

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