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

To help the marketing team establish a consistent BigQuery analytics spend each month, you can use BigQuery reservations to allocate dedicated slots for their queries. This provides predictable costs by reserving a fixed amount of compute resources.

BigQuery Reservations:

BigQuery Reservations allow you to purchase dedicated query processing capacity in the form of slots.

By reserving slots, you can control costs and ensure that the marketing team has the necessary resources for their queries without unexpected increases in spending.

Baseline Slots:

Setting a baseline of 500 slots without autoscaling ensures a consistent allocation of resources.

This provides a predictable monthly cost, as the marketing team will be billed for the reserved slots regardless of actual usage.

Billing Back:

The marketing team's usage can be billed back based on the fixed reservation cost, ensuring budget predictability.

This approach avoids the variability associated with on-demand billing, where costs can fluctuate based on query volume and complexity.

No Autoscaling:

By not enabling autoscaling, you prevent additional costs from being incurred due to temporary increases in query demand.

This fixed reservation ensures that the marketing team only uses the allocated 500 slots, maintaining a consistent monthly spend.

Google Data Engineer References:

BigQuery Reservations Documentation

BigQuery Slot Reservations

Managing BigQuery Costs

Using a fixed reservation of 500 slots provides the marketing team with predictable costs and the necessary resources for their queries without unexpected billing variability.

- A denormalized, append-only model simplifies query complexity by eliminating the need for joins. - Adding data with an ingestion timestamp allows for easy retrieval of both current and historical states. - Instead of updating records, new records are appended, which maintains historical information without the need to create separate snapshots.

To optimize the performance of a complex Spark job on Dataproc that heavily relies on shuffling operations, and given the cost constraints of using preemptible VMs, switching from HDDs to SSDs and using HDFS as an intermediate storage layer can significantly improve performance. Here’s why option C is the best choice:

Performance of SSDs:

SSDs provide much faster read and write speeds compared to HDDs, which is crucial for performance-intensive operations like shuffling in Spark jobs.

Using SSDs can reduce I/O bottlenecks during the shuffle phase of your Spark job, improving overall job performance.

Intermediate Storage with HDFS:

Copying data from Google Cloud Storage (GCS) to HDFS for intermediate storage can reduce latency compared to reading directly from GCS.

HDFS provides better locality and faster data access within the Dataproc cluster, which can significantly improve the efficiency of shuffling and other I/O operations.

Cost Considerations:

Although SSDs are more expensive than HDDs, the performance improvement for shuffle-heavy workloads can justify the cost, especially if the improved performance reduces the overall runtime and thereby the cost of using preemptible VMs.

Using preemptible VMs with SSDs for this workload balances the cost and performance trade-offs effectively.

The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:

Processing only—Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.

No preemptible-only clusters—To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.

Persistent disk size—As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.

The managed group automatically re-adds workers lost due to reclamation as capacity permits.

For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:

Read from, but not write to, any table within the project.

Read from and write to any table within the project, but not manage instances.

Read from and write to any table within the project, and manage instances.

The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.

The columns can be grouped into two types—categorical and continuous columns:

A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.

A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.

Year of birth and income are continuous columns. Country is a categorical column.

You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.