PDE Practice Test 17 — 15 Questions

Question 1

A company has a BigQuery dataset containing sensitive customer data. They want to share a subset of this data with external partners, ensuring that partners can only see specific columns and rows. Which BigQuery feature should they use?

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

Accepted Answer

Authorized views. Authorized views allow you to share a query (view) that filters columns and rows, while granting access to the view only, not the underlying tables.

Answer

Materialized views

Answer

Clustered tables

Answer

Dataset-level access controls

Question 2

A data team is migrating an on-premises Hadoop cluster to Dataproc. The cluster runs a mix of long-running services (Hive, HBase) and transient Spark jobs. They want to minimize cost while maintaining performance. Which TWO strategies should they implement?

Accepted Answer

Use preemptible instances for worker nodes in the transient Spark cluster. Preemptible workers are cost-effective for fault-tolerant Spark jobs. Separating long-running services into a separate cluster avoids interference and allows independent scaling. Using a single-node cluster for services is not practical. Dataproc on GKE adds complexity. Standard persistent disks are fine for HDFS.

Answer

Consolidate all workloads into a single high-availability cluster

Answer

Use local SSDs for all nodes to improve I/O performance

Answer

Use Dataproc on GKE to run long-running services

Question 3

A company has a BigQuery table that is partitioned by ingestion time and clustered by the 'customer_id' column. They notice that queries filtering on 'customer_id' are not benefiting from clustering as expected. What is the most likely cause?

Accepted Answer

The table is too small for clustering to be effective. Clustering works best when the clustering column is used in a filter that limits data scanned. However, if the filter is on a non-clustering column, or if the clustering column has high cardinality with many distinct values, clustering may not help much. In this case, the issue could be that the filter on 'customer_id' is not selective enough, or the table is too small.

Answer

The query is using a wildcard function that prevents clustering pruning

Answer

The clustering column must be the same as the partition column

Answer

Clustering does not work with ingestion-time partitioning

Question 4

A data engineer needs to design a streaming pipeline that ingests events from multiple sources, enriches them with a lookup table stored in BigQuery (updated every hour), and writes the results to a BigQuery table for real-time dashboards. The pipeline must handle late-arriving data up to 1 hour. Which Dataflow feature should be configured to manage late data?

Accepted Answer

Allowed lateness on the window. Allowed lateness in Apache Beam specifies the maximum delay of late data to be included in the window. Watermark estimation does not control late data acceptance; triggers specify when output is emitted, not how late data is handled. Side inputs are for enrichment, not late data management.

Answer

A custom watermark estimation function

Answer

Using side inputs with a periodic refresh

Answer

A trigger that fires on every late element

Question 5

A company uses Cloud Dataproc to run Spark ML training jobs. They want to persist the trained models and metadata in a Hive-compatible metastore. Which Dataproc feature should they use?

Accepted Answer

Dataproc Metastore. Dataproc Metastore provides a Hive-compatible metastore that can be used across clusters and services.

Answer

Cloud Hive Metastore (self-managed)

Answer

Cloud Bigtable

Answer

Cloud Data Catalog

Question 6

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.. Airflow XComs allow tasks to exchange small amounts of data (e.g., file paths) by pushing and pulling values. Cloud Storage can be used as an intermediate store: the Spark job writes output to GCS, and the BigQuery task reads from that location. BigQuery does not directly communicate with Dataproc. Cloud Composer variables are for global configuration, not task-to-task. Pub/Sub is not needed for simple file path sharing.

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 7

A company is evaluating BigQuery for a data warehouse migration. They have a mix of reporting queries and ad-hoc analytical queries. They want to control query costs and prevent runaway queries. Which THREE strategies should they implement?

Accepted Answer

Set a custom quota for concurrent queries. Custom quotas cap query usage. Reservation models (flex slots) provide dedicated resources and predictable pricing. Partitioning and clustering reduce data scanned, lowering cost. Authorized views control access but not cost. Materialized views can reduce cost but are not a cost control mechanism per se.

Answer

Grant authorized view access to limit data visibility

Answer

Create materialized views for all reporting queries

Question 8

A financial services company has a BigQuery dataset containing sensitive customer data. They need to share a subset of this data (excluding PII columns) with an external analytics partner. The partner should be able to query the data using their own BigQuery account, but the company must maintain full control over the underlying table and ensure the partner cannot see or access the original table. Which approach should they use?

Accepted Answer

Create an authorized view in the same dataset, excluding PII columns, and share only the view with the partner's BigQuery account.. Authorized views allow you to share a view with specific users/groups while restricting access to the underlying table. The view can be defined to exclude PII columns and can be shared with the partner's account. The partner queries the view directly, but cannot access the base table. This maintains data control and security.

Answer

Use dataset-level ACLs to deny the partner access to the original table and grant access to a view.

Answer

Export the filtered data to a new BigQuery dataset and grant the partner access to that dataset.

Answer

Create a materialized view and grant the partner the bigquery.dataViewer role on the dataset.

Question 9

A data pipeline using Cloud Dataflow reads from a Pub/Sub subscription that has a dead letter topic configured. Some messages are being sent to the dead letter topic. Upon investigation, the engineer finds that the messages contain valid data but are malformed according to the schema. What is the most likely reason for the messages being dead-lettered?

Accepted Answer

The Pub/Sub topic has a schema that the messages do not comply with. The subscription's message schema enforcement validates incoming messages; if the message doesn't conform to the schema, it is forwarded to the dead letter topic.

Answer

The Pub/Sub topic is not configured with a schema

Answer

The Dataflow pipeline is using at-least-once delivery guarantee

Answer

The subscription's ack deadline is too short

Question 10

Your streaming Dataflow pipeline reads from Pub/Sub and writes to BigQuery. You need to update the pipeline to add a new transformation step without losing any messages or causing duplicate processing. Which TWO actions should you take? (Choose 2)

Accepted Answer

Use the Dataflow update command with the same pipeline name and the new template.. To update a streaming pipeline without draining, you can use the `--update` flag with the new pipeline. The snapshot feature allows you to restore state if needed. Draining would stop the pipeline; canceling would lose messages.

Answer

Stop the pipeline, modify it, then restart with a new name.

Answer

Drain the pipeline before making changes.

Answer

Cancel the pipeline and create a new one.

Question 11

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 short-term, hourly commitments that can be added to an existing flat-rate reservation to handle bursts. They are ideal for temporary capacity needs.

Answer

Switch to on-demand pricing

Answer

Create a secondary reservation with autoscaling

Answer

Purchase additional committed use reservations

Question 12

A company uses Cloud Dataproc for large-scale Spark jobs. They notice that some jobs are failing due to insufficient memory on the worker nodes. They want to improve memory management without over-provisioning. Which three configurations should they apply? (Choose 3)

Accepted Answer

Set spark.executor.memory to a value that fits within the node memory. Setting spark.executor.memory to a value that fits within the node memory ensures that each executor does not exceed the available RAM on a worker node, preventing out-of-memory (OOM) errors. This configuration directly controls the heap size allocated to each executor, and when combined with spark.executor.cores and spark.executor.instances, it allows precise memory budgeting per node. Over-provisioning is avoided by calculating the maximum safe executor memory as (node memory - OS overhead - HDFS cache) / number of executors per node.

Answer

Use local SSDs for temporary storage

Answer

Use preemptible worker nodes for volatile tasks

Question 13

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` command parses the query and reports the number of bytes processed without executing it, allowing cost estimation based on on-demand pricing.

Answer

gcloud logging read

Answer

bq query --use_cache=false

Answer

gcloud bigtable queries run

Question 14

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 DLP can automatically inspect BigQuery tables for sensitive data types (credit card numbers, etc.) and apply de-identification transforms like masking, tokenization, etc.

Answer

IAM Recommender

Answer

Cloud KMS

Answer

Dataplex

Question 15

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. Partitioned tables reduce bytes scanned by time-range queries. Committed use reservations provide predictable slot capacity at a discount. Autoscaling does not provide dedicated capacity; caching is automatic.

Answer

Use query caching

Answer

Create a materialized view for the entire table

Answer

Enable autoscaling slots