Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

The requirements are:

Explore ORC formatted files with Hive partitioning.

Mimic the SQL on Hive query engine experience.

Cost-effective storage and processing.

Avoid impacting the on-premises Hadoop solution.

Let's analyze the options:

Option A (Import to Bigtable):Bigtable is a NoSQL database, not suited for SQL-based exploration of ORC files or Hive-style partitioning directly. This would require significant data transformation and a different query paradigm. Not cost-effective for this use case.

Option B (Import to BigQuery native tables):Importing data into BigQuery native storage is an option. BigQuery can load ORC files. This provides excellent query performance. However, it involves an ETL step (importing) and storage costs for the datawithin BigQuery, which might be higher than keeping it in its original format on Cloud Storage if query patterns are exploratory and not extremely frequent on all data.

Option C (Copy to Cloud Storage, deploy Dataproc):Dataproc allows you to run Hadoop/Spark (and thus Hive) clusters on Google Cloud. This would provide a very similar experience ("SQL on the Hive query engine"). However, running a persistent Dataproc cluster incurs compute costs for the cluster nodes, even when not actively querying. While ephemeral clusters are possible, it adds operational overhead for exploratory queries. Storage on Cloud Storage is cost-effective.

Option D (Copy to Cloud Storage, create external BigQuery tables):This is often the most cost-effective and straightforward solution for this scenario.

Cost-effective Storage:Cloud Storage is a low-cost option for storing files like ORC.

SQL Interface:BigQuery provides a familiar SQL interface.

External Tables:BigQuery can query data directly from Cloud Storage (including ORC files) using external tables. This avoids the need to load data into BigQuery's managed storage, saving on storage costs and ETL effort.

Hive Partitioning:BigQuery external tables support Hive partitioning layouts. When you define the external table, you can specify the partitioning scheme, and BigQuery will use partition pruning to scan only relevant partitions, improving performance and reducing costs for queries that filter on partition keys. This directly mimics the Hive experience.

Processing Cost:You only pay for the data scanned by BigQuery queries, which aligns with exploratory analysis.

Comparing D with B: External tables are generally more cost-effective for storage and initial setup if the data is already in ORC and an ETL process into BigQuery native storage is to be avoided. Query performance might be slightly less than native tables but is often sufficient for exploration, especially with partitioning. Comparing D with C: BigQuery external tables are serverless, meaning no cluster to manage or pay for when idle. Dataproc requires managing and paying for a cluster. For exploration, the serverless nature of BigQuery is usually more cost-effective.

Therefore, copying ORC files to Cloud Storage and using BigQuery external tables is the most cost-effective solution that meets all requirements.

The key requirements for the windowing strategy are:

A window groups data for a specific sensor.

A window should contain data spanningat least30 minutes ("duration of more than 30 minutes" implies activity for this period).

A window for a sensorendswhen no data has been received from that sensor for 15 minutes (this is a gap).

This scenario perfectly describessession windows.

Session Windows:Session windows group elements (per key, e.g., per sensor ID) that arrive within a certain "gap duration" of each other. A new session starts if data for a key arrives after the gap duration has passed since the last data point for that key.

In this case, if data stops arriving for a sensor for 15 minutes, the current session for that sensor closes. This matches "the window ends when no data has been received for 15 minutes."

The "duration of more than 30 minutes" requirement is a condition you would applyafterthe session window closes. You'd calculate the duration of the data within the closed session window and only compute the average if that session's duration (span of event times within it) exceeds 30 minutes. Session windows themselves don't have a fixed duration; their duration is determined by data activity and the gap.

Let's analyze why other options are less suitable:

A (Hopping windows with a 15-minute window, and a thirty-minute period):Hopping windows have a fixed size and a fixed period. They create overlapping windows. This doesn't align with the dynamic nature of sessions ending based on inactivity. A 30-minute period with a 15-minute window means windows like [0:00-0:15], [0:15-0:30], [0:30-0:45]. If activity is continuous, a 30-minute activity span would be covered, but the window closing is not based on a 15-minute gap of inactivity.

B (Tumbling windows with a 15-minute window and a fifteen-minute .withAllowedLateness operator):Tumbling windows are fixed-size, non-overlapping windows. .withAllowedLateness deals with late data arriving for a window that has already passed its end time, not with defining the window based on activity gaps.

C (Session windows with a 30-minute gap duration):This would mean a session ends only if there's a 30-minute gap of inactivity. The requirement is a 15-minute gap.

Therefore, session windows with a 15-minute gap duration (Option D) correctly model the requirement for windows to close after 15 minutes of inactivity from a sensor. The subsequent filtering for sessions lasting more than 30 minutes is a downstream operation.