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

https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts

References:

The best option to meet the ACID compliance and minimal human intervention requirements is to configure a Cloud SQL for PostgreSQL instance with high availability enabled. Key reasons: Cloud SQL for PostgreSQL provides full ACID compliance, unlike Bigtable which provides only atomicity and consistency guarantees. Enabling high availability removes the need for manual failover as Cloud SQL will automatically failover to a standby replica if the leader instance goes down. Point-in-time recovery in MySQL requires manual intervention to restore data if needed. BigQuery does not provide transactional guarantees required for an ACID database. Therefore, a Cloud SQL for PostgreSQL instance with high availability meets the ACID and minimal intervention requirements best. The automatic failover will ensure availability and uptime without administrative effort.

 If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing. References:

Customer-managed Cloud KMS keys

[Authorized views]

[Analytics Hub overview]

[Creating an Analytics Hub listing]

A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:

Materialized views, ML models in data warehouse - Google Cloud

Data Engineering with Google Cloud Platform - Packt Subscription

To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here’s why option C is the best choice:

Materialized Views:

Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.

They can be automatically refreshed to reflect changes in the underlying data.

Incremental Updates:

The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.

This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.

Performance and Maintenance:

By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.

This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.

Steps to Implement:

Create Materialized Views:

Define materialized views for the visualization queries with the necessary configurations

CREATE MATERIALIZED VIEW project.dataset.view_name

AS

SELECT ...

FROM ...

WHERE ...

Enable Incremental Updates:

Ensure that the materialized views are set up to handle incremental updates automatically.

Reference in Visualization Tool:

Update the data visualization tool to reference the materialized views instead of running the original queries directly.

Reference Links:

BigQuery Materialized Views

Optimizing Query Performance

A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete. This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. References:

Signed URLs | Cloud Storage Documentation

V4 signing process with Cloud Storage tools

V4 signing process with your own program

Using a URL list file

What Is a 403 Forbidden Error (and How Can I Fix It)?

Here's a detailed breakdown of why this solution is optimal and why others fall short:

Why Option C is the Best Solution:

Kafka Connect Bridge: This bridge acts as a reliable and scalable conduit between your on-premises Kafka cluster and Google Cloud's Pub/Sub messaging service. It handles the complexities of securely transferring data over the interconnect link.

Pub/Sub as a Buffer: Pub/Sub serves as a highly scalable buffer, decoupling the Kafka producer from the Dataflow consumer. This is crucial for handling fluctuations in message volume and ensuring smooth data flow even during spikes.

Custom Dataflow Pipeline: Writing a custom Dataflow pipeline gives you the flexibility to implement any necessary transformations or enrichments to the data before it's written to BigQuery. This is often required in real-world streaming scenarios.

Minimal Latency: By using Pub/Sub as a buffer and Dataflow for efficient processing, you minimize the latency between the data being produced in Kafka and being available for querying in BigQuery.

Why Other Options Are Not Ideal:

Option A: Using a proxy host introduces an additional point of failure and can create a bottleneck, especially with high-throughput streaming.

Option B: While Google-provided Dataflow templates can be helpful, they might lack the customization needed for specific transformations or handling complex data structures.

Option D: Dataflow doesn't natively connect to on-premises Kafka clusters. Directly reading from Kafka would require complex networking configurations and could lead to performance issues.

Additional Considerations:

Schema Management: Ensure that the schema of the data being produced in Kafka is compatible with the schema expected in BigQuery. Consider using tools like Schema Registry for schema evolution management.

Monitoring: Set up robust monitoring and alerting to detect any issues in the pipeline, such as message backlogs or processing errors.

By following Option C, you leverage the strengths of Kafka Connect, Pub/Sub, and Dataflow to create a high-throughput, low-latency streaming pipeline that seamlessly integrates your on-premises Kafka data with BigQuery.

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

using a timestamp as the first element of a row key can cause a variety of problems.

In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.

Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design]

Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).

[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.

[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.

Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.

If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.

Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.

Dataflow is a unified processing model, and can execute both streaming and batch data pipelines

You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.

You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.

When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions]

To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.

If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.

What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.

Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.

Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.

A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite). https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design

Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.

Google BigQuery is an enterprise data warehouse.

 Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base tables change, so they always return fresh data. Materialized views can also be used by the BigQuery optimizer to process queries to the base tables, if any part of the query can be resolved by querying the materialized view. References:

Introduction to materialized views

Create materialized views

BigQuery Materialized View Simplified: Steps to Create and 3 Best Practices

Materialized view in Bigquery

Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow

service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.

You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.

Standard SQL has been the preferred query language since BigQuery 2.0 was released.

In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.

Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.

If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day.

If you use the LIMIT clause, BigQuery will still process the entire table.

The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.

It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance,

If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.

It is recommended to avoid creating your Compute Engine instance in a different region from

your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.

By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.

Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.