Spark Principal component analysis(PCA) expected results - apache-spark-mllib

Question

I working on a project where I have to do some K-means clustering with MLlib from Spark. The problem is that my data have 744 features. I did some research and I found out that PCA is what I need. The best part is that Spark PCA implemented, so I decided to do that.
double[][] array=new double[381][744];
int contor=0;
for (Vector vectorData : parsedTrainingData.collect()) {
contor++;
array[contor]=vectorData.toArray();
}
LinkedList<Vector> rowsList = new LinkedList<>();
for (int i = 0; i < array.length; i++) {
Vector currentRow = Vectors.dense(array[i]);
rowsList.add(currentRow);
}
JavaRDD<Vector> rows = jsc.parallelize(rowsList);
// Create a RowMatrix from JavaRDD<Vector>.
RowMatrix mat = new RowMatrix(rows.rdd());
// Compute the top 3 principal components.
Tuple2<Matrix, Vector> pc = mat.computePrincipalComponentsAndExplainedVariance(*param*);
RowMatrix projected = mat.multiply(pc._1);
// $example off$
Vector[] collectPartitions = (Vector[]) projected.rows().collect();
System.out.println("Projected vector of principal component:");
for (Vector vector : collectPartitions) {
System.out.println("\t" + vector);
}
System.out.println("\n Explanend Variance:");
System.out.println(pc._2);
double sum = 0;
for (Double val : pc._2.toArray()) {
sum += val;
}
System.out.println("\n the sum is: " + (double) sum);
About the data that I want to apply PCA I have 744 features who represents values(total seconds of active time) collected by sensors in a home on every hour, so it is something like (31 sensors * 24 h), in format(s(sensorNumber)(hour): s10, s11.....s123, s20, s21....223,.....s3123.
From what I understand one of the criteria for a reduction to not lose to much of the information is the sum of Explained Variance to be greater 0.9 (90%). After some tests I got this results:
*pram* sum
100 0.91
150 0.97
200 0.98
250 0.99
350 1
So from what I understand it will be safe to reduce my 744 features vector to a 100 features vector. My problem is that this results looks to good to be true. I search for some examples to have guidance, but I am still unsure if what I did is correct. So is this results plausible?